Key Takeaway
Always require a paid proof-of-concept on your own data before signing an annual contract -- vendor demos on curated datasets are unreliable indicators of production performance.
Why AI Procurement Is Different
Evaluating AI vendors is fundamentally different from traditional software procurement. Model performance degrades over time as data distributions shift. Pricing models are usage-based and can surprise you at scale. Lock-in risks are amplified by proprietary fine-tuning, data formats, and prompt engineering investments. And the vendor landscape changes quarterly as new entrants emerge and existing players pivot.
This kit provides a structured evaluation process that accounts for these AI-specific risks while maintaining procurement velocity. It is designed to be handed to your procurement team, your technical evaluation committee, and your legal team so everyone is working from the same framework.
The Four-Stage Evaluation Funnel
The evaluation follows a progressive funnel that narrows the field at each stage while increasing evaluation depth. This prevents the common failure mode of spending weeks on deep technical evaluations of vendors that fail basic screening criteria.
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Stage 1: Initial Screening (10 vendors to 5) -- 1 week
Apply must-have criteria to eliminate vendors that do not meet basic requirements. Screen on: deployment model compatibility (cloud/on-prem/hybrid), data residency and compliance certifications, pricing model viability at your projected scale, company viability indicators (funding, revenue, customer count), and basic integration compatibility with your stack.
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Stage 2: Technical Deep Dive (5 to 3) -- 2 weeks
Conduct structured technical evaluations including architecture reviews, API documentation quality assessment, latency and throughput benchmarks, security posture review, and reference customer interviews. Each vendor completes a standardized technical questionnaire.
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Stage 3: Proof-of-Concept (3 to 1) -- 3-4 weeks
Run a paid POC on your own data and use cases. Define success criteria before the POC begins. Evaluate model quality, integration effort, operational overhead, and support responsiveness. The POC should simulate production conditions as closely as possible.
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Stage 4: Contract Negotiation -- 2 weeks
Negotiate terms that protect against AI-specific risks: performance SLAs with measurable quality metrics, data ownership and portability clauses, price caps or committed-use discounts, model deprecation notification requirements, and exit terms that include data extraction.
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