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Choose an AI development company by testing its proposal against a measurable business outcome—not by judging a polished demo or broad AI claims. Before you hire, verify the team’s relevant experience, evaluation plan, data and security practices, integration approach, post-launch responsibilities, and contract terms. Compare candidates against the same criteria for your use case.
Start with the business problem, not the model
Ask each company to describe the problem it believes you want to solve, who will use the proposed system, and what result should improve. Then ask how that result will be measured and what assumptions the proposal relies on.
AI may not be the right solution. Ask what a simpler non-AI approach would look like and why the proposed AI system is preferable. A sound decision considers business value, user need, technical feasibility, risk, and the changes required in your organization. Microsoft’s AI use-case evaluation guidance frames assessment around business, experience, and technology, and treats implementation and maintenance as continuing work rather than ending at launch.
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Request the names or roles of the people who would do the work, their relevant technical and domain expertise, a proposed architecture, and a delivery plan. Ask for comparable project examples, then establish exactly what each example demonstrates and how its constraints differed from yours.
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A case study or credential is evidence to examine, not proof that a company can deliver your particular system. NIST’s AI Procurement in a Box: Workbook and Guidelines for AI Procurement include supplier questions about specialist expertise, implementation evidence, interpreting outputs, security, and testing.
Define what success and acceptance mean
Agree on acceptance criteria before work begins, including for any pilot. Specify what the system must do, how results will be evaluated, what evidence the vendor will provide, and what happens if it misses the criteria.
Ask the company to explain its evaluation method and data. Tests should reflect the intended use, expected users, and operating conditions—not only a favorable demonstration. Where relevant, cover:
- Performance and quality measures tied to the business outcome.
- Failure conditions, edge cases, and how incorrect or uncertain outputs are handled.
- Bias or unequal outcomes that could affect users or decisions.
- Human review: when it is required, who provides it, and how reviewers can intervene.
- How test results, limitations, and changes will be documented.
If you are hiring a company specifically for AI red teaming, ask how it defines the threat model, conducts testing, uses tools, handles governance, and substantiates reported findings. OWASP’s 2026 vendor evaluation criteria for AI red-teaming providers and tooling provides a framework for examining those areas; a handful of jailbreak screenshots alone do not establish a rigorous assessment.
Trace data, models, and other dependencies
Ask for a data-flow explanation: what information the system uses, what leaves your environment, which companies receive it, who can access it, how it is protected, and whether prompts or outputs are retained or used for training. Confirm how deletion works and whether you can access the records needed for monitoring and future maintenance.
Request a list of the proposed data sources, models, APIs, software libraries, cloud services, and other vendors. Clarify who owns and can use input data, training data, processed or enriched data, code, configurations, and outputs. NIST’s procurement workbook addresses data access and ownership; AWS advises buyers to review model-provider terms and data-use policies, while Microsoft flags risks involving external data, models, libraries, APIs, and integrations.
Check security and operational readiness
Ask the company how authentication and authorization work, how it limits access to what each person or service needs, and how it isolates environments and user sessions. Discuss vulnerability handling, incident response, logging, and monitoring. Have the vendor identify which controls it owns and which are your organization’s or cloud provider’s responsibility.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsAWS’s security-readiness checklist covers governance, provider terms, access controls, input and output risks, data protection, perimeter security, monitoring, and incident response. Also ask how integrations and dependencies could fail: Microsoft’s AI governance guidance highlights issues such as dependency cascades, incompatible data, performance bottlenecks, and security gaps.
Plan for integration, launch, and ongoing support
A prototype is not necessarily a product your organization can operate. Ask who will connect the system to your existing software and processes, prepare users, monitor behavior, handle incidents, and maintain dependencies. Clarify who is responsible for updating or retraining models and what happens when a model or API changes, performance declines, or your internal process changes.
Agree on what monitoring and support are included after launch, who receives alerts, how quickly the company responds, and what information you will have to diagnose a problem. NIST procurement guidance includes questions about deployment training, transparency, monitoring, and maintenance; Microsoft’s use-case guidance also treats maintenance and product changes as work that continues beyond release.
Put scope, ownership, and costs in the agreement
The proposal and contract should make the work testable and responsibilities clear. Address:
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- Scope, assumptions, deliverables, milestones, and acceptance criteria.
- How changes to scope are approved and priced.
- Rights to project data, code, configurations, and other outputs.
- External services and dependencies, including relevant provider terms.
- Security, support, maintenance, and service expectations where appropriate.
- Documentation, access, handover, and exit arrangements.
Ask how the estimate is calculated and which costs are excluded. Account for cloud and model usage, integration, maintenance, and ongoing support—not only the initial build. Also agree how success and return on investment will be tracked. AWS Prescriptive Guidance recommends AI-specific project and contract templates, service-level agreements, partner performance measures, cost allocation, ROI tracking, and flexible engagement models. There is no universal price range established by these sources: scope, geography, data readiness, integrations, risk, and support needs all affect the estimate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare candidates on the same criteria
Once you have a shortlist, ask every candidate the same questions and compare evidence against your use case. You can use a simple scorecard, but do not treat a single total as a substitute for judgment: the cited guidance supports these comparison axes, not universal weights or one scoring formula.
| Evaluation area | What to compare |
|---|---|
| Business and user fit | Clarity of the outcome, user need, success measure, assumptions, and alternatives to AI. |
| Relevant expertise | Named team roles, technical and domain skills, and evidence from comparable work. |
| Architecture and dependencies | Transparency about models, data sources, APIs, libraries, cloud services, and external providers. |
| Data rights and security | Data flows, access, retention, deletion, ownership, safeguards, and division of security responsibilities. |
| Evaluation and acceptance | Use-case-relevant tests, acceptance criteria, failure handling, human review, and relevant bias checks. |
| Integration and operations | Integration plan, monitoring, incident response, maintenance, user training, and change handling. |
| Contract and total cost | Deliverables, ownership, support, change control, handover, initial fee, and ongoing cost assumptions. |
Questions to ask an AI development company
- What business outcome are you proposing to improve, and how will we measure it?
- Why is AI appropriate here, and what simpler alternatives did you consider?
- Which people and skills will perform the work, and what comparable evidence can you share?
- Which models, data sources, APIs, libraries, and cloud services will the solution depend on?
- What information will be sent to each external provider, and what are the retention and deletion terms?
- What are the acceptance criteria, and how will you test quality, failure modes, and relevant bias risks?
- Which of our systems must the solution integrate with, and how will failures or dependency changes be managed?
- Who owns and can access the data, code, configurations, and other project outputs?
- What monitoring, incident response, maintenance, and support are included after launch?
- What assumptions drive the estimate, and which ongoing costs fall outside the initial fee?
These questions adapt procurement and implementation criteria from NIST, Microsoft, and AWS for a buyer’s shortlist discussion.
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