Evaluating an AI vendor is not the same as evaluating a traditional SaaS product. AI platforms sit at the intersection of data handling, machine learning infrastructure, third-party model dependencies, and rapidly evolving capabilities. A vendor that looks strong on a demo can fall apart under production workloads, security scrutiny, or scaling demands.
This checklist provides 50 questions organized into ten categories. Use it as a structured framework during your RFP process, vendor demos, and technical evaluations. Every question is designed to surface real capabilities and real limitations before you sign a contract.
How to Use This Checklist
Score each answer on a 1-5 scale:
- **5:** Exceeds requirements, best-in-class answer
- **4:** Fully meets requirements
- **3:** Partially meets requirements, acceptable with workarounds
- **2:** Significant gaps requiring custom development
- **1:** Does not meet requirements
Weight each category according to your priorities. The weights below reflect what we typically see across enterprise buyers, but adjust them based on your specific context.
Category 1: Core AI Capabilities (Weight: 20%)
**1. Which foundation models does your platform support, and can we use multiple models simultaneously?**
Platforms that support only a single model create vendor lock-in at the model layer. Look for [multi-provider support](/blog/multi-provider-ai-strategy-claude-gpt4-gemini) across Anthropic Claude, OpenAI GPT-4, Google Gemini, and open-source models.
**2. How do you handle model updates and deprecations?**
Foundation models are updated frequently. Understand the vendor's testing process, rollback capabilities, and notification timelines when model versions change.
**3. What customization options are available for AI behavior?**
Evaluate system prompt configuration, knowledge base integration, fine-tuning capabilities, output formatting controls, and guardrail configuration depth.
**4. How does the platform handle conversations that exceed the model's context window?**
Long conversations and documents can exceed token limits. Strong platforms handle this transparently through summarization, chunking, or context management strategies.
**5. What is the average response latency under production load?**
Ask for p50, p95, and p99 latency numbers under realistic production conditions, not just best-case demo scenarios. Anything above 3 seconds for p95 will degrade user experience.
Category 2: Security and Compliance (Weight: 20%)
**6. Do you hold SOC 2 Type II certification? When was your last audit?**
Type II is the standard for enterprise. Type I only validates control design, not operational effectiveness. Demand the most recent audit report.
**7. How is data encrypted in transit and at rest?**
The answer should be TLS 1.3 for transit and AES-256 for rest, at minimum. Ask about key management practices and rotation schedules.
**8. Is customer data ever used to train or improve AI models?**
This must be a contractual guarantee, not just a verbal promise. Your data should never be used for training by the platform vendor or the underlying model providers.
**9. What data residency options do you offer?**
If you operate in the EU, you need EU-based data processing. Ask for specific cloud regions and data center locations, not vague assurances.
**10. Describe your incident response process. What are your breach notification timelines?**
Regulatory requirements often mandate notification within 72 hours. Understand the vendor's detection capabilities, response playbooks, and communication procedures.
Category 3: Access Control and Identity (Weight: 10%)
**11. Do you support SSO via SAML 2.0 and OpenID Connect?**
Enterprise SSO integration is non-negotiable for organizations with more than 50 employees. If the vendor charges extra for SSO, that is a red flag.
**12. What RBAC permissions are available?**
Evaluate the granularity of permissions: Can you control who creates agents, views conversation logs, modifies knowledge bases, and accesses analytics separately?
**13. Do you support SCIM for automated user provisioning?**
SCIM integration with your identity provider (Okta, Azure AD, OneLogin) automates onboarding and offboarding, eliminating stale accounts.
**14. Can you enforce MFA for all users?**
Multi-factor authentication should be enforceable at the organization level, not optional for individual users.
**15. How are API keys managed, scoped, and rotated?**
API keys should support scoping to specific resources and actions, automatic expiration, and rotation without downtime.
Category 4: Integration and APIs (Weight: 15%)
**16. Is your API RESTful, and is it versioned?**
A well-versioned API with backward compatibility guarantees protects your integration investment when the vendor releases new features.
**17. What pre-built integrations do you offer?**
Catalog the integrations you need (CRM, helpdesk, communication, data warehouse) and verify each one's depth. A "Salesforce integration" that only syncs contacts is very different from one that triggers workflows based on deal stage changes.
**18. Do you support webhooks and event-driven architectures?**
Webhooks enable real-time reactions to AI events (conversation completed, escalation triggered, threshold exceeded) without polling.
**19. What are your API rate limits and how do they scale?**
Understand the limits per minute, per hour, and per day. Ask whether limits increase with plan upgrades and what happens when limits are exceeded.
**20. Do you provide SDKs, and in which languages?**
SDKs in your team's primary programming languages accelerate integration development significantly. Evaluate SDK quality alongside availability.
Category 5: Workflow and Automation (Weight: 10%)
**21. Does your platform include a visual workflow builder?**
Non-technical users should be able to create and modify AI workflows without writing code. Evaluate the builder's capabilities for [no-code workflow creation](/blog/build-ai-workflows-no-code).
**22. Can workflows include human-in-the-loop approval steps?**
For high-stakes decisions, AI should recommend actions that humans approve before execution. This capability is critical for compliance-sensitive use cases.
**23. How do you handle workflow errors and retries?**
Understand error handling patterns: automatic retries, dead letter queues, alerting, and manual intervention options.
**24. Can workflows be triggered by external events, schedules, and API calls?**
Flexible triggering mechanisms determine how deeply workflows integrate into your existing processes.
**25. Do you support workflow versioning and rollback?**
The ability to version workflows, test changes in staging, and roll back to previous versions prevents production incidents.
Category 6: Data and Knowledge Management (Weight: 10%)
**26. What data sources can be connected as knowledge bases?**
Evaluate support for documents (PDF, DOCX), databases, APIs, CRM records, help center articles, and structured data.
**27. How frequently is ingested knowledge base data refreshed?**
Stale knowledge bases produce inaccurate AI responses. Understand refresh frequencies and whether real-time sync is available.
**28. How does the platform handle conflicting information across sources?**
When two knowledge sources disagree, the AI needs a resolution strategy. Ask about source prioritization, confidence scoring, and conflict flagging.
**29. Can knowledge base access be scoped by user role or team?**
In multi-team deployments, different groups should only access relevant knowledge. A sales AI should not reference HR policies unless explicitly configured.
**30. What is the maximum knowledge base size, and how does retrieval performance scale?**
Test retrieval accuracy at your expected data volume, not just with a small demo dataset. Retrieval accuracy often degrades as knowledge base size increases.
Category 7: Analytics and Reporting (Weight: 5%)
**31. What built-in analytics dashboards are available?**
Evaluate dashboards for conversation volume, resolution rates, response quality, user satisfaction, and cost tracking.
**32. Can analytics data be exported to external BI tools?**
Integration with tools like Tableau, Looker, or Power BI enables deeper analysis and correlation with other business data.
**33. Do you provide conversation-level analytics with drill-down?**
Aggregate metrics are useful but insufficient. You need the ability to drill into individual conversations to understand quality issues and improvement opportunities.
**34. How do you measure and report AI response quality?**
Ask about automated quality scoring, user feedback collection, and quality trending over time.
**35. Can custom reports be scheduled and distributed automatically?**
Stakeholders who do not log into the platform regularly still need visibility into AI performance through scheduled report delivery.
Category 8: Pricing and Commercial Terms (Weight: 5%)
**36. What is your pricing model, and how do costs scale with usage?**
Understand whether pricing is per-token, per-interaction, per-seat, or tiered. Model the cost at 1x, 5x, and 10x your expected usage to understand scaling economics.
**37. What is included in the base price versus what costs extra?**
Common items hidden behind premium pricing: SSO, advanced analytics, priority support, additional model providers, and higher rate limits.
**38. What happens when we exceed our usage allocation?**
Understand overage pricing, automatic tier upgrades, and whether the platform throttles or blocks usage when limits are reached.
**39. What are the contract termination provisions?**
Evaluate notice periods, early termination fees, data export timelines, and transition support commitments.
**40. Do you offer a proof-of-concept period with production data?**
A free trial with sample data proves very little. A PoC with your actual data and actual use cases is the only reliable way to evaluate production readiness.
Category 9: Support and Success (Weight: 3%)
**41. What are your support SLAs by severity level?**
For Severity 1 (system down) issues, response time should be under 30 minutes. Understand escalation procedures and after-hours coverage.
**42. Do we get a dedicated customer success manager?**
For enterprise accounts, a dedicated CSM who understands your business drives significantly better outcomes than generic support queues.
**43. What onboarding and training resources do you provide?**
Evaluate documentation quality, training programs, office hours, and community resources. The best platform in the world fails without proper enablement.
**44. How do you communicate product updates, deprecations, and incidents?**
Look for transparent communication channels: status pages, release notes, deprecation schedules, and proactive incident notification.
**45. Do you have a customer advisory board or feedback program?**
Vendors that listen to enterprise customers through structured programs are more likely to build features that address your evolving needs.
Category 10: Vendor Viability and Roadmap (Weight: 2%)
**46. What is your current funding status and revenue trajectory?**
AI startups burn cash quickly. Understand the vendor's financial position and runway to ensure they will be around in 2-3 years.
**47. What does your product roadmap look like for the next 12 months?**
Evaluate whether the roadmap aligns with your anticipated needs. Be cautious of vendors whose roadmap is exactly what you asked for -- that may signal they are selling futures, not capabilities.
**48. How many enterprise customers do you currently serve?**
A vendor with 5 enterprise customers has very different operational maturity than one with 500. More customers means more battle-tested infrastructure and processes.
**49. What is your employee retention rate, particularly in engineering?**
High turnover, especially in engineering, signals cultural or financial problems that will eventually affect product quality and support responsiveness.
**50. Can you provide references from companies in our industry with similar use cases?**
Industry-specific references are far more valuable than generic testimonials. Talk to references about implementation challenges, not just outcomes.
Putting the Checklist to Work
Create a Comparison Matrix
Build a spreadsheet with vendors as columns and questions as rows. Have each evaluator score independently, then discuss divergent scores as a group. This structured approach prevents the loudest voice from dominating the decision.
Involve the Right Stakeholders
AI vendor evaluation requires input from multiple disciplines:
- **Engineering:** API quality, integration complexity, scalability
- **Security:** Compliance, data handling, access controls
- **Operations:** Workflow capabilities, analytics, support quality
- **Finance:** Pricing, contract terms, total cost of ownership
- **Executive:** Strategic alignment, vendor viability, roadmap
Set a Decision Timeline
Open-ended evaluations drag on for months while competitors move ahead. Set a clear timeline: two weeks for initial screening (eliminate vendors that fail on non-negotiable criteria), four weeks for deep evaluation (PoC with top 2-3 vendors), and two weeks for contract negotiation.
Begin Your Evaluation Today
This checklist is a starting point, not a finish line. Customize it based on your industry, regulatory environment, and technical requirements. The organizations that ask better questions during evaluation make better decisions and avoid costly migrations later.
Girard AI welcomes rigorous evaluation. [Start a free proof of concept](/sign-up) with your own data and your own use cases, or [contact our enterprise team](/contact-sales) to walk through this checklist together.