Data & Machine Learning
LLMOps, Evals & Observability
Evaluation harnesses, tracing, guardrails, and cost control: the operational layer that keeps AI features trustworthy.
AI features regress silently. A model update, a prompt tweak, or a shift in user behaviour degrades quality and nobody notices until a customer complains. We install the operational layer that prevents that: golden datasets and automated evals in CI, full tracing of every model and tool call, guardrails on input and output, drift and cost dashboards, and alerting tied to quality metrics rather than just uptime.
What's included
- Golden datasets & CI evals
- End-to-end tracing
- Guardrails & red-teaming
- Cost & latency control
What you get out of it
- A quality score that gates deploys, so regressions are caught before release
- Every response traceable to its prompt, context, tool calls, cost, and latency
- Typical 30–60% cost reduction from routing, caching, and prompt hygiene
If you can't measure it, you can't ship it twice
We start by turning your quality expectations into a dataset: real inputs, expected behaviour, and graders: deterministic assertions where possible, model-as-judge where not. That suite runs on every pull request, so a prompt change that improves one case and breaks four never reaches production. In production, tracing links each response back to the exact prompt version, retrieved context, tool calls, tokens, and dollars it consumed.
eval run #418 · prompt v12 · claude-opusaccuracy 0.941 (+0.017 vs v11)groundedness 0.982 (=)refusal_rate 0.004 (-0.002)p95 latency 2.9s (+0.3s)cost / req $0.011 (-38% via prefix cache)gate: PASS · 3 regressions flagged for review
The operational layer
Evaluation harnesses
Golden datasets built from your real traffic, deterministic graders where the answer is checkable, and calibrated model-as-judge scoring where it isn't, wired into CI so quality gates every merge.
Tracing & observability
Every request captured end to end: prompt version, retrieved context, tool calls, tokens, latency, and cost. When something goes wrong you read what happened instead of guessing.
Guardrails & red-teaming
Input and output filters for prompt injection, PII leakage, and off-topic or unsafe responses, plus adversarial testing before launch to find the failure modes your users would have found first.
Prompt & config versioning
Prompts, models, and parameters treated as versioned artifacts with staged rollout and instant rollback, so a change is a deployment you can reason about rather than an edit in a text box.
Cost & latency engineering
Model routing, prompt caching, context trimming, batching, and streaming, all tuned against your own traces, which is where the large, boring savings actually live.
Technologies we work with
- OpenTelemetry
- Langfuse
- Braintrust
- Python
- pytest
- GitHub Actions
- Grafana
- Prometheus
- Postgres
Frequently asked questions
Standard logs tell you a request happened. LLM tracing tells you which prompt version ran, what context was retrieved, which tools were called, what it cost, and how a grader scored it, which is what you need to debug a bad answer.
Smaller than teams expect. 50–200 well-chosen cases covering your real distribution and known failure modes catches most regressions. We grow it from production traffic over time, prioritising the cases that actually went wrong.
Yes. That is the most common way we start. We instrument what you already have, build an eval set from its real traffic, and use the resulting baseline to prioritise what to fix first.
We prefer OpenTelemetry-based tracing so your data stays portable, layered with a specialised LLM observability tool where it earns its place. We will work inside whatever you already run rather than adding another vendor by default.
Next step
Need llmops, evals & observability?
Tell us what you're trying to build. We'll give you an honest read on the approach, the effort, and what it costs to run.