01
Embed
We join your team, learn your stack, and understand where AI fits in real engineering work — not in a slide deck.
AI enablement partner
Deploy production AI your team can own and operate.
Forward-deployed engineers embed with your team to make AI production-ready — building eval frameworks, guardrails, delivery controls, and human oversight — so you move from pilots to production with confidence.
Evals
Guardrails
Forward-deployed engineers
Production
How we work
Forward-deployed engineers, not slideware.
Senior engineers embed in your codebase and workflows. They build alongside your team, not from a distance — and leave you with systems your team owns.
02
Build the production AI loop
Evals, guardrails, observability, and delivery controls that let you measure, trust, and ship AI output safely.
03
Hand off ownership
Your team runs the system. We leave behind documentation, runbooks, and an operating model that does not depend on us.
Production AI loop
The model is not what makes AI safe to deploy. The operating discipline is.
Production AI needs a closed loop — how you evaluate output, enforce guardrails, ship changes, observe behavior, and improve over time. Without it, pilots stall. With it, AI becomes infrastructure your team can run.
01
Define
Set quality bars and success criteria
02
Evaluate
Measure output before it ships
03
Guardrail
Enforce limits and safety checks
04
Ship
Deploy with controlled rollout
05
Observe
Monitor behavior in production
06
Improve
Close the loop and iterate
What we build
Four capabilities that make AI deployable — and affordable to run.
Production AI needs strategy, economics, infrastructure, and delivery systems that hold up under real load and real spend.
AI Strategy
From pilots to production-ready AI workflows.
Production AI workflows, eval frameworks, guardrails, and system integration that turn experimentation into infrastructure your team can operate.
Explore AI strategyAgentic Engineering
Token efficiency and agent economics at scale.
Options analysis, context design, model routing, and cost observability — so agent workflows scale without runaway inference spend.
Explore agentic engineeringCloud Architecture
Infrastructure where AI workloads run safely.
Platform shape, resilience, and cost clarity for the systems that host agents, inference, and the services AI depends on.
Explore cloud architectureDevOps and Automation
The delivery loop that ships and rolls back AI changes.
Pipelines, release controls, and operational automation that make deploying AI as safe and repeatable as deploying code.
Explore DevOpsThe journey
From first experiment to production AI.
01
Explore
Map where AI fits, what to measure, and what guardrails you need before scaling.
02
Embed
Forward-deployed engineers join your team and start building in your environment.
03
Harden
Build evals, guardrails, observability, and delivery controls so your AI is safe to ship and safe to scale.
04
Scale
Roll out to production with confidence and hand off a system your team owns.