MLOps & LLMOps
Version models, prompts, retrieval settings and evaluation sets. Run checks before releases and observe quality, latency and cost after deployment.
Operate / Quality is an ongoing responsibility
MLOps & managed AI services.
Operate AI systems with clear ownership, monitoring and controlled updates. We support model and application releases, evaluation, incident handling and running-cost visibility through an agreed service scope.
Discuss this with usThe opportunity
Keep your AI useful after launch.
Sources change. Providers update models. Traffic and user behaviour evolve. An application that worked at launch needs a way to detect deterioration, investigate failures and release improvements without losing control.
We define what needs to be monitored, what requires action and who is responsible. Model quality, application health and business usefulness are tracked together. A handover or ongoing support arrangement makes the operating boundary explicit.
What we can do.
Managed operation & improvement
Set responsibilities for incidents, source updates, retraining and model changes. Use feedback to prioritise improvements and maintain a clear change history.
AIOps & operational assistance
Apply AI to operational information such as logs, alerts and incident context where it helps. Keep automated remediation bounded by permissions and approval rules.
Tangible outputs
What you receive.
The exact scope is agreed around your workflow. A typical engagement can include:
- An operating model and responsibility map
- Quality, reliability and cost monitoring
- Release, rollback and incident procedures
- An evaluation and improvement backlog
- Agreed support boundaries and handover materials
Illustrative workflow
A knowledge assistant begins missing relevant sources
Detect the quality change → inspect retrieval and source updates → evaluate a fix → approve the release → monitor the result.
An example of how this capability can fit a business process.Define what better looks like.
We agree useful measures before implementation and compare them with the current workflow.
- ↗Quality and reliability over time
- ↗Incident detection and recovery performance
- ↗Running cost against the actual workload
Your questions, answered.
Do you provide ongoing support?
We can agree an ongoing support scope or hand over to your team. Availability, response expectations and responsibilities are set in the engagement.
Can you take over an existing AI system?
We first assess the architecture, access, deployment process and available evaluations, then define what is needed for a responsible transition.
What happens when a model provider changes?
Versioned configuration and representative evaluations help assess the change. The operating plan should include fallback and rollback options where feasible.
Useful before you start
Questions worth exploring.
What does AI implementation really cost?
A useful budget covers the whole workflow: data, integrations, evaluation, people and the cost of keeping it running.
From AI pilot to production: what changes?
A working demonstration is a starting point. A released service also needs integration, ownership and a dependable response to failure.
How to choose your first AI project
Start with work you can observe, data you can access and an outcome you can measure.
A useful next step
Bring us the work
that should work better.
One process, one product idea, or a question about where to start.