If you've ever been in the room when a vendor pitches an "AI integration" project, you know the deck always looks solid. Confident model talk, a portfolio of logos, a timeline that sounds reasonable. What that deck almost never shows you is whether the team can actually connect a model to your real systems and keep it working once it's live. Research from RAND Corporation found that AI projects fail to reach production at roughly double the rate of ordinary software projects, and having sat through a fair number of these evaluations, the gap almost never traces back to model quality.
This is a technical vetting checklist: the questions worth asking in an engineering-to-engineering conversation before anyone signs a contract. If you're running this evaluation without deep in-house LLM experience yet, this is also exactly the kind of technical review a team offering AI app development services gets asked to sit in on, because the failure modes here aren't obvious from a sales deck alone.
Ask for the architecture, not the pitch
Get specific about how they'd actually wire the model into your stack, not a general description of capability.
Questions worth asking directly:
- How does the model access our data: direct query, RAG pipeline, or cached snapshot?
- What's the retrieval strategy if this involves RAG, and how do you tune it against real queries?
- How do you handle auth for each system the agent needs to touch?
- What's the fallback behavior if a downstream API call fails mid-task?
- How is conversation/session state stored, and what's the retention policy?
A team that can answer these with specifics, not "we'll figure that out in discovery", is a meaningfully different conversation than one giving you a generic capability pitch.
RAG competence is a real technical filter; use it
If the project involves grounding a model in your documents or data (and most integration work does), ask them to walk through their actual retrieval pipeline design, not just confirm they "do RAG."
What a real answer sounds like:
chunking strategy tuned to document structure
-> embedding model choice and why
-> vector store choice and reasoning
-> reranking step (or explicit reasoning for skipping it)
-> reindexing strategy as source documents change
A vague answer here, "we use a vector database and it works well", is a signal worth taking seriously. Teams with real production RAG experience have opinions about chunking strategy and retrieval quality tuning because they've hit the failure modes already. Teams without that experience tend to treat it as a solved problem you install rather than a pipeline you tune.
Legacy and API integration experience separates real teams from demo teams
Connecting to a clean modern REST API is not a differentiator, most teams can do that. Ask specifically about legacy system experience: how they've handled inconsistent data formats, systems without modern APIs, or authentication models that don't fit a standard OAuth flow. This is where projects actually stall, and it's the question that separates a team with genuine integration depth from one that's only built demos against clean sandbox data.
LLMOps and MLOps practices tell you if they can actually run this in production
A working prototype and a production system are different engineering problems. Ask directly:
- What's your approach to model version control and rollback if a new model version regresses on your eval set?
- How do you monitor for accuracy drift after launch, and what triggers a retraining cycle?
- Do you maintain a regression eval set, and how does it get updated as edge cases surface in production?
- What's your incident response process if the agent does something wrong in production?
Teams that can answer these concretely have almost certainly run a system through this cycle before. Teams that treat "deployment" as the finish line, with a vague answer about "we'll monitor it", are telling you something important about what happens after launch.
Security needs a real answer, not a reassurance
"We take security seriously" is not a technical answer, and you should treat it as a yellow flag if that's all you get. Ask for specifics: encryption at rest and in transit, access control model, and documented compliance work relevant to your industry, GDPR, HIPAA, SOC 2, whichever applies. If your data is sensitive, ask how they've handled a security incident before, not hypothetically, but a real example.
The prototype-to-production gap is the single best filter
More than any other question, this one separates teams that ship from teams that produce impressive demos: ask for evidence of a system they've actually taken from a working prototype into a live, production environment handling real traffic, with a real outcome attached. Not a case study slide, an actual technical walkthrough of what changed between the demo and the production version.
Prototype vs production, what actually differs:
Prototype: works on curated test inputs, no monitoring, single environment
Production: handles adversarial and messy real input, has monitoring and alerting, has a rollback plan, has defined SLAs
If every example a team shows you skips that transition, that's the actual answer to whether they can do it for you.
Ownership terms, confirm this before a single commit lands
Get explicit, in writing, on who owns the model, the code, the data pipelines, and any fine-tuned weights once the engagement ends. This sounds like a legal question, but it's also a technical one, ambiguous ownership terms have real consequences if you need to switch vendors later and discover the retrieval pipeline or fine-tuning setup isn't actually portable.
Run a paid pilot against real, messy data
Before a longer contract, scope a two-to-four-week pilot against your actual production-adjacent data, not a clean sample dataset. This surfaces retrieval quality, integration friction, and communication patterns far faster and more reliably than any reference call. A pilot that goes badly costs a few weeks. A long-term contract with the wrong technical partner costs a rebuild.
The takeaway
Vetting an AI integration partner is fundamentally an engineering evaluation, even when it gets handled as a procurement decision. Ask about actual architecture, real RAG pipeline experience, legacy integration history, LLMOps maturity, and concrete production evidence, not a general capability pitch. The team that can answer these specifically is a much safer bet than the one with the most polished deck.
For the fuller framework, including all the evaluation factors and red flags to watch for, this AI integration partner selection guide is worth reading alongside your own technical checklist.
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