Ship the AI product at enterprise quality.
For AI-native finance product teams moving from prototype to production with enterprise buyers.
Production architecture, retrieval, orchestration, evals, prompt optimization, and observability, built into your repo and handed off to your team.
Four moves get the product live.
Each move turns the AI product into production software an enterprise buyer can inspect, test, and trust.
Multi-agent orchestration
Agent chains, tool routing, model selection, retry and fallback. The control plane that turns product intent into production behavior.
Retrieval and grounding
Retrieval pipelines, chunking, hybrid search, freshness, and citation. The plumbing that lets a finance buyer trust the AI product in production.
Eval harness and prompt optimizers
CI-grade evals, prompt search, model comparison, and an evals layer inside your own SaaS, built on a curated golden dataset. The discipline that keeps regression visible between releases.
Observability and redaction
Trace capture, PII redaction, drift indicators, policy violation alerts. The instrumentation a finance customer or auditor will expect to see, wired into your existing stack.
AI Engineering is the productionization practice.
Production code, embedded engineering, two-week increments, launch-quality instrumentation, and full handoff into your team’s stack.
AI Engineering takes product AI into production.
AI Audit, Transformation, Governance, and Fluency help finance enterprises run AI. AI Engineering helps AI-native finance product teams get their own AI product live at enterprise quality.
- AI AuditTwo-week operating read.
- AI TransformationCapture-side workstream against the workflows.
- AI GovernanceContinuous evidence on the same trace pipeline.
- AI FluencyPer-role tooling, training, telemetry.
Start with the 2-week AI Audit.
Leave with the operating read: AI value, AI risk, fluency gaps, owners, and the next funded workstream.
Questions buyers actually ask.
Bring us in when an AI product needs to cross from prototype into enterprise production: architecture, eval harnesses, retrieval, orchestration, observability, and handoff into your engineering process.
We produce running systems: agent chains, evaluation pipelines, prompt optimizers, retrieval plumbing, and observability your team owns after launch.
A named TrustEvals engineer embeds with your team for the productionization window. Two-week increments, named outcomes per increment, full handoff at the end. We work in your repo, your CI, your stack.
The platform is the operating layer for a finance enterprise running AI across many teams. AI Engineering is for AI-native finance product companies getting their own AI product live at enterprise quality.