Service / 04 Operate
Evaluation, Guardrails & Observability
Know how your AI systems behave: test suites, runtime guardrails, tracing and quality monitoring.
/ 01The business problem
Why this matters
Teams ship AI features and then learn about failures from customers. Model updates change behaviour silently, prompts drift and costs spike without explanation.
Evaluation and observability make AI behaviour visible: what good looks like, whether it is still true today and what happened when it was not.
Who it is for
- Teams with AI features already in production
- Platform teams setting AI engineering standards
- Risk teams needing evidence of control
/ 02Scope
What the engagement covers
- 01Evaluation dataset design and labelling
- 02Automated scoring and regression testing
- 03Input and output guardrails
- 04Tracing, logging and cost monitoring
- 05Quality dashboards and alerting
- 06Incident review process
/ 03Technical approach
How we work on it
Define good explicitly
We agree measurable quality criteria with domain experts and turn them into tests.
Combine methods
Deterministic checks, model-graded scoring and human review each do what they are best at.
Close the loop
Production failures become new test cases so the same problem is caught next time.
/ 04Process
From first call to production
- 1
Consultation
A focused conversation about the problem, constraints and what success would mean.
- 2
Discovery
Evidence gathering: data samples, process walkthroughs and a measured baseline.
- 3
Proposal
Scope, approach, team, timeline, risks and a fixed or estimated price.
- 4
Delivery
Iterative sprints with demos and evaluation results at each milestone.
- 5
Handover or run
Documentation, runbooks and training, or ongoing operation by our team.
/ 05Deliverables
What you receive
- Evaluation framework and datasets
- CI-integrated regression tests
- Guardrail configuration
- Tracing and monitoring setup
- Quality and cost dashboards
- Incident response playbook
Engagement options
Fixed-scope sprint
Two to six weeks with a defined deliverable and price, suitable for discovery, assessments and prototypes.
Ongoing partnership
A monthly capacity agreement for continuous improvement, operations and support with agreed response times.
/ 06FAQ
Common questions
Can a model reliably grade another model?
For some criteria, when calibrated against human judgement on a sample. We measure agreement before trusting automated grading.
Which observability tools do you use?
We work with open standards such as OpenTelemetry and integrate with tools you already run where possible.
Related solutions
- SolutionCustomer Support & CXFaster, more consistent service: AI triage, agent assist and self-service that hands over cleanly.
- SolutionInternal Knowledge SearchAnswers from policies, documentation and past work, with citations and permissions intact.
- SolutionIT & EngineeringService desk automation, incident assistance and engineering knowledge, connected to your tooling.
Related product concepts
Talk to an engineer about evaluation, guardrails & observability
Bring the problem and any constraints. You will leave the conversation with an honest view of the options, even if the answer is not us.