Evals
How do you know the output is good?
Automated and human-in-the-loop evaluation frameworks that measure quality before and after deployment.
AI Strategy
Move AI from pilots to production.
Most teams have experimented with AI. Few have built the systems to deploy it safely. We help you design evals, guardrails, and workflows that turn AI into infrastructure your team can operate.
Eval frameworks
Guardrails
Workflow design
The production AI loop
Four layers that make AI production-ready.
Production AI is not a single tool — it is an operating system around your model. Each layer answers a question your team will face when moving from demo to deployment.
Guardrails
What happens when it goes wrong?
Input validation, output filtering, rate limits, and safety checks that prevent bad AI behavior from reaching users.
Delivery loop
How do you ship changes safely?
Controlled rollout, A/B testing, rollback paths, and observability so every AI change is traceable and reversible.
Human oversight
AI in production still needs human judgment — for edge cases, quality review, and decisions the model should not make alone. We design the handoff points so your team stays in control without becoming a bottleneck.
What this covers
Practical AI strategy, not vendor selection.
Tool selection
Choose models, orchestration, and platform tooling based on fit for your workload — not hype cycles.
Workflow design
Map AI into engineering work so it speeds up decisions, reviews, and delivery — not just demos.
Adoption path
A clear sequence from experiment to production with measurable milestones at each stage.
Forward-deployed
We build the production AI loop in your environment, not in a sandbox.
Our engineers embed with your team, work in your codebase, and build eval and guardrail systems against your real data and workflows.
Measurable quality
Eval scores, regression tests, and quality dashboards your team can trust before every release.
Safe rollout
Canary deploys, feature flags, and rollback paths designed for AI-specific failure modes.
Production observability
Tracing, logging, and alerting that show you what AI is doing in production — not just that it is running.