AI Reliability: Evals, Governance, Cost
Evals, guardrails, and cost discipline, so your AI keeps shipping under real load.
Most AI pilots die between demo and production. We build the reliability layer that keeps them shipping: evals to catch hallucinations and regressions, governance for policy and audit trails, cost optimization to keep LLM bills from doubling every quarter.
Who AI Reliability: Evals, Governance, Cost Is For
- Teams whose AI feature works in staging but is breaking unpredictably for real users.
- Companies preparing for compliance review (SOC 2, HIPAA, internal audit) and need defensible AI governance.
- Engineering leaders watching LLM cost growth outrun usage growth.
How AI Reliability: Evals, Governance, Cost Works
- Step 01
Failure mode mapping
Pull a representative sample of production traffic, label what's failing and why, and prioritize the failure classes worth fixing first.
- Step 02
Eval scaffolding
Build offline evals for the failure classes identified, wire them into CI, and add regression gates so a model or prompt change can't ship without passing them.
- Step 03
Governance and audit
Policy enforcement at the prompt and tool layer, audit logs, model and prompt versioning, and the documentation a compliance reviewer actually wants.
- Step 04
Cost work
Caching, smaller models for cheap paths, prompt compression, and budget alerts, measured in cost per task, not raw API spend.
What you get
- Eval suite running in CI with regression gates.
- Governance layer: policy enforcement, audit logs, versioning.
- Cost dashboard with per-task and per-feature breakdown.
- Quarterly reliability report covering failures caught, fixes shipped, and cost trend.
Where we've shipped this
All case studies →Frequently asked questions
What is an AI eval, and why do I need one?
An eval is an automated test for AI behavior: given this input, the output should look like this. Without evals, you cannot tell whether a prompt change, a model upgrade, or a retrieval tweak made the system better or worse. Evals are how AI systems stop being a vibe-check and start behaving like software.
How is governance different from compliance theater?
Compliance theater is a binder of policies that nobody reads. Governance is audit trails, policy enforcement at the prompt and tool layer, and a written process for how the team responds when something goes wrong. The first one passes a checklist. The second one passes an actual audit and protects you when an incident happens.
How do you control LLM cost without hurting quality?
Smaller models for the easy work, bigger models gated behind cost-aware routing. Caching for repeat queries. Prompt audits to remove unnecessary context. Token budgets per request and per agent run. Cost-tracking dashboards so the team sees the bill move in real time. Cost discipline is design, not panic-cutting after the invoice arrives.
Do you replace our existing observability stack, or plug into it?
Plug in. If you already use Datadog, Grafana, Sentry, or Honeycomb, the AI observability layer publishes there. If you use LLM-specific tools like Langfuse, Helicone, or LangSmith, we can pipe to those instead. The point is one place to look, not yet another dashboard the on-call engineer has to learn.
Let's build something that actually works.
Tell us where you are and what you need. We'll come back with a clear, honest plan within 48 hours.