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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
