The challenge
Why AI products feel risky to put into production
Most companies are not afraid of building AI demos. They are afraid of operationalising them. A prompt tweak, model upgrade, retrieval change, or vendor update can quietly degrade quality, introduce unsafe outputs, or blow up spend. Traditional CI/CD does not tell you whether an LLM-backed workflow is still trustworthy.
That creates a production bottleneck. Teams either ship too little because nobody trusts the system, or ship too much without the instrumentation and controls needed to detect failure early. Both outcomes slow the roadmap. The missing layer is LLMOps discipline built around evaluation.
What we do
How we make AI releases testable and governable
We design the tooling, evaluation logic, and release workflow that let your engineering team validate AI changes before they reach production.
Evaluation Strategy & Failure Mapping
We define what failure looks like before anything ships: hallucinations, unsafe responses, policy breaches, regressions in quality, latency spikes, and runaway cost.
The result is a testable quality standard that reflects your use case rather than generic benchmark theatre.
Tracing & Observability Setup
We instrument your AI workflows with the right LLMOps tooling so prompts, model choices, retrieved context, outputs, latency, and spend are visible end to end.
Typically implemented with platforms such as LangFuse, Arize, or Weights & Biases, depending on your stack and operating model.
Automated Eval Pipelines
We build evaluation pipelines that run automatically in CI/CD and before promotion to production.
These tests score prompts and outputs for drift, bias, safety compliance, groundedness, and cost efficiency before new changes reach users.
Release Gates & Continuous Monitoring
We design the release workflow, thresholds, and escalation paths that decide when an AI system can ship, when it needs review, and when it must roll back.
So model updates and prompt changes are governed with evidence, not instinct.
What you get
Outputs
Instrumented LLMOps Stack
A production-ready instrumentation layer for your AI applications, with traces, quality signals, latency data, and cost visibility in one place.
Automated Eval Suite
A reusable library of programmatic and rubric-based evaluations tuned to your use cases, risks, and expected behaviour.
AI Release Workflow
Promotion gates, review rules, and rollback triggers integrated into your existing engineering workflow so AI changes are validated before deployment.
Monitoring & Governance Cadence
A clear operating rhythm for reviewing drift, incident patterns, safety performance, and cost trends after go-live.
Who this is for
A good fit if…
Teams moving from promising AI prototypes to production systems with real user exposure
Organisations that need a defensible way to test prompt, model, and retrieval changes before release
Engineering teams under pressure to prove that AI quality, safety, and cost are being actively managed
Companies with multiple internal AI applications that need one repeatable QA standard across the portfolio
Leaders who know shipping AI without evaluation discipline is a governance and reputational risk
How it works
A controlled path from prototype to production
We establish the failure criteria, implement the observability layer, build the evaluation suite, and wire the release process into your existing delivery model.
Step 1
Failure Mapping
We identify the business-critical failure modes, success criteria, and operational constraints for your AI workflow.
Step 2
Instrumentation Setup
We implement tracing, logging, metadata capture, and experiment visibility across prompts, model calls, and downstream steps.
Step 3
Eval Pipeline Build
We create automated tests and scorecards that run before release and produce evidence your team can review.
Step 4
Release & Review Model
We define the thresholds, human approvals, and monitoring cadence that govern ongoing production changes.