AI vendors are very good at selling. They're less good at disclosing what breaks at scale.
Millennial AI's vendor selection engagement gives you an independent evaluation of AI platforms, licensing structures, and build-vs-buy tradeoffs before you've locked yourself in. We've built these systems. We know what the demos don't show.
Vendor selection is where AI budgets quietly die, or quietly compound.
The demo is not the product
Every AI vendor demo is a controlled environment with pre-loaded data, tuned parameters, and a prepared dataset that makes the model look clean. What you're not seeing: performance on your data, latency at your usage volume, edge case behaviour on the inputs your users will actually send, and what the system looks like after six months of production drift. Most buyers don't find out until they're already mid-implementation.
Pricing models are designed to be opaque
Usage-based pricing, token-based billing, API tier restrictions, overage fees, minimum seat commitments. They're structured to 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 real 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 on that spectrum has a different cost structure, control level, time-to-value, and risk profile. Without someone who has built at multiple points on it, you'll either over-engineer a problem a vendor solves cheaply, 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 are the details that turn an AI vendor relationship into a liability. Regulated industries (fintech, healthtech, legal) routinely discover compliance constraints after vendor selection, not before.
Requirements first, vendors second.
We run the evaluation from your requirements outward, not from a vendor shortlist inward. The output is a documented recommendation you can defend to your board or procurement team.
Requirements & Constraint Definition
Days 1-4
Before we look at a single vendor, we map what you actually need. Functional requirements (what the system must do). Technical constraints (your existing stack, data environment, latency requirements, integration points). Commercial constraints (budget envelope, contract length tolerance, data sovereignty requirements). Compliance requirements (applicable regulations, internal data governance policies, audit trail needs). We also establish your build threshold: the conditions under which building internally becomes the better answer. Most companies skip this step and end up evaluating vendors against an implicit, inconsistent checklist that different stakeholders interpret differently.
Deliverable: Requirements and constraint document with a defined evaluation framework and explicit build-vs-buy decision criteria
Vendor Evaluation & Structured Testing
Days 5-14
We run each shortlisted vendor through the same evaluation structure. A structured demo with our own question agenda (not theirs). A technical deep-dive session with their engineering team. A contract and pricing model review. Reference calls with current customers at comparable scale. Where feasible, a controlled proof-of-concept test against a representative sample of your actual data. We probe specifically for the things vendor sales cycles are designed to obscure: edge case handling, support quality for non-enterprise customers, the real cost model at 3x projected usage, and the contractual mechanics for exiting the relationship.
Deliverable: Vendor evaluation scorecard with ratings across performance, integration complexity, pricing model, compliance posture, and exit flexibility
Recommendation, Negotiation Support & Handoff
Days 15-20
We deliver a written recommendation with a clear rationale, ranked against your requirements document. If the recommendation is to buy, we provide negotiation guidance on the specific contract terms worth pushing on. We can join negotiation calls with your commercial team. If the recommendation is to build, we outline the build scope, the infrastructure decisions it requires, and a realistic cost and timeline estimate. You leave the engagement with a decision that's documented and defensible, not subject to revision 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 suitable for board or procurement review
Requirements & Constraint Definition
Days 1-4
Before we look at a single vendor, we map what you actually need. Functional requirements (what the system must do). Technical constraints (your existing stack, data environment, latency requirements, integration points). Commercial constraints (budget envelope, contract length tolerance, data sovereignty requirements). Compliance requirements (applicable regulations, internal data governance policies, audit trail needs). We also establish your build threshold: the conditions under which building internally becomes the better answer. Most companies skip this step and end up evaluating vendors against an implicit, inconsistent checklist that different stakeholders interpret differently.
Deliverable: Requirements and constraint document with a defined evaluation framework and explicit build-vs-buy decision criteria
Vendor Evaluation & Structured Testing
Days 5-14
We run each shortlisted vendor through the same evaluation structure. A structured demo with our own question agenda (not theirs). A technical deep-dive session with their engineering team. A contract and pricing model review. Reference calls with current customers at comparable scale. Where feasible, a controlled proof-of-concept test against a representative sample of your actual data. We probe specifically for the things vendor sales cycles are designed to obscure: edge case handling, support quality for non-enterprise customers, the real cost model at 3x projected usage, and the contractual mechanics for exiting the relationship.
Deliverable: Vendor evaluation scorecard with ratings across performance, integration complexity, pricing model, compliance posture, and exit flexibility
Recommendation, Negotiation Support & Handoff
Days 15-20
We deliver a written recommendation with a clear rationale, ranked against your requirements document. If the recommendation is to buy, we provide negotiation guidance on the specific contract terms worth pushing on. We can join negotiation calls with your commercial team. If the recommendation is to build, we outline the build scope, the infrastructure decisions it requires, and a realistic cost and timeline estimate. You leave the engagement with a decision that's documented and defensible, not subject to revision 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 suitable for board or procurement review
A decision you can defend.
Discovery & Scoping (Days 1-4)
- Requirements and constraint document covering functional, technical, commercial, and compliance dimensions
- Evaluation framework with explicit scoring criteria and build-vs-buy decision thresholds
Evaluation (Days 5-14)
- Vendor evaluation scorecard for each assessed platform
- Contract and pricing model analysis with flagged terms and financial risk modelling
- Compliance posture assessment covering data residency, training data usage, output ownership, and applicable regulatory requirements
- Reference call summary from existing customers at comparable scale
Recommendation & Handoff (Days 15-20)
- Final recommendation with ranked options and documented rationale
- Negotiation brief identifying the contract terms with the strongest negotiating power and recommended positions
- Build scope outline if the recommendation is to build (including infrastructure requirements, team profile, and cost/timeline estimate)
- Executive summary suitable for board or procurement committee review
What this engagement covers and where it stops.
The vendor selection engagement ends at a recommendation and negotiation support. It doesn't include implementation or ongoing vendor management.
Implementing the chosen solution
Vendor selection and technical implementation are separate engagements. Once you've selected a vendor or decided to build, the actual integration, configuration, and deployment work is scoped as a separate project.
Ongoing vendor management or contract administration
You enter the vendor relationship with the right terms, protections, and pricing structure. Managing the relationship over time (usage tracking, renewal negotiations, issue escalation) is your team's responsibility after the engagement closes.
Legal review or contract execution
We review contracts for technical and commercial risk and flag terms worth negotiating. We're not legal counsel and we don't provide legal sign-off. Your legal team should review final contract language before execution.
Is this the right fit?
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, documented recommendation that leadership can align on.
- You're in a regulated industry (fintech, healthtech, legaltech) where data governance, compliance, 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 the pricing model looks like at 3x projected usage.
Not right if
- You've already signed a vendor contract and are looking for validation. Our evaluation is designed to inform a decision, not to review one that's already been made.
- You're evaluating a single low-cost SaaS tool with a standard monthly subscription. This engagement is designed for material commitments where the cost of a bad decision is significant.
What this looks like in practice.
Problem
A lending platform was evaluating three AI-powered document processing vendors to automate their loan application intake. The three vendors had given demos, submitted pricing proposals, and were actively 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 meaningful and which were demo-polished.
What we did
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 that the leading vendor's pricing model had an overage structure that 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.
Problem
An HR tech company was considering adding AI-powered candidate screening to their platform. Their product team was split between buying a specialist AI screening vendor and building on top of a foundation model API. The decision had been on hold for two months because neither side had enough information to make the case convincingly.
What we did
Scoped the build-vs-buy decision with explicit criteria. Built the requirements document and identified the compliance dimension that was being underweighted in the internal debate: 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 conversations.
Problem
A diagnostics chain was being pitched by an AI radiology platform that claimed significant accuracy improvements over baseline reads. The platform required a 5-year contract with a significant minimum commitment. The clinical team was enthusiastic. The CFO and CTO were not sure how to evaluate the claims.
What we did
Structured a technical due diligence process. Reviewed the vendor's published validation data and identified that the accuracy benchmarks were drawn 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 subsequently 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.