This checklist covers the critical evaluation criteria for ai integration partner questions. Companies that follow a structured vetting process report 3x higher satisfaction with their AI development partners and 60% fewer project failures.
Use this checklist during your evaluation process. Score each agency on these criteria and compare scores objectively before making a final decision.
Technical Expertise Assessment
Core AI Capabilities
- LLM experience: Can they name specific models they’ve deployed (GPT-4, Claude, Llama, Mistral) and explain selection criteria?
- RAG implementation: Have they built production RAG systems? Can they discuss chunking strategies, embedding models, and retrieval optimization?
- Agent development: Experience with multi-step AI agents, tool use, and function calling architectures
- Prompt engineering: Systematic approach to prompt development, versioning, and optimization
- Fine-tuning: When and how they’ve fine-tuned models, including data requirements and evaluation methods
Infrastructure and DevOps
- Cloud deployment: Experience with AWS, GCP, or Azure AI services and production deployment
- MLOps practices: CI/CD for ML models, automated testing, monitoring, and alerting
- Vector databases: Hands-on experience with Pinecone, Weaviate, Qdrant, or Chroma
- Scalability: Can they explain how their architectures handle 10x-100x traffic increases?
- Security: Data encryption, access controls, and compliance frameworks implemented
Evaluation Method
Ask each candidate to walk through a specific past project’s architecture. Engineers (not salespeople) should explain:
- Technology choices and tradeoffs
- Challenges encountered and solutions implemented
- What they’d do differently with hindsight
- Performance metrics achieved
Red flag: Vague answers, inability to discuss technical details, or only salespeople available for technical discussions.
Portfolio and Experience Review
Case Study Quality
- Relevant projects: At least 2-3 case studies in your industry or use case category
- Specific metrics: Quantified outcomes (accuracy rates, cost savings, time reduction, user adoption)
- Technical detail: Architecture diagrams, technology stack decisions, and integration patterns
- Honest challenges: Acknowledgment of difficulties and how they were overcome
- Client references: Willingness to provide direct references you can contact
Team Composition
- Named team members: Specific engineers assigned to your project, not generic “we have talent”
- Relevant experience: Team members have worked on similar projects before
- Seniority mix: At least one senior ML engineer and technical lead on your project
- Stability: Low team turnover and consistent availability throughout engagement
- Backup plan: What happens if a key team member becomes unavailable?
Process and Delivery Assessment
Project Management
- Methodology: Clear development process (Agile/Scrum with AI-specific adaptations)
- Communication: Defined cadence (weekly demos, daily standups, Slack/Teams access)
- Documentation: Standards for code, architecture, and knowledge transfer
- Change management: Process for handling scope changes and budget impacts
- Risk management: How they identify and mitigate project risks proactively
Quality Assurance
- Testing approach: Automated evaluation suites, human review processes, edge case testing
- Performance benchmarks: Clear metrics for model accuracy, latency, and reliability
- Security testing: Penetration testing, prompt injection protection, data leakage prevention
- Code quality: Code review practices, linting, and architectural standards
Commercial and Legal Review
Pricing Transparency
- Detailed estimates: Line-item breakdowns, not just total project costs
- Rate card: Clear hourly rates by role/seniority level
- Scope clarity: Explicit list of included and excluded deliverables
- Change order process: How additional work is priced and approved
- Payment terms: Reasonable structure (30/40/30 or monthly milestones)
Contract Terms
- IP ownership: All work product, code, and model weights belong to you
- Data protection: NDA, data handling procedures, and compliance commitments
- Termination clause: Reasonable exit terms with code handover requirements
- Liability: Professional liability insurance and indemnification provisions
- Support terms: Post-launch support scope, duration, and costs clearly defined
Scoring Guide
Rate each section 1-10 and multiply by weight:
| Section | Weight | Agency A | Agency B | Agency C |
|---|---|---|---|---|
| Technical Expertise | 30% | ___ | ___ | ___ |
| Portfolio/Experience | 25% | ___ | ___ | ___ |
| Process/Delivery | 25% | ___ | ___ | ___ |
| Commercial/Legal | 20% | ___ | ___ | ___ |
| Weighted Total | 100% | ___ | ___ | ___ |
Minimum threshold: Agencies scoring below 6/10 in any single category should be eliminated regardless of total score. Technical expertise below 7/10 is a disqualifier for complex AI projects.
Frequently Asked Questions
How many agencies should I evaluate using this checklist?
Evaluate 3-5 agencies for the best balance of comparison depth and evaluation efficiency. Fewer than 3 doesn’t give sufficient perspective on pricing and approaches. More than 5 creates evaluation fatigue without adding meaningful differentiation. Start with a broader list of 8-10 based on referrals and online research, then narrow to 3-5 for detailed evaluation.
How long does the full evaluation process take?
Plan for 4-6 weeks from initial outreach to final decision. Week 1-2: Initial calls and shortlisting. Week 2-3: Detailed technical discussions and proposal requests. Week 3-4: Proposal review and reference checks. Week 4-6: Final negotiations and contract signing. Rushing this process increases the risk of choosing poorly.
What’s the single most important criterion on this checklist?
Relevant technical expertise with production deployments. An agency that has built and deployed systems similar to what you need will deliver faster, with fewer surprises, and at lower total cost. Portfolio quality (with verifiable references) is the strongest predictor of project success. Prioritize agencies with specific experience in your use case over those with lower rates but no relevant track record.
Should I require agencies to do a paid technical assessment?
A paid technical assessment ($2,000-$5,000) is valuable for complex projects over $100,000. It reveals how the agency thinks about your specific problem, their communication style, and their technical depth. Structure it as a mini-discovery: provide your requirements and ask for architecture recommendations, technology selection rationale, and implementation approach. This investment prevents much larger losses from choosing the wrong partner.
How do I verify an agency’s claimed case studies?
Request direct contact with 2-3 references for similar projects. Ask references: Was the project delivered on time and on budget? How was communication quality? Would you hire them again? What was the biggest challenge? Additionally, verify team members’ backgrounds on LinkedIn, check for technical blog posts or conference talks, and review their GitHub contributions for open-source work.
Key Takeaways
- Structured evaluation using weighted criteria produces 3x better partner satisfaction than informal selection
- Technical expertise assessment is the strongest predictor of project success: prioritize it at 30% weight
- Always verify case studies with direct client references before committing
- Allow 4-6 weeks for thorough evaluation; rushing increases failure risk significantly
- Eliminate agencies scoring below 6/10 in any category regardless of overall score
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