AI Strategy & Transformation · Advisory
AI Vendor, Model & Platform Selection
Feature lists make every shortlisted option look capable. What separates them is the business, technical, risk, and operating constraints you named, put under representative and adverse cases.
Feature lists make every option look capable. Your constraints decide which one actually fits. We compare the shortlist against the business, technical, risk, and operating realities you named, and the recommendation keeps its evidence, exceptions, dependencies, and decision owner attached.
The recommendation arrives with its evidence, unsupported claims, dependencies, and review triggers still attached, so your owner accepts the trade-offs rather than the summary.
Some of the 500+ brands we've worked with
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Steps, gates, and who decides
How we work
Decision rules come first. Then each option runs against representative evidence, because a feature list alone has never settled a selection argument.
Set the selection boundary
The intended outcome, the responsible owner, the candidate options, and the business, technical, risk, and operating constraints that will shape the decision all get defined before any scoring.
- AI assist
- The intended outcome and stated constraints feed a first-pass criteria list, drafted by the model for a person to revise.
- Human gate
- The selection owner ties every criterion to a real use and decision before evidence collection begins. Your selection owner confirms that the criteria connect to a real use and decision before evidence collection begins.
Assemble comparable evidence
Approved materials, representative examples, current constraints, dependencies, and known exceptions come together in one place. A claim without support stays marked as exactly that.
- AI assist
- From vendor materials, the model extracts comparable fields into a side-by-side evidence table.
- Human gate
- Scoring waits until each option has enough comparable evidence and unsupported claims are marked. Which vendor claims still lack support is a judgment the selection owner confirms.
Evaluate the trade-offs
The agreed scorecard runs against representative and adverse cases. We watch where cost, portability, data, risk, or operating demands flip the ranking.
- AI assist
- The model applies the scorecard to representative cases and adverse cases, flagging ranking swings.
- Human gate
- A recommendation advances only if it survives the representative and adverse cases. The selection owner decides whether a trade-off is strong enough to change the draft ranking.
Record the selection
The recommendation, exceptions, dependencies, open evidence, owner, and next review point all go into the record before handoff.
- AI assist
- Once the options are scored, the model drafts a selection record covering exceptions and open evidence.
- Human gate
- Handoff requires explicit acceptance of the option’s trade-offs by the selection authority. The recommended option moves forward only after your selection authority accepts its trade-offs.
Named artifacts you keep
What you get
The recommendation stays reviewable because the criteria, evidence, and exceptions behind it travel with the decision.
Matrix
Weighted selection scorecard and recommendation
The agreed criteria, option scores, trade-offs, and recommendation tied to the intended outcome.
Risk register
Source pack and the claims still unsupported
Approved sources, unresolved claims, assumptions, dependencies, and evidence gaps for the candidate options.
Test evidence
Adverse-case readout and the ranking it changed
What the representative and adverse cases showed, including exceptions that change the ranking or operating fit.
Decision record
The chosen option, its conditions, and review triggers
The accepted option, conditions, remaining risks, owners, and next review point.
Scope and honest limits
When to bring us in
Several plausible options usually look interchangeable until real constraints, evidence, and operating responsibilities enter the comparison. That's the point of this work.
A good fit when
- Every vendor measures something slightly different, so the claims on three datasheets cannot be laid side by side without rebuilding them first.
- Business, technical, risk, and operating teams each argue from a different constraint, and the shortlist keeps changing depending on who is in the room.
- The choice will be questioned by procurement and again at implementation, so it needs to arrive with its evidence and weights attached.
- Criteria exist in someone's spreadsheet, but they were never tied to the intended outcome or weighted against the constraints you actually have.
- Vendor demos ran on the vendor's data, while nobody has put the candidates through a representative case or an adverse one of your own.
- Cost, portability, data terms, and operating load will decide this more than features, yet none of them appear on the comparison sheet.
- A recommendation is due, though no one has agreed who owns it, on what conditions, or what would justify reopening it.
Better handled as other work when
- You want the selection cleared by legal, procurement, or an auditor. We produce the scorecard and its evidence, and those approvals stay where they already sit.
- You want a permanent answer about which vendor is best. The recommendation holds for your constraints, your weights, and the evidence available on the review date.
- The real need is contract negotiation or the implementation itself. Selection ends at the recommendation unless that work is scoped alongside it.
If one of these is closer to your situation, start here instead: Explore AI strategy consulting
Advice from people who build
We've worked with more than 500 brands since Zeo started in 2011. The people helping you decide where AI fits, and where it doesn't yet, are senior engineers and strategists who build and operate production AI systems. The advice stays grounded in work that actually shipped.
Tools we use
Tools behind this work
Airtablejoins benchmark evidence with procurement, risk, and operating criteria
OpenRoutertests multi-provider model candidates through one consistent access layer
Braintrustruns every shortlisted model against identical tasks and rubrics
Heliconecaptures each candidate's real cost, latency, retries, and tokens
Hugging Faceadds open-weight candidates, licenses, and self-hosting trade-offs
Adjacent work
Where this leads
Advisory
AI Opportunity Portfolio Assessment
Vendor evaluations benchmark proprietary APIs and open-weight models against the concrete accuracy and latency demands of prioritized use cases.
Advisory
AI Business Case & ROI Modeling
Licensing terms, hosting fees, and token consumption pricing directly inform the total cost of ownership models.
Advisory
AI Transformation Roadmap
Platform selection establishes the technical dependencies and vendor lock-in risks managed across multi-year transformation phases.
Advisory
AI Operating Model Design
Vendor choices determine whether ongoing model operations are handled in-house or outsourced to specialized managed service providers.
Next step
Compare the options that matter
Before you decide