Selecting an AI implementation partner requires evidence about the application your organization intends to operate. A useful evaluation connects the business task, the proposed architecture, the quality tests, and the responsibilities after launch.

Use this checklist in a technical diligence session with the executive sponsor, application owner, source-system owner, and relevant security or procurement reviewers. Ask the provider to mark each answer Demonstrated, Documented, Proposed, or Unresolved, with a dated evidence link. A proposed capability can belong in an implementation scope; it should not be scored as delivered behavior.

1. Business Fit

Ask: What task changes for the user? How is it performed now? What would make the proposed application worth operating?

Request: A short task description, intended users, current baseline, and two or three measurable acceptance criteria. Specify the work the application supports and the decisions people retain.

Evaluate: Whether the provider can connect the technical design to a useful operating result. A time-saving claim needs a baseline and a measurement method. If the task is performed infrequently or source material is inaccessible, resolve the economics before committing to a build.

2. Source Material and Updates

Ask: Which information is authoritative? Who owns it? How does a changed or withdrawn document affect future answers?

Request: A source inventory showing owner, permitted use, update method, version information, and retention requirements. Ask for a demonstration of an update and a withdrawal using test material.

Evaluate: Whether the team knows how information enters and leaves the application. A polished answer from an old policy is still an operating failure.

3. Access and Data Handling

Ask: Which users can retrieve which information? What happens if someone obtains a direct document link? Which systems and external providers receive data?

Request: A data-flow diagram covering uploads, document storage, indexes, model requests, logs, exports, and backups. Ask the provider to demonstrate denied access using separate test accounts, including direct file access and revoked permissions.

Evaluate: The actual configuration and test results. Role labels and architecture diagrams do not establish that every storage or retrieval path enforces the intended access policy. Agree on permitted data before onboarding it.

4. Answer Quality

Ask: How will the team test routine questions, ambiguous wording, conflicting sources, and questions the available information cannot answer?

Request: A representative test set, expected evidence, scoring criteria, and examples of failed answers. Have a domain expert assess whether citations support the answer. Record cases where a person should review the result.

Evaluate: Quality by task and error consequence. A single average score can conceal an unacceptable failure on a critical question. Set thresholds with the sponsor before running the evaluation, and rerun tests after material changes.

5. Application Integration

Ask: Where will users encounter the application? What does it read or change? What happens if a source system becomes unavailable?

Request: The proposed interface, API contract, dependencies, credential responsibilities, and failure behavior. Distinguish existing connectors from new engineering. For actions that change records, define authorization and human approval before implementation.

Evaluate: Whether the application fits daily work and has an understandable failure path. A separate demonstration screen does not prove integration into the system employees use.

6. Security and Deployment Requirements

Ask: Which controls are implemented in the offered configuration, which require additional work, and which requirements cannot currently be met?

Request: Applicable technical evidence, customer responsibilities, and the exact scope of any independent assessment or authorization cited. Ask how the team tests access, handles sensitive inputs, responds to incidents, and confirms that a proposed hosting arrangement is feasible.

Evaluate: Each claim on its own evidence. A cloud provider’s assurance, a procurement listing, or a development roadmap does not establish authorization of the complete application. Record any requirement that prevents the proposed data or use case from proceeding.

7. Operating Ownership

Ask: Who responds to a failed import, an incorrect answer, a service interruption, or a model change? Who can suspend the application?

Request: Named responsibilities, support hours, escalation paths, release procedures, and an example operating record. Agree on the information needed to investigate an incident and the handling of any personal data in that record.

Evaluate: Whether the commercial agreement covers the work necessary to keep the application useful. Make support and improvement responsibilities explicit rather than assuming they are included in a license.

8. Cost and Commercial Scope

Ask: Which charges are for discovery, implementation, platform access, model usage, support, and future changes? What drives cost as adoption grows?

Request: A written scope with assumptions, exclusions, pricing units, change-control terms, and illustrative usage scenarios. Separate the initial project from recurring commitments. Ask who approves a higher usage limit or a new integration.

Evaluate: Total operating cost and accountability. Compare proposals on the same task, data, users, service scope, and acceptance criteria. A lower initial quote may simply exclude work another proposal includes.

9. Reuse and Expansion

Ask: What can a second team reuse? What must be configured or rebuilt? How will a new group of users change data access, support, and cost?

Request: A short expansion outline separating reusable platform capability from customer-specific work. Identify the expected next application and the evidence needed to justify it.

Evaluate: Whether the provider has a credible path from the first application to a useful operating platform. Require evidence of value before adding scope. Reuse should reduce duplicated effort without weakening the review for a new audience or data set.

10. Transition and Exit

Ask: If your organization changes providers or stops the application, what data and documentation can it retain? What happens to stored material and access?

Request: Contractual provisions for ownership, export, transition assistance, retention, deletion, and any limitations. Distinguish customer data and commissioned work from the provider’s platform intellectual property.

Evaluate: Whether continued use is a considered business choice. Transition requirements should be understood before procurement, while both parties can scope them accurately.

Evaluation Record

Use one row per requirement. Add the reviewer, evidence date, and next action. Do not replace essential requirements with an average vendor score.

For each requirement, record the evidence requested, observed result, status, reviewer, evidence date, owner, and next action. Cover:

• The intended task, business baseline, and acceptance criteria.
• Source updates and withdrawals.
• Allowed and denied data access.
• Answer quality and evaluation results.
• Integration and failure behavior.
• Required deployment controls.
• Operating responsibilities and escalation.
• Initial and recurring cost assumptions.
• Expansion, ownership, and transition provisions.

From Diligence to an Initial Engagement

Proceed when the initial task is valuable, the critical requirements are feasible, and the parties agree on the evidence needed for acceptance. Where a material question remains unresolved, a paid architecture and integration review can produce the information needed to scope delivery: a source and system map, a requirements register, an evaluation approach, and a commercial recommendation.

Sprinklenet combines senior AI advisory and engineering with Knowledge Spaces platform delivery. We work alongside the buyer’s internal team and define platform licensing, implementation, and operating support explicitly when they are included. Bring this checklist and one proposed application to an architecture and integration discussion.

Reference Context

This is Sprinklenet’s practical buyer checklist, not a certification scheme. The NIST AI Risk Management Framework provides voluntary risk-management guidance across AI design, development, use, and evaluation. NIST’s Generative AI Profile provides additional context for generative AI risks. The checklist’s specific evidence requests and commercial review questions are Sprinklenet’s recommendations.