Model and prompt versioning
Every prompt, model choice and configuration versioned and reviewable like code.
From notebook to production
MLOps & AI Deployment
Versioning, evaluation pipelines, deployment, rollback and monitoring for AI systems in production.
What this is
An AI system in production is an operational commitment, not a finished project. Prompts and models change, data shifts and costs drift. We build the operational layer: versioning, automated evaluation on every change, controlled deployment, rollback and monitoring that catches degradation before users report it.
The problem
What this service addresses
If more than one of these is familiar, there is usually a case worth scoping.
- Prompts are edited directly in production with no test and no record.
- A model change improved one case and silently broke five others.
- Nobody noticed quality degrading until customers complained.
- Model spend is a single line on a cloud bill with no attribution.
- There is no way to roll back to the version that worked.
Scope
What we deliver
Every item below is a deliverable you receive, not an activity we perform.
- Prompt and model version control with review before release
- Automated evaluation pipeline run on every change, blocking regressions
- Deployment pipeline with staged rollout and immediate rollback
- Tracing across every model call, tool call and retrieval step
- Cost and latency monitoring attributed per feature and tenant
- Alerting on quality, latency, error-rate and cost thresholds
- Runbook covering incidents, rollback and routine operations
Capabilities
What the system does
Evaluation pipelines
Fixed test sets scored automatically on each change, with regression gates in CI.
Deployment and rollback
Staged rollout, canary traffic and a one-step return to the last known-good version.
Monitoring and observability
End-to-end traces, quality sampling and dashboards per feature.
Cost and latency management
Per-feature attribution, caching, model routing and budget alerting.
Reliability and lifecycle
Fallback chains, provider redundancy, retirement and migration planning.
Applied
Where it is used
Each of these is a bounded job with a measurable result, which is how we prefer to scope a first build.
01
Taking a prototype to production
Wraps a working proof of concept in the evaluation, deployment and monitoring it needs to be relied on.
02
Safe prompt iteration
Lets teams improve prompts frequently without fear, because regressions are caught in CI.
03
Model migration
Moves to a newer or cheaper model with evidence that quality held.
04
Cost reduction
Finds the spend, then applies routing and caching to reduce it measurably.
05
Multi-tenant operations
Per-tenant isolation, quotas and cost attribution for AI features in a SaaS product.
Architecture
How it is put together
An indicative architecture. The final design follows your systems, data boundary and constraints.
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01
Source control
Prompts, configuration, evaluation sets and infrastructure definitions in your repositories.
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02
CI pipeline
Tests, evaluation runs and regression gates on every pull request.
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03
Registry
Versioned prompts and models with the evaluation results attached to each version.
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04
Deployment
Containerised services, staged rollout, canary traffic and instant rollback.
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05
Runtime
Model routing, caching, rate limiting, fallback chains and quota enforcement.
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06
Observability
Tracing, quality sampling, cost and latency dashboards and threshold alerting.
Security & governance
Controls we build in
Access, privacy and oversight are part of the build rather than a later hardening phase.
- No untested prompt or model change reaches production; evaluation gates sit in CI.
- Full change history for prompts, models and configuration, with reviewer recorded.
- Rollback available at all times to the last version that passed evaluation.
- Spend caps and quotas enforced in the runtime, per feature and per tenant.
- Trace retention and access controls defined, with sensitive content handling agreed.
- Incident runbook with named owners and escalation paths.
Delivery
Our approach
How we run the engagement
Discovery
Process walkthroughs, data review, constraints and a written scope you sign off before build.
Architecture
Data model, integration boundaries, hosting and the security model, agreed up front.
Proof of concept
A narrow build on your real data against an agreed quality bar, so the go-ahead is evidence-based.
Development
Two-week increments, each ending in a working deployment and a review.
Testing
Automated coverage on critical paths, evaluation sets for AI behaviour, acceptance testing with your users.
Deployment
Staged rollout into your infrastructure with monitoring and a tested rollback path.
Monitoring & support
Quality, cost and latency tracking after launch, with handover documentation or an ongoing retainer.
Proven work
Products we have built and sold
We are product builders as well as a services team. These are our own commercial platforms, not client references.
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Questions
MLOps & AI Deployment — FAQs
The questions we are asked most often about this service.
We only have one AI feature. Is this overkill?
A lightweight version is not. Version control, an evaluation set and a rollback path are modest work and prevent the most common production failure: a prompt change that silently degrades quality. Full tracing and multi-tenant attribution can wait.
How do you evaluate output that is subjective?
With a fixed set of cases, explicit criteria agreed with your experts, and a mix of automated scoring and sampled human review. The point is comparability between versions, not a perfect absolute score.
Can you reduce our model costs?
Usually, through routing cheaper models at tasks that do not need the strongest one, caching repeated work and trimming context. We measure first, then show the saving against the quality impact.
Does this work with our existing CI/CD?
Yes. Evaluation runs as another pipeline stage in GitHub Actions, GitLab CI or whatever you already use. We avoid introducing a parallel deployment system.
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