A Certificate of Analysis (COA) is one of the most important documents in a manufacturing and quality-control environment.

It contains critical information about a product, material, batch or lot: test results, specifications, supplier information, signatures, remarks and other quality parameters. Yet COAs rarely arrive in a standardized format.

One supplier may send a structured digital PDF. Another may provide a scanned certificate with multiple tables. Some documents may contain handwritten signatures, notes, reference documents or several sets of test results.

This is where Deep Learning for document processing can make a significant difference.

Instead of simply reading text from a document, deep-learning-based Intelligent Document Processing (IDP) can help a system understand the structure, context and relationships within complex quality documents.

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Why Traditional OCR Isn't Enough for COA Processing

Optical Character Recognition (OCR) has been used for years to convert scanned documents into machine-readable text.

But a COA is more than a collection of words and numbers.

Consider a typical certificate containing:

• Supplier information
• Certificate number
• Material or product description
• Batch or heat number
• Multiple test tables
• Chemical composition
• Mechanical properties
• Specifications
• Notes and remarks
• Reference standards
• Digital or handwritten signatures

Basic OCR may successfully recognize individual characters.

The bigger challenge is determining:

What does each piece of information mean, and where does it belong?

For example, the value 0.18 means very little by itself.

A deep-learning system needs to understand whether it represents:

• Carbon percentage
• A dimensional measurement
• A test result
• A specification limit
• Or something else entirely.

That is the difference between text recognition and document understanding.

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What Does Deep Learning Bring to COA Automation?

Deep learning enables document-processing systems to identify patterns and relationships across large volumes of documents.

Rather than relying entirely on fixed templates, the system can learn how information is typically presented and progressively improve its ability to process variations.

For COA processing, this can be particularly useful for identifying several different types of information.

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1. Supplier Detection

COAs can arrive from hundreds of suppliers, each using its own format.

Supplier detection helps identify the source document and determine how its information should be interpreted.

This reduces the need to maintain a completely separate manual process for every supplier.

The result is a more scalable approach to multi-supplier COA automation.

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2. Multiple Table Detection

Tables are often the heart of a COA.

A single certificate may contain multiple tables covering:

• Chemical composition
• Mechanical properties
• Physical properties
• Product specifications
• Test results
• Acceptance criteria

A deep-learning-based system can identify different tables and understand their boundaries and structure.

This is particularly important because extracting numbers without preserving their row-column relationships can lead to incorrect quality records.

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3. Multiple Test Detection

A COA may contain several tests performed on the same material or batch.

The system needs to distinguish between different tests and associate the corresponding values with the right parameter.

For example:

Test → Parameter → Result → Unit → Specification → Status

Maintaining these relationships is essential for reliable downstream validation.

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4. Understanding Notes and Remarks

Important information isn't always contained inside neatly structured tables.

Manufacturers and suppliers frequently add:

• Notes
• Remarks
• Special instructions
• Exceptions
• Processing information
• Additional quality observations

Deep-learning-powered document understanding can help identify this contextual information rather than treating it as irrelevant text.

This becomes especially valuable when the information affects how a quality record should be interpreted.

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5. Digital and Handwritten Signatures

A COA may include a digital signature, a scanned signature or a handwritten approval.

Recognizing these elements can help determine whether the certificate contains the expected approval information.

For organizations concerned with quality compliance and audit readiness, knowing that a certificate has been reviewed or signed can be an important part of the overall document record.

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6. Reference Document Detection

COAs sometimes refer to other documents or standards.

These references can provide important context about:

• Testing procedures
• Specifications
• Quality requirements
• Material standards
• Customer requirements

Deep-learning-based document analysis can help identify references and connect them with the appropriate information within the certificate.

This moves COA processing closer to context-aware document intelligence.

Deep Learning vs. Template-Based Extraction

Traditional document automation often depends heavily on predefined templates.

This can work well when every document follows the same structure.

But real-world COAs are rarely that predictable.

Capability	Template-Based OCR	Deep Learning-Based IDP
Basic text extraction	✓	✓
Fixed document formats	✓	✓
Variable layouts	Limited	✓
Multiple tables	Limited	✓
Context understanding	Limited	✓
Supplier variations	Requires configuration	Better suited
Notes & remarks	Limited	✓
Signature detection	Limited	✓
Multiple test structures	Limited	✓
Continuous learning	Limited	✓

From Extraction to Understanding

This distinction is becoming increasingly important in Intelligent Document Processing.

A modern COA automation workflow can be thought of as:

Extract → Understand → Validate → Verify → Integrate → Trace

Extract

Capture text, numbers, tables and other document elements.

Understand

Determine what each element represents and how it relates to other information.

Validate

Compare extracted results against specifications, rules or reference data.

Verify

Route uncertain or exceptional information for human review.

Integrate

Send structured information into ERP, LIMS, QMS or other business systems.

Trace

Maintain the connection between the original certificate and the resulting quality record.

This is where the value of deep learning becomes much greater than simply improving OCR accuracy.

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Why This Matters for Manufacturing

For organizations processing thousands of certificates, manual COA processing can create several challenges.

Manual data entry

Quality teams may spend significant time transferring information from certificates into spreadsheets or enterprise systems.

Inconsistent formats

Every supplier can potentially introduce a different document structure.

Data-entry errors

A single incorrect value can potentially affect quality decisions, downstream processing or customer documentation.

Slow validation

Teams may need to manually compare test results against specifications.

Limited traceability

When information is manually copied into another system, maintaining a clear link to the original certificate can become difficult.

Deep-learning-powered automation addresses these challenges by turning complex documents into structured, usable quality data.

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Deep Learning Is Particularly Valuable for Complex COAs

The real test for an AI document-processing system isn't a clean, standardized one-page document.

It is the messy, real-world certificate.

A document containing:

Multiple tables + different suppliers + test results + notes + signatures + reference information

requires considerably more than conventional OCR.

This is the type of environment where deep learning can provide meaningful value.

Star Software's approach to COA processing reflects this broader shift toward document intelligence, with capabilities designed to handle elements such as supplier detection, multiple-table detection, multiple-test detection, notes and remarks, reference documents, and digital or handwritten signatures.

The objective is not merely to digitize the certificate.

It is to understand the certificate and convert it into reliable business data.

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What Should Companies Look for in Deep-Learning COA Automation?

When evaluating a COA automation solution, organizations should look beyond the phrase "AI-powered OCR."

Ask:

• Can it handle different supplier formats?
• Can it identify multiple tables on the same certificate?
• Can it distinguish different tests and their corresponding results?
• Can it understand notes and remarks?
• Can it detect signatures and approval information?
• Can it validate extracted values against specifications?
• Can users review uncertain results?
• Can the extracted information be integrated with ERP, LIMS or QMS?
• Can every quality record be traced back to the source certificate?

These questions reveal whether the solution is genuinely providing document intelligence or simply performing OCR.

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The Future of COA Automation Is Document Understanding

COA automation is moving beyond simple scanning and data extraction.

The next generation of systems will increasingly combine:

OCR + Computer Vision + Deep Learning + Business Rules + Workflow Automation

to understand complex quality documents.

For manufacturers, this means a COA can become more than a static PDF stored in a folder.

It can become a structured, validated and traceable quality record that feeds directly into the organization's digital processes.

And that is perhaps the most important shift:

The future of COA automation isn't about teaching computers to read documents. It's about teaching them to understand what those documents mean.

For many manufacturers, the RFQ process begins with a document that looks deceptively simple: an engineering drawing.

But behind that drawing sits a considerable amount of work.

Sales and estimating teams may need to interpret dimensions, identify components, understand materials, determine manufacturing processes, prepare a Bill of Materials (BOM), calculate costs, apply margins, and finally generate a quotation.

When this process is performed manually, even a relatively straightforward RFQ can become a time-consuming exercise.

What if much of that work could happen automatically?

Advances in AI-powered document processing are making it possible to move from engineering drawing → structured data → costing → quotation with significantly less manual intervention.

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The Hidden Complexity Behind an RFQ

An engineering drawing contains far more information than the visible geometry.

Depending on the drawing, it may contain:

• Part numbers
• Material specifications
• Dimensions
• Tolerances
• Quantities
• Notes and annotations
• Manufacturing instructions
• Surface finishes
• Assembly information
• Revision details
• Process requirements

A human estimator can interpret these elements and translate them into a costing model.

The challenge is doing this consistently and at scale when hundreds or thousands of drawings arrive as part of RFQs.

Traditionally, the workflow might look like this:

Receive drawing → Review drawing → Identify parts → Create BOM → Determine processes → Estimate costs → Apply margin → Prepare quotation

Every additional manual step introduces time, repetitive work and the possibility of errors.

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What Is AI-Powered BOM Extraction?

AI-powered BOM extraction uses a combination of technologies such as OCR, computer vision, machine learning and document intelligence to identify relevant information from engineering drawings and convert it into structured data.

Instead of treating a drawing as simply an image or PDF, the system attempts to understand the information contained within it.

For example, an AI system may identify:

Information in Drawing	Structured Output
Part number	PF-1001-A
Component description	Base Plate
Material	Specified material
Quantity	Required quantity
Dimensions	Length × Width × Thickness
Manufacturing information	Required processes
Drawing revision	Revision number
Notes	Manufacturing instructions

The resulting data can then become an input into downstream costing and quotation workflows.

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From BOM Extraction to Automated Costing

BOM extraction is only the first step.

The real opportunity lies in connecting extracted information with business rules and costing logic.

Consider a simplified workflow:

1. Upload the Engineering Drawing

The system receives the drawing as a PDF or image.

2. Extract Relevant Information

AI identifies parts, quantities, dimensions, materials and other relevant attributes.

3. Build the BOM

The extracted information is converted into a structured Bill of Materials.

4. Identify Manufacturing Processes

The system can associate components with applicable processes such as:

• Cutting
• Drilling
• Welding
• Machining
• Forming
• Finishing

5. Apply Costing Rules

Cost information can be applied based on materials, quantities, processes and other predefined parameters.

6. Calculate the Quote

The system can calculate:

Material Cost + Processing Cost + Other Costs + Margin = Quote Value

7. Generate the Quotation

The final information can be converted into a quotation-ready format.

This creates a much more connected RFQ workflow than simply extracting text from a drawing.

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Why This Matters for Manufacturers

1. Faster RFQ Response

Speed matters in competitive manufacturing.

If a company can respond to an RFQ in hours rather than days, it can potentially handle more opportunities and improve its responsiveness to customers.

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2. Less Manual Data Entry

Estimators and sales teams shouldn't have to repeatedly copy information from drawings into spreadsheets.

AI can handle much of the repetitive extraction work while humans focus on reviewing exceptions and making commercial decisions.

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3. Greater Consistency

Manual estimation can vary depending on who processes the RFQ.

A standardized digital workflow can apply the same extraction and costing rules consistently.

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4. Better Traceability

An automated workflow can maintain a connection between:

Original Drawing → Extracted Data → BOM → Costing → Quote

That traceability becomes particularly valuable when drawings are revised or quotations need to be reviewed later.

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5. Easier Scaling

Hiring additional estimators isn't always the best way to handle growing RFQ volumes.

Automation can provide a digital processing layer that allows existing teams to handle substantially more documents.

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AI Doesn't Mean "No Human Involvement"

This is an important distinction.

The goal of AI-powered RFQ automation shouldn't necessarily be to eliminate the estimator.

Instead, it should eliminate unnecessary manual work.

A practical workflow can include human validation at critical points:

AI extracts → AI structures → AI calculates → Human verifies exceptions → Quote generated

This human-in-the-loop approach can provide a balance between automation and control.

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The Importance of Handling Real-World Drawings

A major challenge is that engineering drawings are rarely perfectly standardized.

Organizations may receive drawings from:

• Different customers
• Different CAD systems
• Different suppliers
• Different engineering teams
• Different revisions
• Different document formats

Some drawings may contain clear digital text. Others may include scanned information, annotations, complex tables or densely packed technical details.

Therefore, an effective solution needs to go beyond basic OCR.

It needs to understand layout, context, relationships and engineering information.

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What Should Companies Look for in an AI RFQ Automation Platform?

When evaluating solutions, businesses should look beyond the headline claim of "AI-powered extraction."

Key questions include:

Can it understand complex engineering drawings?

Text extraction alone isn't enough. The system needs to identify the relationship between different elements of a drawing.

Can it create structured BOMs?

Extracted information should be usable downstream—not simply displayed as text.

Can it support manufacturing processes?

The ability to associate parts with processes can make the transition from extraction to costing much more valuable.

Can it integrate costing logic?

Organizations should be able to incorporate their own material, process and pricing rules.

Can humans review exceptions?

AI should provide confidence indicators and allow users to verify uncertain information.

Can the workflow maintain traceability?

Users should be able to understand how information moved from the source drawing to the final quotation.

Can it integrate with existing systems?

ERP, CRM, MES and other enterprise systems should ideally be part of the broader workflow.

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The Future of RFQ Processing

The traditional RFQ process is document-heavy.

The future is likely to be increasingly data-driven.

Instead of:

Drawing → Human Interpretation → Spreadsheet → Costing → Quote

the workflow can evolve toward:

Drawing → AI Understanding → Structured BOM → Automated Costing → Human Validation → Quote

This doesn't just save keystrokes.

It changes where people spend their time.

Estimators can spend less time finding and entering information and more time evaluating opportunities, optimizing costs and making commercial decisions.

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Where Intelligent Document Processing Fits In

This is where Intelligent Document Processing (IDP) becomes particularly interesting.

The value isn't simply in extracting words from a PDF.

The complete journey is:

Extract → Understand → Validate → Verify → Calculate → Integrate → Trace

That distinction is important.

The ultimate objective isn't to create another digital copy of an engineering drawing.

It's to transform the information inside that drawing into usable business intelligence.

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From Documents to Decisions

For manufacturers dealing with high RFQ volumes, engineering drawings can represent a significant source of structured business information—if that information can be captured effectively.

AI-powered document automation is making that possible.

The next generation of RFQ platforms won't simply help teams read drawings faster.

They will help them move from:

Engineering data → BOM → Cost → Quote

with fewer manual steps and greater visibility across the process.

Platforms such as Star Software are exploring this model by combining AI-powered document understanding with structured BOM extraction, process-based costing and quotation workflows.

The bigger opportunity, however, extends beyond any single platform:

When AI can understand engineering documents, the document itself can become the starting point for an automated business process—not the beginning of another manual task.

 

In manufacturing and industrial supply chains, three documents appear frequently across quality, procurement, manufacturing, and compliance workflows:

• Certificate of Conformance (CoC)
• Certificate of Analysis (CoA)
• Material Test Report (MTR)

They may all arrive as PDFs from suppliers, but they answer very different questions.

A simple way to think about them is:

CoC = Does the product conform?
CoA = What did the testing show?
MTR = What exactly is this material, and can I trace its properties back to the mill?

For organizations processing hundreds or thousands of these documents every month, understanding these differences is also the first step toward automating quality-document workflows.

CoC vs. CoA vs. MTR: At a Glance
Parameter	Certificate of Conformance (CoC)	Certificate of Analysis (CoA)	Material Test Report (MTR/MTC)
Primary purpose	Confirms that a product conforms to specified requirements	Reports actual laboratory or quality-test results	Provides chemical, mechanical and material traceability
Core question answered	"Does this product meet the agreed requirements?"	"What did the tests actually show?"	"What material is this, and what are its certified properties?"
Typical data	Part number, PO number, lot/batch number, standards, compliance statement	Test parameters, measured values, specifications, acceptance limits	Heat number, chemical composition, tensile/yield strength, elongation, hardness, grade, mill
Raw test data	Usually not included	Yes	Yes
Chemical composition	Generally no	Sometimes	Yes, especially for metals
Mechanical properties	Generally no	Sometimes	Yes
Traceability	Product/lot level	Batch level	Heat/mill/material level
Typical industries	Aerospace, automotive, electronics, industrial manufacturing	Pharmaceuticals, chemicals, food & beverage, cosmetics	Steel, metals, oil & gas, construction, aerospace, heavy engineering
Typical business function	Compliance & supplier assurance	Quality control & laboratory verification	Material quality & traceability
Example	Supplier confirms a component meets an aerospace specification	Pharmaceutical manufacturer reports purity of a batch	Steel mill certifies the chemistry and mechanical properties of a steel plate

 

CoC vs. CoA vs. MTR: Which One Do You Need?

The answer depends on what you need to establish.

If you need confirmation of compliance:

CoC

If you need laboratory test results:

CoA

If you need detailed metal/material traceability:

MTR/MTC

And in many industrial workflows, you may need more than one.

 

Where AI-Powered Document Automation Changes the Equation

This is where Star Software's perspective becomes particularly relevant.

At Star Software, we see CoCs, CoAs and MTRs not simply as documents to be digitized, but as sources of structured business and quality data.

AI-powered Intelligent Document Processing can help organizations move from:

Document → Human reading → Manual entry → Verification

to:

Document → AI extraction → Validation → Structured data → Enterprise system

The objective isn't merely to convert a PDF into text.

The real value comes from understanding what the information means.

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• A CoC tells you that a product conforms.
• A CoA tells you what the tests revealed.
• An MTR tells you what the material is, what properties it has, and where it came from.

Understanding the difference is important.

But for organizations processing these documents at scale, the bigger opportunity is to automate the entire journey—from document ingestion and data extraction to validation, traceability and integration.

That's where AI-powered Intelligent Document Processing can turn quality documentation from a manual administrative task into a strategic source of operational intelligence.

Material Test Reports (MTRs), also called Mill Test Reports or Mill Test Certificates (MTCs), are among the most important quality documents in the metals supply chain. They connect a specific heat, batch, coil, plate, pipe, bar or other metal product to its chemical composition, mechanical properties, applicable specifications and traceability records.

For decades, MTR management has largely involved PDFs, scanned documents, spreadsheets, shared drives and manual data entry. That model is increasingly being replaced by document intelligence, OCR, AI-based extraction, automated validation and digital traceability.

However, not every MTR automation product solves the same problem.

Some platforms are primarily MTR databases and document-management systems. Others focus on AI extraction. Some validate chemistry and mechanical properties against standards. Others connect MTRs to inventory, purchase orders and ERP systems. A few are evolving toward supplier-quality intelligence and predictive risk management.

This article compares the leading approaches and products available, with particular attention to their relevance for steel mills, metal service centers, distributors, fabricators, EPC companies and manufacturers.

There is no single "best" MTR automation platform for every metals company. The strongest choice depends on the problem an organization is trying to solve:

 

Platform	Primary strength	Best suited for	Key limitation / consideration
MetalTrace	MTR document management and traceability	Established metals companies requiring mature MTR databases and ERP connectivity	More document-management oriented than AI-first compliance automation
Star Software	AI/OCR extraction plus workflow and validation	Manufacturers, service centers and companies wanting configurable document automation	Broader platform; implementation may require more configuration
GoSmarter MillCert Reader	AI mill-certificate extraction and metals workflow	Metals manufacturers and distributors seeking a modern cloud workflow	More focused on operational metals workflows than complex EPC compliance
Kinetech Automator	MTR review, standards validation and supplier intelligence	Quality teams seeking exception-based review and supplier analytics	Newer product; independent MTR-specific review evidence is still limited
Pathnovo	EPC-grade MTR extraction, PO matching and compliance	EPCs, engineering companies and large project environments	Broader engineering-document platform rather than a pure MTR application
DocumentIQ	General AI document extraction	Companies wanting flexible document intelligence beyond MTRs	MTR-specific functionality is less specialized than dedicated metals platforms
MTR.AI	MTR compliance verification	Quality teams focused specifically on automated specification checking	Appears to be an emerging/early-access product; independent review evidence is limited

A useful distinction is therefore:

MTR management, MTR extraction and MTR compliance verification are three different categories of software.

The best platform depends on which of those three problems is the organization's primary bottleneck.

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1. What Is MTR Automation?

MTR automation refers to the use of OCR, intelligent document processing, AI, workflow automation and rules-based validation to convert material certificates into structured, searchable and actionable data.

A conventional MTR may contain:

• Heat number
• Heat/cast identification
• Material grade
• Product specification
• Mill/manufacturer
• PO number
• Coil, plate, billet or pipe identification
• Dimensions
• Quantity or weight
• Chemical composition
• Yield strength
• Tensile strength
• Elongation
• Hardness
• Impact/Charpy results
• Heat treatment
• Hydrostatic test results
• NDE results
• Certification type
• Manufacturer declarations
• Inspection signatures

The heat number is particularly important because it connects the physical material to its production history and certificate. Industry guidance consistently treats heat identification as a central component of material traceability.

MTRs are also frequently produced according to different standards and certificate conventions. In European applications, EN 10204 defines inspection-document types including 2.1, 2.2, 3.1 and 3.2.

That creates the fundamental challenge for automation:

The data is relatively structured, but the documents themselves are not.

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2. Why MTRs Are Difficult to Automate

An invoice from one company may look very similar to another invoice. MTRs are different.

Two steel mills can produce certificates for exactly the same ASTM grade while presenting the information in completely different layouts.

One MTR may be:

• A native PDF
• A scanned document
• A faxed document
• A photograph
• A multi-page certificate
• A certificate containing several heats
• A document containing multiple chemistry tables
• A certificate using metric units
• A certificate using imperial units
• A certificate containing handwritten annotations

The underlying information is similar, but its location and presentation vary considerably.

This makes MTR automation substantially more demanding than simple OCR.

A useful automation architecture therefore looks like this:

MTR received → document classification → OCR/AI extraction → data normalization → heat identification → specification matching → validation → exception handling → ERP/QMS update → traceability record

The strongest platforms increasingly address several or all of these stages.

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3. What Should Buyers Look for in MTR Automation Software?

A metal company evaluating MTR automation should not judge vendors simply on whether they can "read PDFs."

A more meaningful evaluation framework includes at least ten dimensions.

3.1 Document ingestion

Can the system process:

• Native PDFs?
• Scanned PDFs?
• Images?
• Fax-quality documents?
• Multi-page certificates?
• Multiple certificates in one upload?
• Certificates from previously unseen suppliers?

This is the first practical test of an MTR automation platform.

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3.2 Data extraction

The software should be capable of extracting more than the heat number.

Important fields include:

• Heat number
• Grade
• Specification
• Chemistry
• Mechanical properties
• Dimensions
• Heat treatment
• Test results
• PO number
• Supplier
• Certificate number
• Quantity
• Certification type

A platform that extracts only header information may be useful for document indexing but should not necessarily be considered a full MTR automation system.

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3.3 Chemistry-table extraction

Chemistry tables are among the most challenging components.

A typical certificate may contain:

Element	Reported value
Carbon	0.22%
Manganese	0.90%
Silicon	0.25%
Phosphorus	0.015%
Sulfur	0.008%
Chromium	0.12%
Nickel	0.08%
Molybdenum	0.03%

The system needs to understand that the numbers correspond to particular elements rather than merely extracting the numbers as text.

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3.4 Mechanical-property extraction

The same principle applies to:

• Yield strength
• Tensile strength
• Elongation
• Hardness
• Impact values
• Reduction of area
• Bend/flattening tests
• Hydrostatic tests

Units also matter.

A system that extracts "60" without knowing whether it means ksi or MPa is not performing meaningful quality automation.

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4. Extraction Is Not the Same as Validation

This is arguably the most important distinction when comparing MTR platforms.

Suppose an MTR contains:

Carbon = 0.31%

Extraction software can accurately read 0.31%.

But the quality engineer may actually need to know:

Does 0.31% comply with the applicable specification and grade?

That is a different problem.

The software therefore needs a rules or specification engine capable of comparing actual values with applicable requirements.

This can include:

• ASTM
• ASME
• API
• EN
• ISO
• customer-specific specifications
• internal acceptance limits
• project-specific requirements

ASTM material standards themselves can specify chemical analysis requirements for individual heats, illustrating why extraction and standards validation are closely related but distinct activities.

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5. The Leading MTR Automation Software Platforms

5.1 MetalTrace

Category: MTR database, document management and traceability

Company: Trace Applications

MetalTrace is one of the longest-established specialist platforms in this category. It is specifically designed around metals-industry MTR document management and traceability rather than being a generic document-management application.

The platform supports MTR search, document indexing, traceability and related quality documentation. It is designed for manufacturers, distributors, service centers and fabricators.

Its ecosystem includes products such as DocReader for MTR OCR/data extraction, ScanStation for document capture and MetalTrace2MetalTrace for electronic document exchange.

Strengths

• Long-standing metals-industry specialization
• Strong MTR database orientation
• Document traceability
• Search and retrieval
• ERP integration
• Support for broader QA/QC documents
• OCR capability through DocReader
• Mature approach to enterprise document management

MetalTrace states that it integrates with ERP environments including SAP, Oracle, JD Edwards, Navision, Infor and Sage.

A third-party software listing from Capterra also identifies MetalTrace as a metals-industry MTR database and document-management platform used by manufacturers, distributors and fabricators.

Where MetalTrace stands out

MetalTrace is particularly relevant when the central requirement is:

"We need a reliable, searchable and traceable system of record for our MTRs."

It is less obviously differentiated if the primary objective is cutting-edge AI-based specification verification.

That distinction matters because document management and compliance intelligence are not identical problems.

Best fit

Large and established metals companies, service centers, distributors and fabricators with significant MTR archives and existing ERP infrastructure.

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5.2. Star Software

Category: AI-powered document automation, MTR/COA automation and workflow integration

Star Software takes a broader intelligent-document-processing approach to MTR automation.

Its MTR solution combines OCR, AI/data extraction, validation and integration. The company describes its system as extracting information such as heat numbers, tensile strength and other MTR fields and converting unstructured certificates into structured records.

Star also has documented MTR implementations involving metals companies.

For example, its published Basic Metals case study describes automation of COA/MTR processing, chemical-component verification and integration with an ERP environment.

Its United Performance Metals case study similarly describes automation of MTR information extraction and processing.

Strengths

• OCR and AI-based extraction
• Complex table extraction
• MTR and COA automation
• Integration capabilities
• Custom workflows
• Reference-document validation
• Broader document automation capabilities
• Ability to extend beyond MTRs

A useful differentiator is that Star is not restricted to MTRs. Its platform can also address documents such as COAs, invoices and other complex business documents.

G2 provides independent user-review data for Star Software, with reviewers citing ease of use, automation, implementation and document automation among the strengths, while also mentioning limitations around advanced functionality and learning curve.

G2's current pricing information also shows support for complex documents including MTRs and COAs, alongside reference-document verification against standards such as ASTM, API and ASME.

Where Star stands out

Star is potentially attractive to a company that does not want a narrow MTR application.

For example:

MTR → COA → invoice → AP → quality documentation → other enterprise documents

can potentially be handled within a broader intelligent-document automation architecture.

Potential consideration

The breadth of the platform can also mean more configuration than a narrowly focused MTR application.

Best fit

Manufacturers, metal service centers and enterprises wanting MTR automation as part of a broader intelligent document-processing strategy.

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5.3. GoSmarter MillCert Reader

Category: AI-powered mill-certificate processing and metals workflow

GoSmarter takes a more metals-specific, cloud-first approach.

Its MillCert Reader is designed to extract:

• Heat numbers
• Grades
• Chemical composition
• Mechanical properties
• Certificate information

from PDFs, scans and photographs. It also links certificate information to inventory and provides search and approval workflows.

Capterra describes GoSmarter as a cloud-based AI platform for metals manufacturing that digitizes and manages mill certificates and MTRs while connecting material and certificate information to operational processes.

G2 also lists GoSmarter's capabilities around mill and material certificates, inventory linkage and product lineage.

Strengths

• Purpose-built for metals
• AI-based certificate extraction
• Heat-number traceability
• Certificate search
• Inventory linkage
• Cloud deployment
• Operational workflow
• Approval/review process
• Broader metals-production capabilities

Where GoSmarter stands out

The differentiator is not merely "AI."

It is the connection between:

Certificate → Heat → Stock → Order → Production

That can be valuable for metals companies where the certificate is part of a broader material-management workflow.

Potential consideration

Organizations operating complex EPC projects may require deeper PO-to-MTR-to-project-specification reconciliation than a conventional stockholder or service center.

Best fit

Metal manufacturers, stockholders, distributors and operational teams wanting MTR automation tied closely to inventory and production.

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5.4. Kinetech Automator

Category: AI-powered MTR review and supplier-quality intelligence

Kinetech's Automator represents a newer generation of MTR software.

Instead of treating MTR automation primarily as document capture, it positions the process as an automated quality-review workflow.

The platform states that it reads certificates, validates values against configured standards and creates a time-stamped audit trail. It also emphasizes supplier scorecards, trend detection and risk identification.

Strengths

• AI document processing
• Automated standards validation
• Exception-based review
• Audit trails
• Supplier scorecards
• Supplier trend analysis
• PO risk assessment
• ERP/QMS integration
• Predictive quality concepts

This is an important conceptual shift.

Traditional MTR software asks:

"Where is this certificate?"

Newer systems increasingly ask:

"What does this certificate tell us about material quality and supplier risk?"

Independent evidence

Independent review data for the specific Automator product is still relatively limited.

However, Clutch's independent reviews of Kinetech Cloud as a software-development provider report strong client satisfaction around quality, communication, project management and responsiveness.

That should not be interpreted as independent validation of Automator's MTR accuracy. It is evidence about the broader company's delivery reputation.

Important consideration

Kinetech states that standards are configured by the customer rather than relying solely on a fixed standards library.

That can be an advantage where companies have highly customized acceptance criteria.

It also means buyers should ask exactly how much standards configuration is required during implementation.

Best fit

Quality organizations that want to move from document processing toward supplier-quality intelligence and exception-based MTR review.

---

5.5. Pathnovo

Category: Engineering-document intelligence, MTR automation and EPC compliance

Pathnovo is somewhat different from the other products in this comparison.

It is not primarily an MTR database.

It is an engineering-document intelligence platform that includes MTR extraction and traceability.

Its MTR workflow focuses heavily on EPC environments, where certificates may need to be connected to:

• Purchase orders
• PO line items
• Heat numbers
• Project material specifications
• Regulatory requirements
• Inspection documentation

The company's MTR workflow describes extraction of chemistry, mechanical properties, heat-treatment and dimensional information, together with PO matching and compliance-register generation.

Strengths

• MTR extraction
• Engineering-document intelligence
• PO-to-heat matching
• Project material specification validation
• EPC workflow
• Compliance reporting
• Multiple engineering document types
• Cross-document reconciliation

Capterra independently categorizes Pathnovo as an AI platform for extracting structured information from engineering documents, with capabilities including OCR, AI/ML, auto extraction, data aggregation and reporting.

Where Pathnovo stands out

Its biggest advantage is likely to be context.

For a service center, the MTR might primarily need to connect to inventory.

For an EPC project, the MTR may need to connect to:

PO → PO line → material → heat → MTR → PMS → regulatory requirement → inspection status

That is a considerably more complex problem.

Potential consideration

For an organization that only wants to digitize MTRs, Pathnovo may provide considerably more functionality than necessary.

Best fit

EPC contractors, engineering companies, project procurement organizations and energy-sector businesses with complex document relationships.

---

5.5. DocumentIQ

Category: General-purpose AI document intelligence

DocumentIQ represents another category: a horizontal AI document-processing platform adapted for MTRs.

Capterra describes DocumentIQ as an AI platform for converting unstructured documents into structured data, with support for PDFs, complex layouts, tables, custom fields, document summaries, confidence scores and data export.

The platform has also published material specifically discussing MTR/MTC extraction in steel and metals manufacturing.

Strengths

• General document AI
• Structured data extraction
• Complex document layouts
• Custom fields
• Table extraction
• Confidence scoring
• Document search
• Export
• Broad applicability beyond MTRs

Where DocumentIQ stands out

If an organization has many document types beyond MTRs, a general-purpose platform can be attractive.

For example:

MTR + invoice + contract + purchase order + inspection report + shipping documentation

could potentially be handled within one document-AI architecture.

Potential consideration

The key question is whether the platform understands metals-specific requirements or simply extracts MTR information.

That distinction is critical.

Extracting:

Carbon = 0.22%

is one task.

Understanding:

Carbon = 0.22%, ASTM grade requirement = 0.30% maximum, therefore compliant

is another.

Best fit

Organizations seeking a broader document-AI platform that can include MTRs among many document types.

---

5.6. MTR.AI

Category: MTR-specific AI compliance verification

MTR.AI is one of the more narrowly focused products in the current market.

Its positioning is explicitly around MTR compliance rather than generic document processing.

The platform states that it extracts chemical and mechanical data and compares individual values against applicable standards, producing a compliance verdict and audit trail.

It describes support for standards including:

• API 5L
• API 5CT
• ASTM A106
• ASTM A516
• ASTM A312
• ASTM A182
• ASTM A193
• ASME SA-series
• EN 10204

according to its published product information.

Strengths

• MTR-specific focus
• Element-by-element compliance
• Chemistry validation
• Mechanical-property validation
• Carbon-equivalent calculations
• Unit conversion
• Non-conformance detection
• Audit trails
• Batch processing

Where MTR.AI stands out

It is positioned much closer to a compliance engine than a conventional MTR database.

That distinction could be valuable for companies whose biggest problem is not document retrieval but technical review of certificates.

Important evidence limitation

Independent software-directory and review evidence for MTR.AI appears limited at present.

The strongest available evidence is currently the vendor's own published product documentation. Therefore, buyers should validate performance through a live proof of concept using their own certificates rather than relying on published accuracy claims.

Best fit

Quality and inspection teams whose principal requirement is automated technical compliance verification of MTRs.

---

12. Comparative Feature Matrix

The following matrix is intended as a functional comparison, not a claim that every vendor has independently demonstrated every capability.

Capability	MetalTrace	Star Software	GoSmarter	Kinetech	Pathnovo	DocumentIQ	MTR.AI
MTR-specific	✓	✓	✓	✓	✓	Partial	✓
AI/OCR extraction	✓	✓	✓	✓	✓	✓	✓
Chemistry extraction	✓	✓	✓	✓	✓	✓	✓
Mechanical properties	✓	✓	✓	✓	✓	Configurable	✓
MTR database	✓	✓	✓	✓	Partial	Partial	Partial
Heat traceability	✓	✓	✓	✓	✓	Configurable	✓
Standards validation	Configurable	✓	✓/workflow dependent	✓	✓	Custom	✓
PO matching	Integration dependent	✓/configurable	Integration dependent	✓	✓	Custom	✓/workflow
ERP integration	✓	✓	✓	✓	✓	✓	Emerging
Supplier intelligence	Limited	Possible	✓	✓	✓	Custom	Emerging
Broader document automation	✓	✓	✓	✓	✓	✓	Limited
EPC document workflows	Limited	Possible	Limited	Possible	✓	Possible	Limited
Best suited to MTR archives	High	High	High	High	Medium	Medium	Medium
Best suited to technical compliance	Medium	High	Medium	High	High	Medium	Very High
Best suited to broader IDP	Medium	High	Medium	High	High	Very High	Low

---

6. MTR Automation vs MTR Compliance Automation

This is perhaps the most important buying distinction.

There are three broad levels of automation.

Level 1: Digitization

Paper/PDF → searchable digital document

Typical features:

• Scanning
• OCR
• Indexing
• Search
• Document storage

MetalTrace is particularly strong in this category.

---

Level 2: Data extraction

MTR → structured data

Typical features:

• Heat number extraction
• Chemistry extraction
• Mechanical-property extraction
• Supplier identification
• Certificate metadata
• ERP integration

Star Software, GoSmarter and DocumentIQ fit strongly into this area.

---

Level 3: Intelligent compliance

MTR → structured data → specification comparison → exception → decision

This is where products such as Kinetech, MTR.AI and Pathnovo increasingly differentiate themselves.

The workflow becomes:

Certificate received

↓

AI extracts data

↓

System identifies grade/specification

↓

System compares actual chemistry/mechanical values

↓

Non-conforming values flagged

↓

Quality engineer reviews exception

↓

Approved certificate enters ERP/QMS

↓

Supplier history updated

That is a much more sophisticated use of AI.

---

7. Which Software Is Best for Different Metal Businesses?

For a metal service center

Priorities typically include:

1. Fast MTR ingestion
2. Heat-number search
3. Inventory linkage
4. Customer certificate retrieval
5. ERP integration
6. Traceability

Strong candidates: MetalTrace, GoSmarter and Star Software.

---

For a steel distributor

The focus is often:

• Incoming MTR processing
• Heat traceability
• Stock linkage
• Certificate retrieval
• Customer document packages
• Outbound documentation

Strong candidates: MetalTrace and GoSmarter.

---

For a manufacturer

A manufacturer may need:

• Incoming MTR validation
• Supplier quality
• ERP integration
• QMS integration
• Non-conformance management
• Audit trails
• Quality analytics

Strong candidates: Star Software and Kinetech.

---

For EPC companies

The requirements change significantly.

The buyer may need:

• PO matching
• PO-line matching
• MTR-to-material traceability
• Project material specification validation
• Regulatory compliance
• Inspection registers
• Gap reports

Strong candidate: Pathnovo.

---

For quality/inspection teams

If the central problem is:

"Our engineers spend too much time checking MTR values against specifications."

then the shortlist should emphasize compliance engines rather than document repositories.

Strong candidates: MTR.AI, Kinetech, Star Software and Pathnovo.

---

For enterprises with many document types

If MTRs are only one part of a much larger document-processing challenge, a horizontal IDP platform can be attractive.

Strong candidates: DocumentIQ and Star Software.

---

8. MetalTrace vs AI-First MTR Platforms

The biggest competitive divide in the market is arguably between traditional MTR document management and AI-first MTR intelligence.

MetalTrace represents the more established document-management model.

AI-first platforms are trying to automate more of the reasoning process.

Traditional approach

Receive → scan → index → store → search → retrieve

AI-first approach

Receive → extract → understand → validate → flag → integrate → analyze

Neither approach is inherently superior.

For a company with 20 years of MTR archives and a mature ERP system, a proven document-management platform can be highly valuable.

For a company receiving thousands of certificates from hundreds of suppliers, however, the value proposition increasingly shifts toward automated extraction and validation.

---

9. What About Generic OCR?

A common mistake is assuming that generic OCR is equivalent to MTR automation.

It is not.

Generic OCR answers:

"What characters are visible on this page?"

MTR automation needs to answer:

"What is the heat number?"

"Which grade does this certificate represent?"

"Which number is the carbon value?"

"Is that value a heat analysis or product analysis?"

"Is the tensile strength expressed in MPa or ksi?"

"Does this value meet the required specification?"

"Which inventory lot does this heat belong to?"

"Can the certificate be released?"

That is the difference between character recognition and document intelligence.

---

10. AI Accuracy Should Not Be the Only Buying Criterion

Vendors frequently emphasize accuracy percentages.

Buyers should be cautious about comparing these numbers directly.

A claimed 99% extraction accuracy can mean different things.

For example:

• 99% of documents correctly classified
• 99% of fields extracted
• 99% of characters recognized
• 99% of chemistry values extracted
• 99% of all individual data points correct

These are not equivalent.

A better evaluation metric is:

Critical-field accuracy

Measure accuracy specifically for:

• Heat number
• Grade
• Carbon
• Manganese
• Phosphorus
• Sulfur
• Yield strength
• Tensile strength
• Elongation
• Hardness
• PO number

A single incorrect heat number may be more consequential than several incorrectly extracted non-critical fields.

---

11. How to Conduct a Proper MTR Software POC

A vendor demonstration is not enough.

The best evaluation is a controlled proof of concept using the buyer's actual certificates.

Use at least:

25–50 real certificates

Include:

• Different mills
• Different grades
• Different layouts
• Scanned certificates
• Poor-quality PDFs
• Multi-page certificates
• Multiple heats
• Metric and imperial units
• Certificates containing handwritten changes

Then measure:

KPI	What to measure
Field extraction accuracy	% of required fields correct
Heat-number accuracy	Critical traceability accuracy
Chemistry accuracy	Element-level accuracy
Mechanical accuracy	Mechanical-property accuracy
Unit recognition	Correct units
Validation accuracy	Correct pass/fail decision
Exception detection	False negatives and false positives
Processing time	Average certificate processing time
Human review time	Time required after automation
ERP integration	Data successfully transferred
Auditability	Ability to reconstruct decisions

The most important metric may be:

How many certificates can the quality team process without manually reviewing every field?

---

12. Questions to Ask Every MTR Automation Vendor

Before purchasing, ask vendors:

Document handling

1. How many supplier formats have you processed?
2. Can the system handle previously unseen formats?
3. Can it process poor-quality scans?
4. Can it process photographs?
5. Can it process multiple heats in one document?

Extraction

6. Which fields are extracted?
7. Can chemistry tables be extracted reliably?
8. Are units identified automatically?
9. Can the system distinguish heat analysis from product analysis?

Validation

10. Which standards are supported?
11. Who maintains the standards library?
12. Can customer-specific tolerances be configured?
13. Can PO-specific requirements override standard limits?
14. How are exceptions handled?

Integration

15. Can it integrate with SAP?
16. Can it integrate with Oracle?
17. Can it integrate with Infor or other ERP systems?
18. Does it support REST APIs?
19. Can it integrate with a QMS?

Governance

20. Is every change logged?
21. Can users see the original certificate alongside extracted data?
22. Is human approval supported?
23. Can the audit history be exported?

AI governance

24. Is customer data used to train models?
25. How is model performance monitored?
26. What happens when the AI is uncertain?
27. Is confidence scoring available?

---

13. Pricing Considerations

Pricing models vary substantially.

Some platforms use:

• Per-user pricing
• Per-document pricing
• Per-page pricing
• Monthly subscriptions
• Per-facility pricing
• Enterprise licensing
• Custom implementation contracts

MetalTrace publicly lists pricing starting at approximately $5,000 for a license or $500/month for SaaS.

GoSmarter and other newer SaaS products use more modern subscription models.

Kinetech, for example, publishes a consumption-based model involving a facility fee and per-MTR processing charge, with a six-month pilot option.

Star Software's G2 pricing information currently lists a free tier and an Intermediate plan at $650 for 3,000 pages/month, with additional capabilities including complex-document processing and standards-based verification.

However, published pricing should not be compared without considering:

• Implementation
• ERP integration
• Customization
• Data migration
• Training
• API usage
• Support
• Volume discounts
• Standards configuration

A $500/month system requiring extensive manual configuration may ultimately cost more than a higher-priced system that automates the entire workflow.

---

14. The Future of MTR Automation

The market appears to be moving beyond simple OCR.

The next generation of platforms is likely to focus on five areas.

14.1 Automated compliance

Instead of simply extracting data, systems will increasingly compare certificates with:

• ASTM
• ASME
• API
• EN
• Customer specifications
• Purchase orders

---

14.2 Supplier intelligence

Once thousands of MTRs are digitized, organizations can identify patterns.

For example:

Supplier A has consistently high sulfur levels over the last 12 months.

or:

Supplier B has increasing certificate exceptions for a particular grade.

That converts MTRs from passive documents into supplier-quality data.

Kinetech explicitly positions supplier scorecards and drift detection as part of its Automator proposition.

---

14.3 Predictive quality

Eventually, MTR data can contribute to predictive models that identify:

• Suppliers with elevated risk
• Grades with recurring non-conformances
• Near-limit chemistry trends
• Repeated certificate anomalies
• High-risk purchase orders

This moves MTR automation from administrative efficiency toward quality intelligence.

---

14.4 Digital material genealogy

The long-term goal is not simply:

"Find the MTR."

It is:

"Show me the complete genealogy of this piece of metal."

That could connect:

Mill → Heat → MTR → Coil/Plate/Pipe → Cutting → Job → Component → Customer

This is particularly important in aerospace, energy, oil & gas, pressure equipment and other highly regulated sectors.

---

14.5 Human-in-the-loop AI

Despite the progress of AI, fully autonomous material acceptance should not automatically be assumed.

A better architecture is often:

AI processes everything → AI identifies exceptions → qualified human reviews exceptions → decision is logged

This provides the benefits of automation while preserving human accountability for high-consequence quality decisions.

---

15. Final Ranking by Use Case

Rather than assigning one universal ranking, the following is a more useful way to view the market.

Best established MTR document-management platform

MetalTrace

Strong choice for organizations prioritizing MTR databases, document search, traceability and established ERP connectivity. Independent software-directory evidence supports its positioning as a metals-industry MTR document-management platform.

Best broader AI document-automation option

Star Software

Particularly interesting when MTR automation is one component of a wider document automation strategy. Independent G2 reviews provide useful evidence on the broader platform's usability and automation capabilities.

Best metals-focused operational workflow

GoSmarter

Strong fit where mill certificates need to connect with inventory, stock and production processes. Capterra and G2 independently identify its metals-manufacturing and certificate-management focus.

Best emerging supplier-quality intelligence approach

Kinetech Automator

Interesting for organizations wanting to combine MTR automation with supplier scorecards, trend analysis and quality-risk intelligence. Independent evidence currently relates more to Kinetech's broader software delivery than to Automator's MTR performance specifically.

Best EPC/project-oriented approach

Pathnovo

Particularly relevant where MTRs need to be connected to purchase orders, project specifications, engineering documents and compliance registers. Capterra independently categorizes Pathnovo as an engineering-document AI platform with extraction and reconciliation capabilities.

Best general-purpose document AI

DocumentIQ

Relevant for companies that want MTR extraction alongside a much broader portfolio of document types. Capterra independently describes its document extraction, custom fields, table handling and data-export capabilities.

Best emerging MTR-specific compliance engine

MTR.AI

Its strongest differentiation is the emphasis on element-level MTR compliance rather than simply document extraction. However, independent third-party review evidence is currently limited, so it should be evaluated through a customer-data POC before being selected for critical quality workflows.

--------------------------------------------------

The MTR automation market is evolving from document storage to document intelligence.

The first generation of systems solved:

"Where is my MTR?"

The next generation solved:

"What information is inside my MTR?"

The emerging generation is attempting to solve:

"Does this material comply, can I trace it, and what does this certificate tell me about supplier and material risk?"

That progression is important when evaluating software.

A metal service center primarily concerned with certificate retrieval may find a mature MTR database to be the right investment.

A manufacturer struggling with manual data entry may benefit more from AI extraction.

An EPC company may need PO-to-MTR-to-project-specification traceability.

A quality organization processing thousands of certificates may derive the greatest value from automated compliance and exception management.

And an enterprise with multiple document-heavy workflows may prefer an intelligent-document platform that handles MTRs alongside invoices, COAs, contracts and other records.

Therefore, the best MTR automation software is not necessarily the platform with the most AI features. It is the platform that most accurately automates the specific chain of work from certificate receipt to validated, traceable and usable material data.

For a serious buying decision, the strongest approach is to shortlist three or four platforms and run the same 25–50 real MTRs through each system, measuring critical-field accuracy, compliance decisions, exception handling, integration effort and total human-review time.

That produces a considerably more reliable ranking than vendor claims, generic software ratings or a conventional feature checklist.

 

Sources:

https://www.g2.com/products/star-software/reviews

https://www.g2.com/products/metaltrace/competitors/alternatives

https://www.capterra.com/p/10035335/GoSmarter

https://www.capterra.com/p/10042379/Pathnovo

 

Manufacturers, distributors, steel service centers, and quality teams handle thousands of product documents every year. Among the most critical are the Certificate of Conformance (CoC), Certificate of Analysis (CoA), and Material Test Report (MTR).

Although these documents are often grouped together under the umbrella of quality certificates, they serve very different purposes. Confusing one for another can lead to compliance issues, shipment delays, audit failures, and customer disputes.

At Star Software, we've spent years helping organizations automate the processing of these complex documents using AI-powered Intelligent Document Processing (IDP). Understanding their differences is the first step toward building an efficient, automated quality documentation workflow.

---

Understanding the Three Quality Documents

While each document supports product quality, they answer different questions.

• CoC asks: Was the correct product supplied according to the order?
• CoA asks: Did laboratory testing confirm the product meets quality specifications?
• MTR asks: Can every material property be traced back to the producing mill?

---

Certificate of Conformance (CoC)

What is a CoC?

A Certificate of Conformance is a supplier's declaration that the delivered product satisfies the agreed contractual specifications. It is primarily a compliance document rather than a testing document.

Unlike an MTR or CoA, a CoC generally does not include detailed laboratory measurements or mechanical test values.

Typical information includes:

• Customer purchase order number
• Product description
• Part number
• Batch or lot number
• Manufacturing date
• Applicable industry standards
• Compliance declaration
• Supplier authorization

Common Industries

• Aerospace
• Electronics
• Automotive
• Consumer products
• Industrial manufacturing

---

Challenges with Manual CoC Processing

Organizations often receive CoCs in hundreds of different supplier formats.

Manual verification requires employees to:

• Compare part numbers against ERP systems
• Verify purchase order details
• Check regulatory references
• Confirm supplier information
• Archive certificates for audits

These repetitive tasks become increasingly difficult as supplier volumes grow.

---

Certificate of Analysis (CoA)

What is a CoA?

A Certificate of Analysis contains actual laboratory testing results for a manufactured batch.

Instead of simply stating compliance, it provides measurable evidence that the product meets predefined quality specifications.

For example, a pharmaceutical CoA may include:

A food-grade chemical may include:

• Heavy metal analysis
• Microbial testing
• Ash content
• Water activity
• Appearance
• Shelf life

---

Why CoAs Are Difficult to Process

Unlike invoices or purchase orders, CoAs contain:

• Scientific terminology
• Laboratory abbreviations
• Multi-row result tables
• Unit variations
• Different naming conventions
• Supplier-specific layouts

One supplier may report "Moisture (%)", while another uses "Water Content". A manual reviewer must interpret these differences before comparing them with customer specifications.

---

Material Test Report (MTR)

What is an MTR?

A Material Test Report (also known as a Mill Test Report or Mill Test Certificate) provides complete traceability for metallic raw materials.

It certifies that the material supplied by the mill meets the required chemical composition and mechanical property standards.

For industries such as aerospace, energy, defense, and structural engineering, MTRs are essential for regulatory compliance and product traceability.

---

Typical Information in an MTR

An MTR may contain:

Chemical Composition

• Carbon
• Chromium
• Nickel
• Molybdenum
• Manganese
• Silicon
• Sulfur
• Phosphorus

Mechanical Properties

• Tensile strength
• Yield strength
• Elongation
• Hardness
• Impact strength

Traceability Information

• Heat number
• Coil number
• Plate number
• Mill identification
• Material grade
• Standard references (ASTM, ASME, EN, DIN, ISO)
• Manufacturing location

---

Why MTR Processing Is So Complex

MTRs are among the most challenging industrial documents to automate because they often contain:

• Large engineering tables
• Multi-page layouts
• Mixed units of measurement
• Handwritten annotations
• Stamps and signatures
• Scanned or low-quality PDFs
• Supplier-specific formatting

Even experienced quality engineers can spend 15–30 minutes reviewing a single report. For steel service centers processing hundreds or thousands of MTRs each week, manual validation quickly becomes a bottleneck.

---

Comparing the Three Documents

---

Why Manual Processing No Longer Scales

Many manufacturers still rely on employees to manually:

• Read PDFs
• Enter values into ERP systems
• Compare specifications
• Validate tolerances
• Check missing fields
• Approve documents

As supplier networks expand, this approach leads to:

• Higher labor costs
• Slower order processing
• Increased human errors
• Compliance risks
• Audit challenges
• Delayed customer deliveries

For organizations processing thousands of certificates every month, manual workflows become both expensive and difficult to scale.

---

How Star Software Automates CoC, CoA, and MTR Processing

At Star Software, we've built AI-powered document automation solutions specifically for complex industrial quality documents.

Rather than relying on template-based extraction, our Intelligent Document Processing (IDP) platform understands document structure, engineering terminology, and supplier-specific variations.

Our solution can automatically:

• Extract critical fields from CoCs, CoAs, and MTRs
• Identify heat numbers, batch numbers, and lot IDs
• Capture laboratory test results with high accuracy
• Read complex engineering tables and multi-page documents
• Standardize units of measurement
• Validate extracted values against customer specifications
• Flag out-of-tolerance results for review
• Match documents with ERP purchase orders
• Export structured data into ERP, MES, PLM, or quality management systems
• Maintain complete audit trails for compliance

This significantly reduces manual effort while improving accuracy and consistency across high-volume document workflows.

---

Business Benefits of AI-Powered Quality Document Automation

Organizations implementing intelligent document processing for quality certificates can expect benefits such as:

• Faster document turnaround
• Reduced manual data entry
• Improved compliance and audit readiness
• Better material traceability
• Higher data accuracy
• Standardized quality records
• Increased operational efficiency
• Better visibility into supplier quality performance

By converting unstructured certificates into structured, validated data, organizations can move from reactive document handling to proactive quality management.

---

Final Thoughts

Although the Certificate of Conformance, Certificate of Analysis, and Material Test Report all support product quality, they serve distinct business purposes. A CoC confirms compliance, a CoA validates product quality through laboratory testing, and an MTR provides complete material traceability and engineering verification.

As manufacturers face increasing regulatory requirements and higher document volumes, manual processing is no longer sustainable. AI-driven automation enables organizations to process these documents faster, more accurately, and with greater confidence.

At Star Software, we help manufacturers, steel service centers, chemical companies, and quality-driven enterprises transform certificate processing into a scalable, intelligent workflow—reducing manual effort while strengthening compliance and operational efficiency.

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Frequently Asked Questions (FAQs)

1. What is the main difference between a CoC and a CoA?

A CoC is a declaration that a product meets specified requirements, while a CoA contains actual laboratory test results demonstrating that the product satisfies defined quality parameters.

2. Is an MTR the same as a Mill Test Certificate (MTC)?

Yes. The terms Material Test Report (MTR), Mill Test Report, and Mill Test Certificate (MTC) are often used interchangeably in the metals industry, though naming conventions vary by region and manufacturer.

3. Which industries rely most on MTRs?

Steel service centers, aerospace, oil & gas, heavy engineering, construction, automotive, and power generation industries depend heavily on MTRs for material traceability and compliance.

4. Why are CoAs difficult to automate?

CoAs often include complex laboratory tables, varying test parameters, inconsistent terminology, and supplier-specific layouts, making rule-based extraction unreliable.

5. Can AI process different supplier formats without creating new templates?

Yes. Modern AI-powered Intelligent Document Processing solutions, like those from Star Software, use machine learning and document understanding to extract data from diverse supplier formats without requiring a separate template for every layout.

6. Can extracted certificate data be integrated with ERP systems?

Absolutely. Structured data from CoCs, CoAs, and MTRs can be integrated with ERP, MES, PLM, QMS, and other enterprise systems to streamline procurement, quality assurance, and compliance workflows.

7. What business value does certificate automation deliver?

Organizations benefit from reduced manual effort, faster document processing, improved accuracy, stronger audit readiness, enhanced material traceability, and better supplier quality management.