A month of manual document review, compressed to minutes.
A large government contractor delivering complex technical solutions was reviewing dense RFP and proposal documents — often hundreds of pages each — to extract requirements and assemble them into structured project plans. The process took multiple people over a month per cycle, and accuracy still suffered because the volume exceeded what any team could reliably hold in working memory. We replaced the manual review with an agentic system that does the first pass in minutes, with extraction accuracy upwards of 99% on verified samples.
The same pattern shows up in construction bidding, complex services proposals, and any submission or RFP workflow with dense source documents.
Requirement extraction was the bottleneck — and the source of risk.
Volume the team could not absorb
Each proposal document ran into the hundreds of pages. Requirements were scattered across narrative sections, appendices, technical specifications, and footnotes. No reviewer could hold the full document in working memory, which meant requirements were missed, mis-categorized, or duplicated.
A month per cycle, every cycle
Multiple staff worked the document review for weeks at a time. The cost was significant — but the worse cost was the delay. By the time the project plan was assembled, the response window was nearly closed and the planning team had no time to make judgment calls.
Accuracy issues that compounded
Every missed or mis-extracted requirement showed up later as scope creep, rework, or contractual exposure. The downstream cost of a 90%-accurate extraction was far higher than the extraction itself.
An agentic system that reads the way the team reads — only faster.
Multi-pass document understanding
Agents process the full document in coordinated passes — structural parsing, semantic extraction, cross-reference resolution, requirement classification. Each pass narrows and verifies the prior one rather than trying to do everything at once.
Structured output, ready for planning
Extracted requirements flow into a structured project plan with traceability back to the source document, page, and section. The planning team starts with a complete, sourced inventory of requirements instead of a blank page — and can interrogate the document directly, asking questions and getting answers tied to their source sections.
Verification and audit trail
Every extraction is auditable. Confidence scoring flags the small percentage of items that benefit from human review. The system tells you what it knows, what it inferred, and what it could not resolve.
Minutes instead of a month. 99%+ accuracy instead of best-effort.
The team that previously spent a month on each document now spends minutes reviewing agent output and adjudicating flagged items. On samples checked against human-verified baselines, requirement extraction accuracy is consistently above 99%. The downstream effects matter more than the speed: project plans are built from complete requirement inventories, scope is locked earlier, and the planning team is spending its time on judgment rather than on reading.
- First-pass processing time reduced from a month to minutes
- Requirement extraction accuracy at 99%+ on human-verified samples
- Full source traceability — every requirement links back to its origin
- Confidence scoring on every extraction, surfacing only the items that need human review
- Planning capacity redirected from reading to judgment
Agents structured around the work, not a general-purpose chatbot.
A single LLM call against a 400-page document does not produce 99% accuracy. A coordinated agent workflow — with verification, cross-reference resolution, and confidence scoring — does. The architecture matched the actual cognitive work of the task. That is the difference between a demo and a production system, and it is why the accuracy held under real document volume.
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Tell us about the documents your team is reading.
If your team is extracting requirements, terms, or specifications from long documents — and accuracy or cycle time is hurting downstream work — we can scope what an agentic system would look like for your workflow.