Before you sign a vendor contract, get an honest assessment from people who build these systems.

Before you sign an AI vendor contract, get an honest assessment from people who build these systems. Millennial AI evaluates AI platforms, negotiates licensing, reviews compliance, and tells you when to build instead of buy.

The Problem

Vendor selection is where AI budgets quietly die or quietly compound.

The demo is not the product: Every vendor demo runs in a controlled environment with pre-loaded data, tuned parameters, and a prepared dataset. What you don't see: performance on your data, latency at your usage volume, edge case behaviour on actual user inputs, and what the system looks like after six months of production drift. Most buyers find out mid-implementation.

Pricing models are designed to be opaque: Usage-based pricing, token-based billing, API tier restrictions, overage fees, minimum seat commitments. They look simple in a sales conversation and surface complexity later. Companies routinely sign AI contracts at a projected annual cost, then land 2-3x over it once actual usage patterns take hold. The clause that governs that outcome is already in the contract you're reviewing.

Build vs buy is treated as a binary when it isn't: The actual decision is a spectrum: buy off-the-shelf, buy and configure, buy infrastructure and build the application layer, fine-tune an open-source model, or build from scratch. Each point has a different cost structure and control level. Without someone who has built at multiple points on that spectrum, you'll either over-engineer or outsource something that should be proprietary.

Compliance questions don't get answered until after signing: Data residency, model training data usage, output ownership, GDPR and DPDP implications, third-party subprocessor chains. These details turn a vendor relationship into a liability. Regulated industries like fintech, healthtech, and legal routinely discover compliance constraints after vendor selection, not before.

Our Approach

Requirements first, vendors second. We run the evaluation from your requirements outward, not from a vendor shortlist inward. You get a documented recommendation you can defend to your board or procurement team.

Phase 1 — Requirements and constraint definition (Days 1-4): Before we look at a single vendor, we map what you need. Functional requirements, technical constraints (stack, data environment, latency, integrations), commercial constraints (budget, contract length, data sovereignty), and compliance requirements. We also establish your build threshold: when building internally is the better answer. Many companies skip this and evaluate vendors against an inconsistent checklist that different stakeholders read differently. Deliverable: Requirements and constraint document with a defined evaluation framework and explicit build-vs-buy decision criteria

Phase 2 — Vendor evaluation and structured testing (Days 5-14): Each shortlisted vendor runs through the same evaluation: structured demo with our question agenda, technical deep-dive with their engineering team, contract and pricing review, reference calls at comparable scale, and where feasible, a controlled POC against your actual data. We probe for what sales cycles obscure: edge case handling, support quality, the true cost at 3x projected usage, and exit mechanics. Deliverable: Vendor evaluation scorecard with ratings on performance, integration complexity, pricing model, compliance posture, and exit flexibility

Phase 3 — Recommendation, negotiation support, and handoff (Days 15-20): Written recommendation with detailed rationale, ranked against your requirements document. If buying: negotiation guidance on specific contract terms worth pushing on, and we can join calls. If building: scope outline, infrastructure decisions, and realistic cost and timeline estimate. You leave with a documented decision, not one that gets revised the next time a vendor calls with a better deck. Deliverable: Final recommendation document, negotiation brief (if buying) or build scope outline (if building), plus a vendor evaluation summary for board or procurement review

Deliverables

Who This Is For

Right for you if: You're evaluating one or more AI vendors with contracts above $25K annually and want an independent technical and commercial assessment before committing.. You've received conflicting internal opinions on whether to build or buy an AI capability and need a structured recommendation that leadership can align on.. You're in a regulated industry (fintech, healthtech, legaltech) where data governance and contractual protections around AI systems aren't optional.. You don't have a senior technical person internally who has evaluated AI vendor contracts before and understands what pricing looks like at 3x projected usage..

Not right if: You've already signed a vendor contract and want validation. Our evaluation informs a decision, not reviews one already made.. You're evaluating a single low-cost SaaS tool with a standard monthly subscription. This engagement is for material commitments where a bad decision is expensive..

Use Cases

Fintech -- mid-market lending platform: A lending platform was evaluating three AI-powered document processing vendors to automate loan application intake. All three had given demos, submitted pricing proposals, and were pushing for a decision. The CTO was a strong engineer but had never negotiated an AI vendor contract at this scale and wasn't sure which performance claims were real. — Ran the full vendor evaluation. Built a requirements document grounded in their actual document types, volumes, and accuracy thresholds. Ran structured technical sessions with each vendor using their own loan document samples. Reviewed all three contracts and modelled actual annual cost at 1.5x, 2x, and 3x projected volume. Found the leading vendor's overage structure would have cost 2.4x the base contract at their projected Year 2 volume.. Outcome: Client selected the second-ranked vendor, which had a flat-fee structure that held up at scale. We negotiated a data portability clause and a cap on annual price increases that weren't in the original contract. The engagement paid for itself in the first renewal cycle.

B2B SaaS -- HR technology: An HR tech company was considering AI-powered candidate screening for their platform. Their product team was split between a specialist AI screening vendor and building on a foundation model API. The decision had stalled for two months because neither side had enough information to make the case. — Scoped the build-vs-buy decision with explicit criteria. Built the requirements document and identified the compliance dimension the internal debate was underweighting: AI hiring tools in several target markets face regulatory scrutiny, and the specialist vendor's compliance posture was materially different from a raw foundation model API. Evaluated two vendors and modelled the build option against a realistic team and timeline estimate.. Outcome: The company chose to build on a foundation model API with a compliance layer developed internally. The build cost was lower than either vendor option, and the proprietary compliance approach became a differentiator in enterprise sales.

Healthcare -- diagnostic chain: A diagnostics chain was being pitched by an AI radiology platform claiming large accuracy improvements over baseline reads. The platform required a 5-year contract with a large minimum commitment. The clinical team was enthusiastic. The CFO and CTO didn't know how to evaluate the claims. — Structured a technical due diligence process. Reviewed the vendor's published validation data and found that the accuracy benchmarks came from a different demographic dataset than the chain's patient population. Requested a prospective test on 500 cases from the chain's own archive. Results showed a material accuracy gap versus the vendor's headline number in the relevant diagnostic category. Also reviewed the contract and flagged the data licensing clause, which granted the vendor rights to use de-identified study data for model training without explicit opt-out provisions.. Outcome: The chain declined to sign. They later engaged a different vendor whose validation data more closely matched their patient population, on a 2-year pilot contract with an accuracy threshold clause before full rollout.

Results

What proper vendor evaluation prevents.

Fintech -- lending platform, $50M annual disbursements: 2.4x cost overrun avoided; data portability and price cap clauses secured before signing. A lending platform came to us with three vendor proposals, a two-week deadline, and an internal recommendation to sign with the highest-performing vendor in the demo. Our pricing model analysis found that the preferred vendor's overage structure would have cost $46K in Year 2 instead of the $20K base contract. That difference only emerged when we modelled actual projected volume against the contract terms rather than the vendor's sample projections. We also found that the data portability clause would have required six months' notice and a proprietary export format that no third-party tool supported. Both issues were renegotiable before signing. The client entered the vendor relationship with a flat-fee structure, a standard export format clause, and a 5% annual price increase cap. None of those terms were in the original proposal.

Frequently Asked Questions