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Case studies

How committees use PropEval AI

Real source-selection workflows, anonymized for procurement sensitivity. Each shows how AI-assisted evaluation changed the time, evidence, and auditability of the decision.

RFP-ITM-2025-014

Civilian Agency — IT Modernization BPA

A 5-member committee evaluated 11 vendor proposals for a multi-award BPA. PropEval AI cut framework setup from two weeks to two days and surfaced compliance gaps the team had missed in manual screening.

2 days
Framework ready in
11
Proposals analyzed
40%
Consensus time saved

The AI didn't make the decision for us. It gave every evaluator the same evidence base, so our consensus meeting actually focused on the two proposals that were genuinely close.

Contracting Officer, IT Acquisition Division

SOL-LOG-2025-0089

Defense Agency — Logistics Support Services

Three evaluators scored 7 proposals under a tight 30-day deadline. Divergence scoring let the chair prioritize the two criteria where evaluators split, instead of re-debating all of them.

30 days
Evaluation cycle
3
Evaluators
240+
Audit trail events

When a vendor filed an inquiry, we could point to the exact passage the score was based on. That changed the tone of the conversation.

Committee Chair, Logistics Command

RFP-PS-2024-221

State Agency — Professional Services RFP

A first-time chair used PropEval AI's framework extraction to stand up a compliant FAR Part 15 evaluation in a single afternoon. The locked framework kept all four evaluators anchored to the same criteria.

1 afternoon
Setup time
4
Committee size
0
Protests filed

I'd never chaired a source selection before. Having the framework extracted and the audit trail built in meant I could focus on the decision, not the paperwork.

Procurement Manager, State Agency

The AI edge

Why AI changes proposal evaluation

Source selection has always depended on careful human judgment. AI doesn't replace that judgment — it gives every evaluator a sharper, faster, more consistent starting point.

Consistency across proposals

AI applies your locked framework the same way to every proposal, so scores reflect the submission — not which evaluator read it first or how tired they were on page 40.

Evidence-backed scores

Every AI score comes with a citation to the exact passage in the proposal that supports it. Evaluators start from evidence, then adjust with their own expertise.

Divergence you can act on

When evaluators disagree, PropEval AI quantifies the gap so the chair spends consensus time on the criteria that matter — not re-litigating the ones everyone already agrees on.

The result: evaluators spend their time on judgment, not on page-counting. Committees reach consensus faster, with an audit trail that shows exactly where AI informed the decision and where a human overrode it.

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