Practical guide to evaluate AI partners: technical criteria, essential questions and red flags to avoid when selecting the right vendor.
Your AI partner choice can make or break your technology strategy. Unlike traditional software, AI projects involve technical uncertainties, sensitive data handling and specific regulatory requirements.
An inadequate vendor doesn't just waste budget — it can compromise data, create problematic technical dependencies or deliver solutions that don't scale. For SMEs where every investment counts, this decision becomes even more crucial.
Start by evaluating the vendor's real technical competence. Ask specifically about technologies they use: do they work with LangChain for LLM orchestration? Do they use frameworks like Vercel AI SDK or prefer custom solutions? Do they have experience with OpenAI, Anthropic APIs or open-source models?
Verify they understand concepts like RAG (Retrieval-Augmented Generation), fine-tuning, and security guardrails. A serious vendor should be able to explain trade-offs between different approaches and justify their technical choices.
Don't settle for vague case studies. Ask for technical details: what specific problems did they solve? Which metrics improved? How did they measure ROI? An experienced vendor should be able to share challenges faced and how they overcame them.
Pay attention to the type of clients they serve. B2B SaaS experience differs from e-commerce or public sector. If possible, speak directly with previous clients to validate performance claims and satisfaction levels.
AI involves processing sensitive data, especially in use cases like document analysis or customer support automation. Verify the vendor has robust data privacy protocols and knows relevant regulations (GDPR, sector-specific compliance).
Ask about where data is processed, how it's stored, and what guarantees they offer about not using it for model training. Serious vendors should have clear agreements about intellectual property and confidentiality.
AI projects have significant variable costs — from model APIs to compute for fine-tuning. A good vendor should be transparent about cost structure and help estimate future operational expenses.
Be wary of proposals with unrealistically low fixed prices or that don't mention external API costs. Ask about pricing models: hourly, per project, revenue share, or hybrid? Each has trade-offs that should be clearly explained.
Some warning signs are obvious: promises of 'AI that solves everything', unrealistically short timelines, or vendors who can't explain their solutions' limitations. Others are more subtle but equally problematic.
Avoid vendors who use only buzzwords without technical substance, who don't have similar use cases to yours, or who resist sharing details about architecture and methodology. Transparency is essential in AI projects.
Structure evaluation in phases: first, filter vendors by basic technical competence and relevant experience. Then, request detailed proposals from a subset of 2-3 candidates, including technical architecture and realistic timeline.
Consider starting with a small pilot project or POC before committing to larger implementations. This allows you to evaluate delivery capability, communication quality and cultural fit without excessive risk.
Consultancies focus on strategy, assessment and roadmaps. Development companies implement technical solutions. Many, like Simmple, offer both services to ensure continuity between planning and execution.
Depends on project complexity. For specific use cases (natural language processing, computer vision), specialists are preferable. For general strategy and multiple use cases, generalists with proven experience work better.
Ask for detailed practical cases, not just marketing case studies. Inquire about specific technologies used (LangChain, OpenAI API, etc.) and technical challenges faced. Speak directly with previous clients.
Performance SLAs, robust confidentiality agreements, clarified intellectual property, and contingency plans for model failures. Avoid vendors promising 100% guaranteed results.
Varies dramatically: POCs in 2-4 weeks, simple implementations in 2-3 months, complex systems can take 6-12 months. Be wary of unrealistically short or excessively long timelines.
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