Stop reading 300-page RFPs to find the 12 requirements that matter
An AI agent that reads technical documents, RFPs, specifications, standards, contracts, extracts the requirements, finds the gaps and contradictions, and answers expert questions. Hours of work, minutes of agent.
Discuss a pilot →Quick facts
- Business size
- Companies that respond to RFPs, navigate technical specifications, or analyze technical contracts
- Timeline
- 4-6 weeks test, 2-4 months pilot, 4-6 months production
- Budget range
- Pilot from €35k.
- Hardware
- Cloud-based. Self-hosted LLM option for confidential documents.
- Data needed
- Sample technical documents. Examples of typical questions and required extractions.
- Evolution
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- Genesis
- Custom-built
- Product
- Commodity
New ground. The models exist but are still shaky. We work it out as we go.
What this scale means
Further right means more proven and cheaper. Further left means newer and riskier. Here is the test for each step.
- Commodity
- You could get the result yourself from a ready service, with almost no work. We rarely take these on.
- Product
- A vendor already sells this result turnkey, like shelf recognition from Trax or document reading from ABBYY. If one of them fits you, use it. You come to us when it does not: when it has to run on your own servers, cost less, or fit systems the product cannot reach.
- Custom-built
- Vendors sell only the parts. A tool like Tableau hands you charts, but the dashboards and metrics for your business still have to be built. That build is the work, and that is us.
- Genesis
- The approach exists but does not work reliably yet. You are betting on it maturing, so it costs more and carries more risk.
Expected outcomes
The Problem
Technical documents, RFPs (Request for Proposal), technical specifications, industry standards, complex contracts, are dense, often 100-500 pages, and full of requirements that aren’t always explicitly labeled. The “must have”, “should have”, “compliance” sections are scattered across the document. The internal contradictions are hidden in cross-references. The missing requirements (things the document forgot to specify but that matter) are invisible.
Experts read these documents for a living. A senior bid manager spends days on a major RFP. A compliance officer spends hours per standard. An engineer evaluating a technical specification spends the time reading, when the job needs engineering.
LLMs have changed what’s possible here. An AI agent can read the document in minutes, extract the structured requirements, surface internal contradictions, and answer expert questions about the content. The expert reviews and decides; the agent absorbs the reading.
What the Solution Does
A technical-document agent specialized for your document type.
- Ingest, upload the document (PDF, Word, etc.).
- Parse and chunk, structure-aware parsing that follows the document’s own sections.
- Extract requirements, structured table of every “must / should / compliance” requirement with source citation.
- Find gaps and contradictions, internal inconsistencies surfaced.
- Q&A, expert-level questions answered with citations.
- Compare, diff against templates, against past documents, against your offering.
Where It Fits
This makes sense if you…
- Respond to RFPs at meaningful volume
- Navigate complex technical specifications regularly
- Review long contracts as part of operations
- Have measurable expert time consumed by document reading
- Are willing to accept LLM-grade accuracy (85-95% extraction, expert review for high-stakes)
This is probably not the right time if you…
- Read documents rarely
- Need 100% legal-grade extraction, that requires human review even with the agent
- Have documents whose structure is too inconsistent for the agent to parse reliably
Business Value
Expert time recovery. Senior bid managers and engineers stop doing reading; they do analysis. Typical reduction in pre-bid analysis time: 50-75%.
Coverage improvement. Manual reading misses 10-20% of requirements in long documents. The agent extracts the full set with citations.
Contradiction detection. Cross-reference inconsistencies that humans miss get surfaced. This is the highest-value result for complex specifications.
Bid-coverage benchmarking. Compare extracted requirements against your offering’s capabilities; identify gaps before submission.
How It Works
1. Document parsing
Structure-aware parsing, distinguishes sections, sub-sections, lists, tables, references. PDF parsing handled with care (PDFs are a hostile format for ML).
2. Requirement extraction
LLM-driven extraction with structured output. Each requirement gets: type (must / should / compliance), source citation, dependencies, related requirements.
3. Cross-reference and contradiction detection
Graph-like analysis of requirement relationships. Internal contradictions (“section 3.4 requires X” vs “section 7.1 requires not-X”) get flagged.
4. Q&A interface
RAG over the parsed document plus a query understanding layer. Questions answered with citations to specific sections.
5. Comparison surfaces
Compare against templates, past documents, your capability catalog. Surface what’s familiar, what’s new, what’s missing.
Stack
LLM-backed (GPT-4 / Claude / self-hosted for confidential), structure-aware PDF / Word parsing, Datapipe for the data pipeline, vector store for RAG, custom requirement-extraction prompts per document type.
What You Need to Make This Work
Data. Sample documents per type. Examples of typical extraction needs.
Integrations. Document ingestion. Output to your CRM / proposal system.
Hardware. Cloud-based. Self-hosted LLM for confidential.
Team. Domain expert for validation. Workflow lead.
Implementation Roadmap
1. Test (4-6 weeks)
Pick one document type (RFP, spec, contract). Build the extraction pipeline. Test against historical documents with known answers. Output: working extraction and measured completeness.
2. Pilot (2-4 months)
Production deployment for one workflow. Wire up output to downstream system. Iterate on edge cases. Output: working deployment with documented business outcomes.
3. Production (4-6 months)
Expand document types. Continuous improvement. Your team owns the workflow.
Keep in Mind
- PDF parsing is hostile. Scanned PDFs, weird formatting, embedded images, all degrade. We handle common cases; extremes need additional work.
- Requirements aren’t always explicit. Some “musts” are implied by context. The agent catches most; experts catch the rest.
- High-stakes documents need human review. Legal contracts and regulatory specs: agent assists; humans decide.
- Domain customization matters. RFP agents and contract agents and spec agents are similar architectures but different prompts and extractions.
FAQ
Can this work for legal contracts?
Yes for contract analysis (summarization, requirement extraction, risk flagging). For legal review (is this contract binding / acceptable?), human review is required.
Multi-language documents?
Yes. LLMs handle multilingual content; we tune prompts per language.
How is this different from “just paste it into ChatGPT”?
ChatGPT context windows have limits; complex documents need structured parsing first. The pipeline runs parsing, then extraction, then cross-reference, then Q&A. That is more than a single LLM call.
What about confidential documents?
Self-hosted LLM deployment supported. No data leaves your infrastructure.
Ready to Discuss?
If your team spends material time reading technical documents, this pilot pays back quickly. We’ll walk through your document types and your workflow, and tell you what to expect.