Vendor evaluations are supposed to be objective, but they rarely are. Different evaluators weight criteria differently, proposals vary in format and emphasis, and the final decision often comes down to whoever presented best rather than who best meets the requirements.

The Problem

Evaluating three to five vendor proposals against a 20-item criteria list requires each evaluator to read hundreds of pages and score consistently. Evaluators anchor on the first proposal they read, score fatigue sets in by the third, and formatting differences between proposals make apples-to-apples comparison difficult.

The AI Approach

An LLM can read each vendor proposal and score it against your evaluation criteria in a consistent, format-independent way. The model extracts relevant information from wherever it appears in the proposal and maps it to your scoring framework.

Three-Step Build

Step 1 - Criteria framework. Define your evaluation criteria with clear scoring rubrics and weights. Example: “technical capability” (30%), “pricing” (25%), “implementation timeline” (20%), “support model” (15%), “references” (10%).

Step 2 - Proposal scoring. For each vendor proposal, send it to the LLM with the criteria framework. The model returns a per-criterion score with evidence citations from the proposal and notes on gaps where the proposal does not address a criterion.

Step 3 - Comparison report. Generate a side-by-side comparison showing scores by criterion, overall weighted scores, and a summary of each vendor’s strengths and weaknesses.

Where It Breaks

The model scores what is written, not what is true. Vendors that write better proposals score higher even if their actual capabilities are weaker. Pricing comparisons require financial modeling beyond what an LLM can do accurately. Relationship factors and cultural fit are not captured in written proposals.

The Production Path

Use AI scoring as a starting point for evaluation discussions, not as the final decision. Add a verification step where high-scoring claims are validated through reference checks and demos. Track actual vendor performance post-selection to calibrate scoring criteria over time.