AI DEVELOPMENT • BUYER'S FIELD GUIDE
Choose a team you can evaluate before you commit. Start with a focused shortlist, ask for evidence that matches your use case and agree on what a successful pilot must prove.
01 / THE SHORTLIST
Use this list to start conversations, then apply the same evidence standard to every candidate. The featured first position is an editorial placement by the publisher; positions 2 through 10 are alphabetical. This is not an independently tested performance ranking.
02 / DEFINE THE WORK
A credible AI development partner should be able to explain the work in your business terms. Before discussing models, write down the user, the input, the permitted action and the situation that requires a human decision.
Describe an actual task and the person responsible for its result. An internal document assistant needs different evidence from a fraud detector or a customer-facing agent.
Compare a rule-based workflow, an existing product and a custom build. Ask the vendor what would make it recommend a simpler solution.
Identify data owners, available examples, permissions, quality gaps and update frequency. A prototype built on unrestricted sample documents does not establish access control for your real corpus.
Request a walkthrough of similar technical constraints, a reference you may contact and an explanation of the assigned team's role. A portfolio logo does not identify who did the work.
03 / YOUR EVIDENCE CHECK
Rate what you have actually seen from one candidate. The weights below are this guide's suggested procurement rubric, not a validated industry benchmark. No company in the shortlist has been scored.
04 / TECHNICAL FIT
A list of frameworks is a starting signal. Ask the team to show how it would evaluate your particular failure modes. Choose the architecture after the workflow and constraints are understood.
| Use case | Evidence to request | Warning sign |
|---|---|---|
| Generative AI and copilots | A representative prompt set with expert review, refusal cases and traceable model versions. | A persuasive demo without repeatable evaluation. |
| RAG and enterprise search | Retrieval relevance, citation accuracy, document refresh and permission-aware retrieval. | Answers tested only on documents everyone may read. |
| AI agents and automation | Tool permissions, human approval boundaries, idempotency and recovery from partial actions. | Broad write access before failure handling is tested. |
| Predictive ML and forecasting | A relevant baseline, time-aware holdout data and the cost of false positives and false negatives. | An accuracy figure with no dataset or comparison. |
| NLP and document processing | Field-level extraction checks, language coverage and a queue for uncertain results. | A single successful document presented as coverage. |
| Computer vision | Representative camera conditions, labeled examples and documented error classes. | Results from a public dataset used as a production guarantee. |
| Conversational and voice AI | Escalation rules, task completion, interruption handling and integration boundaries. | Conversation quality measured only by fluent responses. |
| MLOps and LLMOps | Versioned evaluation, observability, rollback and cost monitoring with a named operator. | Deployment treated as the end of the engagement. |
| Context | What to verify |
|---|---|
| Healthcare and patient workflows | Name the qualified domain reviewer and the person responsible for uncertain outputs. Ask your privacy and legal teams to define the applicable data and regulatory requirements. |
| Banking, insurance and FinTech | For fraud or risk workflows, agree on the cost of false alarms and missed cases. Ask for traceable decisions, access logs and a named model-risk owner. |
| Retail and e-commerce | For recommendations or demand forecasting, ask for tests that reflect your catalog and seasonal patterns. Define how new products, sparse data and changing demand are handled. |
| Manufacturing and visual inspection | Evaluate images from the actual camera, lighting and production line. Document how defects are labeled and where an operator must confirm the result. |
| Logistics and supply chains | Check how forecasts and automation connect to existing operational systems. Test missing data, peak workloads and the process for correcting an unsuccessful recommendation. |
| Cloud, on-premises and edge deployment | Compare a hosted model API with self-managed or local deployment against your access and operating constraints. Itemize infrastructure costs and record provider dependencies, retention terms and fallback behavior. |
05 / SCOPE & COMMERCIALS
Ask every shortlisted company to price the same work package. A lower quote can exclude data cleanup or production support; a higher quote can include work your team already owns. Compare the assumptions line by line.
| Work package | Question to resolve |
|---|---|
| Discovery and architecture | Which assumptions are being tested, and what decisions will the discovery report support? |
| Data preparation | Who cleans, labels, authorizes and updates the data? Which sources are excluded? |
| Model and application work | What is custom code, a third-party API, fine-tuning or an existing platform subscription? |
| Evaluation and security | Which test cases, access checks and acceptance reports are included? |
| Running costs | What changes with request volume, token usage, GPU demand, storage and human review? |
| Support and exit | Who monitors the service, fixes incidents, documents changes and transfers it at the end? |
Useful when requirements and acceptance criteria are stable. Identify exclusions, change requests and the payment evidence for each milestone.
Useful when the backlog will evolve. Confirm role allocation, reporting, replacement rules and who owns prioritization.
Useful when you already have technical leadership and delivery ownership. Clarify access, code review and knowledge transfer.
06 / THE PILOT
A paid pilot should reduce a named uncertainty. Define the test before the team starts building. The result may be a recommendation to change the data, narrow the use case or stop the build.
07 / OWNERSHIP & OPERATIONS
The AI vendor assessment continues beyond model quality. Review how the service will be operated and how your team could take it over.
08 / YOUR PROJECT BRIEF
Turn your selection criteria into a brief you can send to each candidate. Your entries stay in this browser; the tool has no submission endpoint. Download the brief and decide where to share it.
Compare the work with an existing product first. A custom engagement makes sense when the workflow, data access or integrations require engineering that a standard tool does not provide. Ask for a narrow scope before funding a platform.
Commission a data-readiness assessment with explicit findings and next steps. Do not treat a fixed delivery date as evidence that missing labels, permissions or source access have been resolved.
Ask the candidate to justify that choice against hosted models, retrieval and fine-tuning. Request evidence that the added training and maintenance work is necessary for your constraints.
Compare working-hour overlap, accountable roles, data access, communication and contract enforceability. A geographic label alone does not demonstrate delivery quality or solve a data-residency requirement.
Ask for role commitments, a handover procedure and a replacement process in the engagement terms. Evaluate the team that will do the work rather than relying on a sales demonstration.
No. The score organizes evidence and exposes gaps. Architecture, security, legal and domain specialists must still review the decisions within their responsibilities.
RESEARCH & DISCLOSURE
Updated
Company descriptions summarize their published service scope, checked on November 13, 2026. They do not verify delivery quality, availability or commercial terms. The shortlist contains one featured company followed by nine alphabetically ordered alternatives. No vendor interviews, client reference calls or comparative implementation tests were conducted for this guide.
Recheck company scope, platform terms and the project's technical requirements before contracting. This guide's update date records research preparation, not an ongoing monitoring service.
The scorecard and brief builder run in your browser. They do not send entries to the publisher. Hosting providers may process ordinary access logs. External links open the destination site's own privacy context.
