AI Reliability, Evals & Governance

Overview

A production AI system needs more than a strong prompt. We build the evaluation, observability and governance layer that tells you whether it is accurate, safe, fast and worth the cost. This can be part of a new build or an independent review of AI already running in your business.

The problem

AI can look convincing while failing quietly. Without test cases, traces, approval rules and an owner, teams discover problems through customers, compliance reviews or a rising API bill.

What we build

What this looks like in practice.

Evaluation datasets and scorecards based on real tasks and failure modes
Automated regression tests for prompts, models, retrieval and agent workflows
Guardrails, permissions and human approvals for sensitive actions
Tracing and dashboards for quality, latency, failures and cost
Red-team testing for misuse, prompt injection and unsafe behaviour
Practical AI policies, model inventories and clear operational ownership

Outcomes

What changes for your team.

01

Evidence that the system is ready for production

02

Problems caught before a model or prompt change reaches users

03

A clear audit trail for important decisions and actions

04

Better quality and cost over time instead of slow, invisible drift

Questions