A product and architecture approach for adding AI without destabilising a working product. This guide gives buyers a structured way to frame the decision, identify risk and ask more useful questions of an internal team or delivery partner.
Start with a user decision or task
Do not begin with a model catalogue. Identify a repeated task where users spend time interpreting, searching, drafting or deciding, then define what a better outcome looks like.
Create an AI boundary
Place model calls behind a service layer that controls identity, data access, prompts, output schemas, cost and logging. This keeps the core product from depending directly on one provider.
Ground and evaluate
If the feature relies on company knowledge, retrieval should cite approved sources. Build an evaluation set from real tasks before launch and track quality by use case rather than one generic accuracy number.
- Factual grounding and citations
- Task completion quality
- Unsafe or out-of-scope responses
- Latency and cost per successful task
- Escalation and correction rate
Design human control
Decide which outputs can be shown, which can be saved as drafts and which actions require explicit approval. Consequential workflows need visible evidence, audit history and an easy way to correct the system.