Service / AI
Measure your AI like the rest of your stack.
Evaluation suites, observability, guardrails and red-teaming for AI already in production or about to be, so quality, safety and cost are measured on every change.
[ 01 ]The short answer
What is LLMOps, Evaluation and Guardrails?
LLMOps is the operational discipline for language-model applications: versioned prompts and models, evaluation suites that run on every change, tracing of every request, cost and latency budgets, and guardrails that catch unsafe input and output. We add it to existing AI systems or build it in from the start, including red-team tests for prompt injection and data leakage, so a team can ship model and prompt changes with the confidence it has for ordinary code.
What you get
- Evaluation suite wired into CI
- Tracing and dashboards
- Guardrail service
- Red-team report with fixes
- Model and prompt release process
[ 02 ]What we build
The pieces, built to last.
- 01
Evaluation suites
Golden sets, rubric-graded tests and model-graded checks that run in CI on every prompt or model change.
- 02
Tracing and observability
Every request traced with inputs, retrieved context, tool calls, cost and latency.
- 03
Guardrails
Input and output checks for injection, PII, toxicity and policy, enforced in code.
- 04
Red-teaming
Adversarial suites for jailbreaks, indirect injection and data exfiltration, mapped to OWASP.
- 05
Cost and latency budgets
Alerts when a change raises cost per task or slows responses past a budget.
- 06
Model migration
Swap or upgrade models with side-by-side evaluation instead of a leap of faith.
[ 03 ]Where it pays off
Where it earns its keep.
- 01
AI in regulated workflows
Evidence that the system was tested, monitored and overseen, ready for audit.
- 02
Agent platforms
Trajectory checks that catch an agent taking a wrong turn before a customer does.
- 03
Model upgrades
Move to a new model version in a day, with proof nothing regressed.
- 04
Cost control
Find the prompts and routes that burn money and fix them.
[ 04 ]How we deliver
Four steps, one team, start to finish.
The same people scope it, build it and stay after launch. Timings are typical for a first release; we agree yours after the deep dive.
- 01Week 1
Find the decision
We pick the decision or task worth automating, measure how it is done today and agree what better means in numbers.
- 02Week 1 to 2
Build the eval first
A labelled test set from your own data comes before any prompt or training run, so every change is scored, not argued.
- 03Weeks 2 to 10
Build with guardrails
Models suggest, code decides. Permissions, injection checks, human override and audit logs are part of the first version.
- 04Ongoing
Ship and improve
We launch to real users, watch cost, latency and quality per request, and feed failures back into the eval set.
[ 05 ]Stack
Tools we reach for.
Chosen per project, and we work in yours. Nothing here is a lock-in: you own the code and the accounts.
- Langfuse
- Arize Phoenix
- OpenTelemetry
- Promptfoo
- Ragas
- DeepEval
- Llama Guard
- NeMo Guardrails
- GitHub Actions
[ 06 ]Related work
Built by the same team.
[ 07 ]Further reading
Problems we found, and how we would solve them.
- AI security3 min read
Prompt injection is an architecture problem
ProblemAny agent that reads untrusted content and can act can be steered by instructions hidden in that content, and no prompt or filter reliably separates data from commands.ProposalAssume injection will happen and limit what it can achieve: keep untrusted content away from privileged decisions, give tools least privilege, put consequential actions behind code-level checks, and break the path to exfiltration.Read the proposal - Governance3 min read
The EU AI Act deadlines moved. Build the evidence trail anyway
ProblemTeams read the AI Act delay as a reprieve, while the evidence the Act requires, from risk management to logging and human oversight, takes longer to build than to document.ProposalGenerate compliance evidence as a by-product of engineering: versioned data and models, evaluation in CI, request-level logs, documented human oversight and incident handling, mapped once to the Act, NIST AI RMF and ISO/IEC 42001.Read the proposal - AI agents3 min read
Why AI agent pilots stall before production
ProblemAgent pilots impress in demos and stall before production because nobody can show they work, limit what they can do, see what they did, or overrule them.ProposalDraw a hard decision boundary: the model scores and drafts, deterministic code decides and acts, every step is logged with its reasoning, and a person can overturn any decision in one click.Read the proposal
[ 08 ]FAQ
Questions buyers ask.
Yes. Most engagements start by tracing an existing system for a week, then building the evaluation set from what we see.
With rubrics written by your experts, graded by a model and spot-checked by people, plus exact checks wherever an answer can be verified.
It produces much of the evidence the Act asks for: testing, logging, monitoring and human oversight. See our article on building that evidence trail.
[ 09 ]More AI services
- Custom AI Model DevelopmentA model trained on your data, owned by you.
- LLM Fine-Tuning and DistillationFrontier quality at a fraction of the cost.
- AI Agent DevelopmentAgents that finish the job, inside the rules.
- AI Workflow AutomationHours of back-office work, done in minutes.
- RAG DevelopmentAnswers from your documents, with citations.
- AI Memory and Knowledge GraphsAI that remembers what your organisation learned.
- MCP Server DevelopmentYour product, usable by every AI assistant.
- Voice AI AgentsEvery call answered, qualified and logged.
- Document AI and Intelligent Document ProcessingPaperwork turned into clean, checked data.
- Private LLM DeploymentAI on your infrastructure, data never leaves.
- Predictive ML and ForecastingDecisions made on what will happen next.
One company at a time