What metrics do you track out of the box?

Accuracy, answer relevance, hallucination rate, latency (p50/p95/p99), cost per request, escalation rate, human override rate, semantic drift, and token efficiency.

How does regression testing for LLMs work?

We build curated test suites that run automatically on every model update, prompt change, or code release. Eval failures block deployment - same as a unit test failure.

Can you retrofit this to systems already in production?

Yes - retrofitting LLMOps infrastructure to existing AI systems is one of our most common engagements. We instrument without breaking running systems.

What tools do you use?

RAGAS, DeepEval, custom evaluation harnesses, Langfuse, OpenLLMetry, Prometheus, Grafana, and Datadog - chosen based on your existing stack and compliance requirements.

Service/Financial Services

AI Integration and Operations

Connect, evaluate, and monitor your AI reliably.

$18k - $32k /monthMinimum 3 months

We integrate AI capabilities into your existing applications and operations, and build the LLMOps infrastructure to keep your AI accurate, observable, and cost-efficient. Not just on launch day, but six months later.

Book a Discovery Call See case studies

Key outcomes

AI connected to your existing stack, securely
Evaluation and monitoring pipeline from day one
Cost per request tracked and optimised continuously

Delivery speed

Scoped per engagement

Integration architecture and data flow designAPI connectors and event-driven pipelinesEvaluation framework (offline and online eval)Monitoring dashboards with SLO alertsCost governance and optimisation setupIntegration architecture and data flow designAPI connectors and event-driven pipelinesEvaluation framework (offline and online eval)Monitoring dashboards with SLO alertsCost governance and optimisation setup

Who it's for

Built for teams that need this now

This service was designed around a specific kind of problem. If any of these sound like your team, you're in the right place.

Engineering teams with AI in production

Who've shipped AI features but have no visibility into accuracy, cost, or drift

ML platform teams

Building the internal infrastructure to support reliable, observable AI across the organisation

Compliance-sensitive AI teams

Needing documented accuracy metrics, audit trails, and governance for deployed AI systems

Common triggers

Signs you need this

Most teams come to us after one of these moments. Recognise any of them?

You've shipped AI features but have no accuracy metrics or quality monitoring

A model update broke something and you found out from user complaints, not your dashboards

AI API costs are growing unpredictably and you don't know where the spend is going

You can't answer the question: 'how accurate is our AI system right now?'

You need to present AI quality evidence to leadership, clients, or auditors

Recognise two or more of these?

Let's talk - no commitment

100%

Systems with production monitoring

How we deliver

4 phases to production

Every engagement follows a structured delivery process with clear artifacts at each stage - so you always know exactly where you are.

Baseline

Evaluation Design

Metric definitions (accuracy, hallucination, latency, cost)Test set curation and labellingBaseline measurement reportSLO threshold recommendations

Build

Eval & Monitoring Infrastructure

Offline evaluation pipelineOnline sampling and eval in productionMonitoring dashboards (Grafana / custom)CI/CD eval gate integration

Deploy

Production Observability

Live monitoring with alert thresholdsCost attribution by model, team, and featureAnomaly detection and drift alertsSession tracing and audit log system

Iterate

Regression & Improvement

Regression test suite for model and prompt changesMonthly eval report templateModel update checklist and release gateCost optimisation recommendations

Questions