AI PARTNER · SELECTION CRITERIA
How to choose an AI development partner
A strong proposal explains how value, data, errors, integrations, operations, and responsibility will be tested—not only the model and stack.
01 · DIRECT ANSWER
Direct answer
Choose an AI development partner by the quality of its first testable stage. It should name inputs, artifacts, continue and stop criteria, evaluation, risk, ownership, and future operations. Read portfolio claims by evidence and stage rather than the words AI or enterprise.
02 · CRITERIA
Criteria before development
Apply these criteria to the specific workflow, data, and cost of error. They are not a universal readiness checklist.
Testable first stage
One result, required inputs, validation window, and resulting decision are explicit.
Evidence
Case studies distinguish live, MVP, prototype, and concept; metrics have provenance and caveats.
Evaluation and risk
Errors are assessed by class and consequence instead of one average score.
Ownership and operations
Code, data, access, documentation, providers, support, and change cost are understood.
03 · PROCESS
Validation sequence
- 01
Compare framing
Give candidates one context and compare questions, assumptions, and validation methods.
- 02
Review artifacts
Ask for examples of a workflow map, evaluation plan, risk register, and readiness criteria.
- 03
Start bounded
Do not commit to full delivery before checking data and the highest uncertainty.
- 04
Define handover
Specify repository, infrastructure, data, documentation, and operating-procedure access.
04 · DECISION
Decision table
A strong signal is the ability to reduce uncertainty without invented guarantees.
| Signal | Interpretation | Why |
|---|---|---|
| Guarantees impact before data | High risk | Value and quality have not been measured. |
| Proposes one testable stage | Positive signal | Scope is tied to a decision rather than a full-project sale. |
| Cases omit stage and provenance | Request evidence | A demo, synthetic result, and production system mean different things. |
| Discusses incidents and rollback | Positive signal | The team considers operations, not only the happy path. |
05 · MEASUREMENT
What to measure
Record the baseline workflow and collection method first. Then compare equivalent scenarios without presenting a planned target as measured impact.
Before contract
Question quality, explicit assumptions, artifact availability, and absence of unsupported promises.
First stage
Agreed output, new evidence, decision log, and honest estimate changes.
Handover
Reproducible build, access, documentation, evaluation, and another team's ability to continue.
06 · BOUNDARIES
Risks
Vendor lock-in
Critical data, prompts, evaluation, or infrastructure may exist only with the provider.
Hidden subcontractors
Know who receives data access and who actually performs the work.
Fixed scope before discovery
It often hides assumptions or moves risk into change requests.
WHAT CANNOT BE CLAIMED
Limitations of the conclusion
- These criteria do not replace legal, security, or financial due diligence.
- A large team does not guarantee quality, while founder-led delivery does not fit every scale.
- The lowest price is not comparable without matching artifacts and boundaries.
07 · PORTFOLIO
Related projects and honest stage
AI SaaS Solution cases expose stages and limitations. They demonstrate an approach, not guaranteed outcomes in another organisation.
AI Digital Insider
The public platform is live at aidigitalinsider.ru with technology content and practical AI tools.
Open project reviewMarketing Optimisation
The MCP MVP implements one end-to-end reactivation workflow through eight tools and has been tested on synthetic data. Its architecture targets software replacement of routine CVM work, but a real-data pilot must validate its ability to replace a full department and deliver commercial impact.
Open project review08 · METHODOLOGY
Primary sources
These sources support the methodology and definitions. They do not validate AI SaaS Solution project outcomes.
Guidelines for AI procurement
Open sourceArtificial Intelligence Risk Management Framework (AI RMF 1.0)
Open sourceThe ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction
Open source