AI Technology Selection That Ends in a Stack You Can Build On
Model, orchestration, infrastructure, and cost profile chosen as one decision, evaluated on your real data and workload.
Most selection advice stops at the shortlist. We run the candidates on your real workload, score them against your constraints, and hand you a stack your team can build on, with a portability ramp designed in from the start. Our expert team made these same calls for PASSR, DOCKR, and TESTR, and for a banking support agent handling roughly 500,000 conversations a month inside a PCI-DSS environment.
Why Technology Choices Fail After the Contract
Most AI stacks are chosen in a demo room and regretted in production. These are the four failure modes that recur across selections.
Demos Are Theater
Vendor demos run on curated data and rehearsed flows, and the demo tells you how the tool handles their data. The candidate that wins the room loses in production. Candidates should be scored on your workload before a contract exists.
TCO Gets Counted Wrong
Buy underestimates integration engineering and the year-three renewal escalation. Build underestimates maintenance, drift management, and the engineers who leave. A license quote and a build estimate are halves of one model, and the model counts both.
Leaderboards Pick the Model
Public benchmark tables answer questions you did not ask. Your constraints are latency per call, cost at your volume, data residency, and how the model handles your documents, and a public benchmark chart answers none of them.
Lock-In Shows Up at the Exit
The switching cost of a stack stays invisible during the honeymoon and becomes brutally clear at renewal. Portability belongs in the selection criteria and in the contract terms, decided before the first invoice, not in a post-mortem.
Three Verdicts, Scored Per Workflow
The verdict lands per workflow. The same enterprise rents its commodity workflows and owns its edge.
The workflow is your edge, the data is regulated, or no candidate survives the evaluation. You own the model layer, the evals, and the runbooks.
A partner or platform ships the first version and your team owns the run. The most common verdict for teams with real deadlines and finite engineers.
The workflow is a commodity, compliance is standard, and you need it next quarter. Rent it, with exit terms and data portability negotiated up front.
What Ships With the Engagement
Everything ships as a named artifact your team owns. Every item lands in your repositories and decision documents.
The selection package
- Requirements and constraints capture: data, latency, compliance, and team capacity
- Weighted selection scorecard per workflow, agreed with your stakeholders
- Candidate evaluations run on your real documents and workload (Evaluated track)
- Model shortlist with eval scores and measured cost-per-call profiles (Evaluated track)
- Orchestration comparison: LangChain, LangGraph, crewAI, or custom, matched to your team
- Infrastructure recommendation across AWS, GCP, and Azure, with residency notes
- Three-year TCO model per candidate, counting integration and exit costs
- Build, blend, or buy verdict per workflow, with the reasoning attached
- Portability design: an abstraction layer so switching models is a config change
- Contract red flags for counsel: lock-in clauses, data terms, renewal escalators
Two Tracks, Scoped by a Feasibility Study*
The Scan answers the question on market and benchmark evidence. The Evaluated Selection proves the answer on your own workload. The feasibility study determines which track fits your data and your deadline.
Feasibility Study
Maps where selection sits inside your broader initiative, which workflows need the verdict, and whether your data can support real candidate evals. Priced and scheduled separately.
Selection Scan
Requirements capture, market scan and shortlist, benchmark-informed scoring, three-year TCO per candidate, a build, blend, or buy verdict per workflow, plus portability design and contract red flags.
Evaluated Selection
Everything in the Scan, plus a golden set from your workflow's real inputs and known-good outcomes, candidates scored against it, and measured latency and cost-per-call on your volumes.
*Duration and effort depend on the number of workflows and candidates in scope and the customization your environment needs beyond the baseline evaluation, confirmed in the feasibility study.
Advisors Who Score Demos, and Engineers Who Run the Stacks
The market splits between firms that evaluate vendors and content that lists criteria. We run the candidates and operate what we recommend.
Vendors and packaged platforms.
Decision criteria and scorecards.
The full stack: model, orchestration, infrastructure, and cost profile as one decision.
References, RFP responses, and demo scoring.
Public benchmarks and generic frameworks.
Candidates run on your data, by engineers who operate these stacks in production.
Contract clauses and renewal terms.
Rarely covered.
Portability designed into the architecture, so switching a model is a config change.
GenAI-era procurement applied to agents.
Generic decision matrices.
Governance fit scored: whether the stack can carry policy-as-code, scoped credentials, and audit trails.
Contract negotiation support.
The guide ends.
We can build and instrument what we recommended, or hand it to your team with runbooks.
| Dimension | Procurement Advisors | Framework-Content Shops | FLYTEBIT Selection Practice |
|---|---|---|---|
| What gets evaluated | Vendors and packaged platforms. | Decision criteria and scorecards. | The full stack: model, orchestration, infrastructure, and cost profile as one decision. |
| Evidence base | References, RFP responses, and demo scoring. | Public benchmarks and generic frameworks. | Candidates run on your data, by engineers who operate these stacks in production. |
| Exit thinking | Contract clauses and renewal terms. | Rarely covered. | Portability designed into the architecture, so switching a model is a config change. |
| Agentic depth | GenAI-era procurement applied to agents. | Generic decision matrices. | Governance fit scored: whether the stack can carry policy-as-code, scoped credentials, and audit trails. |
| After the verdict | Contract negotiation support. | The guide ends. | We can build and instrument what we recommended, or hand it to your team with runbooks. |
Match the Tool to the Question
A selection engagement answers which stack and which sourcing model fits your workflows. If that is not your question, one of these fits better.
Frequently Asked Questions
What is AI technology selection consulting?
AI technology selection consulting chooses the model, orchestration framework, infrastructure, and commercial shape of an AI initiative as one decision. Requirements are captured as weighted criteria, candidates are evaluated on your real data and workload, and each workflow ends in a build, blend, or buy verdict with a three-year cost model and a portability design. Our version is engineering-led, so the evaluation is run by the same people who operate these stacks in production.
Is there a right answer to build vs buy?
For most workflows the answer is clear once the criteria are weighted without a preferred outcome, and the remaining cases are genuinely situational. Each workflow gets its own verdict: commodity workflows with standard compliance are rented, workflows that carry your edge are owned, and many land in the blend, where a partner ships the first version and your team owns the run. A weighted scorecard on your data turns the situational cases into defensible ones.
How do you evaluate candidate technologies?
We capture your requirements and constraints as weighted selection criteria, then run candidates against your real documents and workload. Latency and cost-per-call are measured on your volumes, not quoted from vendor materials, and every candidate gets a three-year total-cost model that counts integration engineering and exit costs alongside license fees. The verdict comes with the evidence attached.
Will we be locked into whatever we pick?
Portability is a scored criterion in our framework, and the recommendation ships with an abstraction design that keeps switching costs low: a model swap becomes a config update. We also flag contract terms for your counsel before you sign, including lock-in clauses, data-portability terms, and renewal escalators.
We don't have an AI system yet. Can candidates still be evaluated on our data?
Yes. Your workload is the business workflow's real data: the documents, tickets, and records the AI will process. The golden set pairs samples of those inputs with known-good outcomes, so candidates are scored on the job they will actually do. An existing AI system is not required. Where no usable samples exist at all, the feasibility study flags the data gap and the Selection Scan is the right track.
Do you implement what you recommend?
If you need us to. We build and operate our own AI products and take implementation engagements, so the team that recommends the stack can also stand it up. When your own engineers take over instead, the handover includes the evaluation evidence, the architecture sketch, and the runbooks they need.
What does a technology selection engagement cost and how long does it take?
Every engagement is scoped through a feasibility study first, which starts from $2K, so the workflows, candidates, and effort are priced before a larger commitment. The Selection Scan starts from $8K and runs two to three weeks. The Evaluated Selection adds candidate evaluations on your real workload and typically runs four to six weeks, scoped to the number of workflows in play. Once scoped, the fee is fixed and confirmed before kickoff.
Pick the Stack on Evidence, Not on Demos
Schedule a 30-minute working session with our expert team. We will review the workflows you are deciding on, identify which constraints drive the verdict, and give you a straight answer on whether a selection engagement is the right next step.