Teams don’t lack knowledge — they lack accessible knowledge. The best timelines, staffing plans, and technical approaches often live in past proposals, trapped in PDFs.
This post summarizes the approach we use in BidGenie to extract reusable Q&A and content blocks, route the right context based on question type, and apply quality gates so generated answers are grounded in real evidence — not generic filler.
Extraction job
Find the answer candidate, proof context, and constraints hiding inside long proposal documents.
Routing job
Match the extracted asset to the new question without assuming every prompt needs the same context.
Quality job
Stop low-signal or unverifiable content before it becomes part of the next draft cycle.
Smart Q&A Extraction
We classify extracted questions into a small set of high-value types. That helps us extract structured “building blocks” like timelines and staffing plans — the details evaluators look for.
| Type | What it captures |
|---|---|
| Timeline | Schedules, milestones, durations |
| Staffing | Team composition, roles, resourcing |
| Technical | Architecture, approach, tech stack |
| Past Performance | Case studies, outcomes, metrics |
| Qualifications | Certifications, licenses, expertise |
| Compliance | Security controls, regulatory alignment |
| Pricing | Cost model language, value framing |
Intelligent Context Routing
Retrieval isn’t one-size-fits-all. A question asking for a 12-month implementation schedule should “cast a wider net” than a general marketing question. We use adaptive similarity thresholds by type so high-stakes categories pull in more candidate context.
Timeline questions → 0.25 threshold (wider net)
Staffing questions → 0.25 threshold (wider net)
Technical questions → 0.35 threshold
General questions → 0.40 thresholdQuality Validation Before Reuse
Extraction is only useful if the extracted content is actually reusable. We score candidate Q&A across multiple dimensions and flag low-quality items for review:
Relevance
Looks like a real RFP question (not boilerplate text).
Completeness
Substantive answer length with enough detail to reuse.
Specificity
Prefers metrics, dates, named tools, and concrete steps.
Actionability
Avoids vague “world-class” filler; favors real commitments.
What this improves (in practice)
The goal is simple: when a question demands specificity, the draft should include specifics — because the system retrieved them and the prompt requires using them.
- Timeline answers should include phases and milestones (not generic “agile” language).
- Staffing answers should reference roles and responsibilities (not placeholders).
- Compliance answers should cite controls, standards, and artifacts.
Next steps
We’re continuing to improve extraction by prioritizing proven content, enhancing vertical-specific rules, and making it easier for teams to refine extracted knowledge collaboratively.