AI integration for AI Products teams
Eval suites that block bad deploys, cost-aware model routing, agent boundaries that hold, and prompt observability for production AI surfaces.
Domain context
AI integration inside an AI-product team is a different problem than AI integration anywhere else. The model isn't a feature added to a product that already works. It is the product. A regression in eval pass rate is a customer-visible quality regression. A 2x cost-per-request bump from a model upgrade is a business-model regression. An agent that loops on a tool-use error is a support incident at 3am. The work is engineering the system around the model so that prompt changes, model swaps, and agent extensions become routine events rather than heroic ones. Every prompt is versioned. Every model call is logged. Every agent step is bounded. Every deploy is gated on an eval suite the product team actually trusts.
Why this combination
Most AI integration work today is generic LLM plumbing: pick a provider, write a prompt, ship a chat surface. In an AI-product team, that plumbing is the easy part, and it's mostly already built. The work that decides whether the product survives a year of model changes is everything around it. The eval harness with golden datasets and an LLM-as-judge layer that's calibrated against human review. Prompt observability that captures every call with input, output, model version, latency, and cost. A model-tier router that sends easy queries to a cheaper model and reserves the frontier model for the hard ones. An agent framework with explicit step budgets and human-in-the-loop checkpoints. And a kill switch the on-call engineer can pull without a deploy. None of that is provider-SDK code. It's the production discipline AI products live or die on.
“We shipped an eval-driven release pipeline for an AI-native B2B platform with a 2,400-case golden set, an LLM-as-judge layer with calibrated agreement, and a model-tier router that cut average cost-per-request 47% with no measurable quality regression.”
Frequently asked
- A golden dataset that grows with the product. A pre-merge eval run that blocks deploys on regression. An LLM-as-judge layer that's calibrated against human eval and honest about its disagreement rate. A canary stage where the new prompt or model serves a small cohort with full observability before it goes wide. The eval suite is owned by both engineering and the product team, not handed off to a research team that ships separately.
- Classify the incoming query, usually with a small, cheap model or a heuristic, and route it to the smallest model that can handle it. Easy lookups go to a small model. Hard reasoning goes to a frontier model. The boundary gets set per-surface and validated against the eval set. The router itself is logged so you can replay any decision and tune the threshold. In our experience this is where the cost-per-request gains actually come from, and most teams are still routing every query to the most expensive model.
- Useful, but easy to over-build. Multi-step agents work well when each step is bounded: explicit tool list, hard step budget, structured output validation between steps, and a circuit breaker that stops the loop if the same tool fails twice. They go badly when teams treat the agent as a magic black box that 'just figures it out.' We design agents with the same observability discipline as a microservice. Every step is a logged event with input, output, latency, and cost.
- Two ways. First, every assertion the model produces is grounded against retrieved sources where the surface allows it, and outputs that fail to ground come back as 'I do not have a confident answer' rather than fabricated. Second, every output passes through a structured-extraction layer that pulls out the claims. Outputs that fail extraction get a degraded UI rather than the confident-looking default. Hallucination becomes a refusal or a hedge, not a customer-visible mistake.
- Every model call gets captured with input, output, model version, the prompt template version, the user and surface IDs, latency, and the token cost. The store is queryable. A support ticket that says 'the assistant said something weird at 3:14pm' becomes a single query, not a forensic exercise. Prompts and responses with PII are handled per the data classification rules, and the retention window is set per-surface: long enough for debugging, short enough to limit blast radius if the store is ever compromised.