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.

Certificates of Analysis (COAs) play a critical role in ensuring product quality, regulatory compliance, and supplier accountability. Industries such as pharmaceuticals, chemicals, food and beverage, cosmetics, and specialty manufacturing rely heavily on COAs to verify that products meet specified standards before they reach customers.

However, despite their importance, many organizations still process COAs manually—a time-consuming and error-prone practice that creates bottlenecks across quality assurance and supply chain operations.

So, what is the best way to digitize Certificates of Analysis?

The answer lies in combining Artificial Intelligence (AI), Optical Character Recognition (OCR), and Intelligent Document Processing (IDP) to transform unstructured COA documents into validated, structured business data.

The Best Approach: AI-Powered Intelligent Document Processing

While basic OCR technology can convert text from images into digital format, it often struggles with complex COA layouts and varying supplier templates.

Modern Intelligent Document Processing (IDP) goes far beyond traditional OCR by combining:

Optical Character Recognition (OCR)

Extracts text from scanned or digital COA documents.

Artificial Intelligence (AI)

Identifies key fields regardless of document format.

Machine Learning

Learns from historical COAs and continuously improves extraction accuracy.

Validation Engines

Compares extracted values against predefined quality specifications and business rules.

Workflow Automation

Routes exceptions to quality teams while automatically approving compliant documents.

This approach enables organizations to process thousands of COAs with minimal human intervention.

Key Capabilities of an Effective COA Digitization Solution

1. Multi-Format Document Processing

The solution should handle:

• PDF COAs
• Scanned certificates
• Images
• Supplier-specific templates
• Multi-page documents

without requiring template-specific configurations.

2. Automated Data Extraction

The platform should automatically capture:

• Product identifiers
• Quality attributes
• Laboratory results
• Specification ranges
• Supplier details

and convert them into structured digital records.

3. Automated Validation

One of the biggest advantages of AI-powered digitization is automatic validation.

For example:

If a product specification requires a purity level between 98% and 100%, the system can automatically compare extracted values against acceptable thresholds and flag deviations immediately.

4. ERP, QMS, and LIMS Integration

The best solutions integrate directly with:

• ERP systems
• Quality Management Systems (QMS)
• Laboratory Information Management Systems (LIMS)
• Supply Chain Platforms

This eliminates duplicate data entry and accelerates business processes.

5. Audit-Ready Document Repository

Digitized COAs should be stored in a searchable repository, enabling instant retrieval during:

• Customer audits
• Regulatory inspections
• Internal quality reviews
• Supplier performance assessments

Benefits of Digitizing Certificates of Analysis

Organizations implementing AI-powered COA automation often experience significant operational improvements.

Faster Processing

Documents that previously required several minutes of manual review can be processed in seconds.

Improved Accuracy

AI-based extraction significantly reduces transcription errors and missing information.

Better Compliance

Automated validation helps ensure adherence to FDA, GMP, ISO, and customer-specific quality requirements.

Reduced Operational Costs

Automation decreases the need for repetitive manual data entry and document handling.

Faster Product Release

Quality teams can review exceptions rather than every document, accelerating product approvals and shipments.

Enhanced Supplier Management

Digitized COA data provides valuable insights into supplier performance, quality trends, and compliance history.

Industries Benefiting Most from COA Digitization

COA automation delivers substantial value across multiple industries:

Pharmaceuticals

Accelerates batch release and supports regulatory compliance.

Chemicals

Ensures accurate validation of chemical properties and specifications.

Food & Beverage

Improves food safety documentation and supplier quality management.

Cosmetics

Supports ingredient verification and quality assurance processes.

Manufacturing

Enhances traceability and quality control across supply chains.

The Future of COA Processing

As AI continues to evolve, organizations are moving beyond simple document digitization toward intelligent quality automation.

Future capabilities include:

• Predictive quality analytics
• Automated supplier scorecards
• Real-time compliance monitoring
• Intelligent exception handling
• Self-learning extraction models

Companies that adopt AI-driven COA automation today will be better positioned to improve operational efficiency, reduce compliance risks, and scale quality processes as their business grows.

Conclusion

The best way to digitize Certificates of Analysis is through AI-powered Intelligent Document Processing that combines OCR, machine learning, automated validation, and workflow automation. Unlike traditional manual processes or basic OCR solutions, modern AI platforms can extract, validate, and integrate COA data at scale while improving accuracy, compliance, and operational efficiency.

For organizations handling large volumes of quality documents, COA digitization is no longer just a productivity initiative—it's a strategic investment in quality, compliance, and business growth.

Material Test Reports (MTRs) and Certificates of Analysis (COAs) are critical documents for ensuring quality, compliance, and traceability across manufacturing, metals, chemicals, pharmaceuticals, and food industries.

 

In several regulated and precision-driven industries—such as aerospace alloys, medical implants, oil & gas tubing, and automotive safety components—manufacturers must manage both a Material Test Report (MTR) from their suppliers and a Certificate of Analysis (COA) generated within their own plant. Although these two documents serve related purposes, they originate at different stages of the value chain, which often creates a complex and time-consuming workflow. As production volumes and compliance demands rise, this dual-document requirement has become one of the most underestimated bottlenecks in quality assurance.

Why Both Documents Matter

The MTR provides upstream material assurance. It is issued by the metal mill or supplier and validates the raw material’s chemical composition, mechanical properties, heat number, and conformance to standards such as ASTM or ASME. In simple terms, an MTR answers the question: Was the material manufactured correctly before entering our factory? On the other hand, the COA reflects downstream production validation. It is created by the manufacturer after machining, forming, coating, or heat treatment and includes dimensional checks, surface finish values, additional chemical or mechanical tests, and any customer-specific inspections. A COA answers the complementary question: Did the finished product meet the customer’s exact requirements?

In high-assurance sectors like precision tubing for oil wells, orthopedic components, superalloy blades, and critical automotive parts, customers insist on receiving both documents for each batch. Together, MTRs and COAs provide full lifecycle traceability, from the moment the alloy is melted to the moment the final component is shipped.

Where the Workflow Starts Breaking Down

Handling both MTRs and COAs manually quickly becomes inefficient, especially when manufacturers process dozens or hundreds of batches per day. Quality teams often find themselves spending significant time cross-verifying values from two different documents that rarely follow the same layout. Supplier MTRs come in varied PDF formats, forcing inspectors to search for chemistry, mechanical properties, heat numbers, and material grades across different designs. Meanwhile, COAs require operators to retype test values into ERP systems, quality modules, or customer-specific templates. Even a minor typing error can lead to compliance issues or customer escalations.

Another common issue is the last-minute document scramble before dispatch. Production may finish on schedule, but shipments get delayed because COAs are still being compiled, matched with the correct MTRs, or double-checked for accuracy. For companies operating on tight delivery windows—especially those supplying aerospace or automotive customers—documentation delays quickly become a major operational risk.

How Automation Brings the Process Under Control

Automation platforms designed for industrial documentation offer a structured way to simplify this dual-document workflow. Modern solutions can read MTRs directly from PDFs, regardless of the supplier’s format, and accurately extract critical values such as chemistry, tensile strength, hardness, and heat numbers. This eliminates the need for templates, manual scanning, or repetitive data entry.

At the same time, COA generation can be streamlined by pulling inspection results directly from measurement equipment or internal databases. As soon as final testing is done, the system automatically populates the COA in the correct customer format, eliminating inconsistencies and making the document available far earlier in the dispatch cycle. The real strength of automation is the ability to match MTR and COA data in real time. Heat numbers, material grades, tolerances, and specification limits are cross-validated instantly, and any deviation is flagged for review. This ensures that non-conforming material is caught before it leaves the facility.

Automation also integrates seamlessly with ERP and quality systems. Once documents are validated, they are linked to the correct work order, stored in the system of record, and, if required, automatically shared with the customer. This end-to-end workflow significantly reduces manual handling and creates a reliable audit trail.

What Manufacturers Gain

Manufacturers adopting COA and MTR automation report substantial improvements in efficiency and compliance. Manual processing time drops sharply, freeing quality teams to focus on more value-added tasks. Errors linked to data entry or document mismatches reduce dramatically, improving customer trust and reducing the risk of returns or corrective actions. Shipment delays caused by documentation bottlenecks disappear, enabling a smoother and more predictable dispatch cycle. Perhaps most importantly, companies gain stronger traceability and easier audit readiness—two factors that have become critical in regulated industries.

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As industries that rely on MTRs and COAs evolve toward tighter specifications and faster delivery expectations, the limitations of manual document handling become more visible. Automating both documents together—not as separate workflows—creates a unified, traceable process that supports quality, compliance, and operational speed. For manufacturers working with high-performance alloys, medical-grade materials, or precision-engineered components, this integrated approach is quickly becoming essential to maintain competitiveness and reliability.

In the pharmaceutical industry, where patient safety and regulatory compliance are paramount, Certificates of Analysis (COAs) are critical. These documents verify that raw materials, intermediates, and finished products meet predefined quality and safety standards. As companies adopt automation to streamline workflows, one truth stands out: in COA automation, the most critical step is ensuring data accuracy and integrity at the point of extraction.

Why Accuracy at Extraction Matters

Pharma COAs arrive in a wide variety of formats—PDFs, scanned images, or supplier-specific templates. Each document carries crucial details: assay results, impurity levels, dissolution rates, and compliance thresholds. A single misinterpretation—for example, reading “0.02%” as “0.2%”—can cascade into flawed validations, ERP mis-entries, or incorrect regulatory filings. The consequences can be severe: compliance breaches, costly recalls, or even risks to patient health.

A 2023 Deloitte survey revealed that up to 40% of pharma firms report compliance gaps directly tied to poor data capture in quality documentation. This proves that even the most advanced validation or integration systems cannot correct errors created at the extraction stage.

Regulatory and Client Expectations

Global regulators such as the FDA (21 CFR Part 11) and EMA place strict emphasis on data integrity, requiring pharmaceutical firms to prove that their records are authentic, consistent, and accurate. Any missteps in COA accuracy can result in FDA warning letters, production halts, or import bans.

Beyond regulators, clients demand error-free data as well. In tightly interlinked supply chains, a single inaccurate COA entry can delay drug release or shake trust. According to PwC, nearly 60% of pharma executives rank error-free quality data as the top factor in sustaining supplier-client relationships.

A Real-World Example: Novartis

Novartis, one of the world’s largest pharmaceutical companies, undertook a digital quality transformation initiative to strengthen its global supply chain. By implementing AI-driven document processing for COAs, Novartis was able to reduce manual quality checks by 65% and cut down review cycle times significantly. More importantly, automated extraction ensured accurate capture of assay and impurity data across thousands of supplier COAs. This allowed faster batch release, improved regulatory audit readiness, and created a single source of truth across their ERP and LIMS platforms.

Their experience illustrates how building accuracy at the point of extraction forms the foundation for efficiency, compliance, and trust. Without that foundation, downstream automation risks collapsing like a skyscraper built on weak ground.

The Payoff: Speed, Savings, and Safety

Accurate COA automation delivers multiple benefits. It reduces manual verification time by 50–70%, freeing skilled quality teams for higher-value work. It also minimizes human error, lowering the likelihood of recalls that, according to FDA estimates, cost $20 million to $100 million per incident. McKinsey further notes that pharma quality teams spend 25–30% of their time on manual document checks—time that automation can reclaim.

Ultimately, the integrity of COA data at extraction determines whether automation is a compliance liability or a competitive advantage. For pharmaceutical companies, the future of automation is not just about digitization—it is about building a foundation of trust, accuracy, and reliability from the very first data point.