NLP for RFPs: Parsing, Prompting, and Safeguarding Procurement
Turn long solicitations into actionable requirements and risk signals, using AI safely to accelerate, not to steer the acquisition.
🟤Structure the ask: extract sections (L/M/N), requirements, and constraints; build a living compliance matrix.

🟤Requirement quality: detect ambiguity, contradictions, and vendor-locked language; propose neutral rewrites.

🟤Evaluation alignment: map requirements to factors and subfactors; surface measurable acceptance criteria.

🟤Prompt frames for procurement: analysis-only prompts that avoid revealing acquisition-sensitive intent.

🟤Safeguards: redaction tokens, no-open-web defaults, and “summarize-don’t-operationalize” rules.

🟤Risk scan: identify protest risks, conflict-of-interest signals, and unverifiable claims.

🟤Records & traceability: citations to source sections; change logs for edits and clarifications.

🟤Outputs: compliance matrix, issue list with recommended fixes, and a one-page decision brief.

AI for Cost Estimation & Contract Evaluation
Use AI to structure defensible should-cost, schedule, and performance estimates, and evaluate offers against evidence, not hype.
🟤Cost and schedule drivers: labor, materials, lead times, risk buffers

🟤Offer evaluation scaffolds: factor/subfactor scorecards and rationale fields

🟤Extraction of past-performance signals from CPARs, delivery metrics, and narratives

🟤Monte-Carlo-informed risk ranges; sensitivity analysis

🟤Documentation: assumptions register, data sources, calculation worksheets

🟤Outputs: estimate package, evaluation worksheet, risk notes

Generative AI Use Without Data Leakage (e.g. prompt framing that avoids IP compromise)
Prove value before you field it: design safe micro-experiments and day-to-day prompt patterns that protect IP and sensitive data.🟤Redaction and tokenization patterns; “summarize-don’t-operationalize” rules

🟤DOE-style pilots: hypotheses, control groups, stop-rules, accessibility/508 checks

🟤Sandboxes and synthetic datasets: when to use, how to scope

🟤Metrics that matter: time-to-decision, error classes, decision quality

🟤Audit trail: experiment cards, artifacts, and approvals

🟤Outputs: experiment plan, safe-use prompt kit, results brief

Drafting Technical Requirements with GenAI Support
Draft clear, testable PWS/QASP elements and technical specs, then back them with measurable acceptance criteria..
🟤Requirement decomposition; interface assumptions; performance baselines

🟤Measurability: acceptance tests, data sources, thresholds, and evidence

🟤Bias/lock-in checks; portability and export format requirements

🟤Traceability: requirement ↔ factor ↔ verification mapping

🟤Versioning and change-control notes for post-award updates

🟤Outputs: structured requirement set, acceptance criteria table, trace matrix

Vendor Assessment with AI Risk Insight
Evaluate vendor claims and data integrity with lineage, privacy, and quality checks suitable for federal contexts.
🟤Data types and pipelines (structured, document intelligence, imagery, logs)

🟤Provenance and quality: labeling, lineage, confidence annotations

🟤Privacy posture: handling PII/PHI/CUI; workflow access controls

🟤Model claims review: reproducibility signs, evaluation artifacts, maintenance plans

🟤Vendor-performance prediction: simple indicators and watch-items

🟤Outputs: vendor risk profile, data quality checklist, assessment memo