Production engineering for AI-native product teams. We architect the whole implementation, orchestration, retrieval, evals, and the framework-mapped evidence, so your AI product ships at enterprise quality and clears your customer's procurement.
Every claim in the report traces back to source evidence, ownership, and the workflow decision it supports.
Anyone can build an AI workflow. The craft is architecting the whole implementation, combining your existing stack with the right mix of traditional ML and LLMs in a setup that is reliable and trustable by design. We author the integration, the deterministic guardrails, the evals, and the monitoring, not a wrapper around someone else's model.
Agent chains, tool routing, model selection, retry, and fallback. The control plane that turns product intent into dependable production behavior, with deterministic paths where an answer must be exact and reproducible, and an LLM only where judgment actually helps.
Retrieval pipelines, chunking, hybrid search, freshness, and citation. The plumbing that lets an enterprise buyer rely on the AI product in production instead of spot-checking every output it hands back.
CI-grade evals, prompt search, and model comparison, built on a curated golden dataset. Every release passes the golden set before it ships, so behavior never drifts into a customer's hands between one version and the next.
Trace capture, PII redaction, drift indicators, and policy-violation alerts. The instrumentation a customer or auditor expects to see, wired into your existing stack rather than bolted on beside it.
A named TrustEvals engineer embeds for the productionization window and works in your stack. Short increments, named outcomes per increment, and full handoff at the end. We stitch into what you already have; there is no rip and replace.
One accuracy figure never clears an enterprise buyer. We build and gate the real surface your customer touches, whatever shape the product takes.
Intent accuracy, groundedness, refusal correctness, and multi-turn consistency, gated so a customer-facing assistant behaves the same on release day as it did in review, and never quietly regresses into an answer no one signed off.
Tool-use correctness, plan validity, sub-step verification, and recovery. The orchestration and evals that let an agent act on your customer's systems of record without acting wrong, and prove it before the customer trusts it with real work.
Retrieval precision and recall, citation faithfulness, and hallucination control against grounded sources, so every answer traces back to a document the buyer can open and defend, not a plausible sentence the model invented.
Tenant-scoped evaluation under token isolation, with per-tenant accuracy and policy adherence, so each of your customers can clear their own compliance review on the same product without you forking the codebase per account.
Metric deltas across versions, providers, and fine-tunes, so a model upgrade ships on evidence instead of a hunch, and a regression is caught in the harness before a single customer ever sees it.
Framework-mapped evidence packs that answer your buyer's CISO in artifacts rather than slides, so the AI product clears review on the same trace stream that runs your regression suite, without a six-month security cycle.
Representative outcome at an AI-native finance product company once the eval harness became the deploy gate. 95% is the stated figure, ~90% measured. Not an audited fact, and durability, not speed, is the point.
The reasons an impressive AI product never crosses into enterprise production are consistent, and each one is an engineering problem before it is a sales problem.
Enterprise buyers freeze on an AI product they cannot inspect. Without framework-mapped evidence, every deal waits on a manual security cycle. The evidence pipeline answers the CISO in artifacts your buyer's team can read, so procurement moves.
A model swap or a prompt edit quietly regresses, and the first person to notice is a paying customer. The eval harness gates every release on a golden set, so behavior holds from one version to the next instead of degrading unseen.
A demo that wins the room stalls the moment it meets real traffic, tenant isolation, and audit expectations. Harness engineering rebuilds it on your existing stack to hold under load and under inspection.
Teams burn their scarce AI engineers standing up the eval and evidence layer instead of shipping the product. We build that layer, wire it into your stack, and hand it back, so your team stays on features.
When an AI product needs to cross from prototype into enterprise production: architecture, orchestration, retrieval, eval harnesses, observability, and handoff into your engineering process. Earlier is cheaper than retrofitting the assurance layer after a failed security review.
We take your AI from strategy to outcome, with governance, audit, and evals built into every build. Start with a discovery call, or a quick audit.