Quality and drift monitoring
Dashboards and alerts for answer quality, retrieval accuracy, data drift and user feedback.
Managed AI Operations
Keep your AI accurate, safe and affordable after launch.
We run AI systems in production for you or alongside your team. We monitor quality, drift and cost, keep evaluations running on live traffic, respond to incidents and handle model, prompt and security updates, with a review every month.
The problem
Why AI systems so often degrade after launch.
01
Quality that slips quietly
Answers get worse as data, users and models change. Nobody notices until a customer or a regulator does.
02
Model changes nobody planned for
A provider retires a model or changes its behaviour, and prompts that worked last month start failing.
03
Costs that creep up
Usage grows, prompts get longer and the monthly model bill rises with no one tracking why.
04
No one on call for AI
When the assistant gives a harmful answer or an agent misbehaves at night, there is no runbook and no owner.
What we run
Everything it takes to run AI in production.
The same discipline we apply to cloud and platform operations, adapted for models, prompts and data.
Evaluation in production
Evaluation sets run on samples of live traffic and before every change, so regressions are caught early.
Cost and latency control
Spend tracked per use case, with caching, model routing and prompt changes to keep it within budget.
Incident response
On-call cover, runbooks and root cause reviews for AI incidents, from outages to harmful outputs.
Model, prompt and security updates
Planned upgrades to models and prompts, tested against evaluations, plus security patches and dependency updates.
Monthly reviews
A monthly review of quality, cost, incidents and the business metric, with a plan for the month ahead.
How we deliver
From handover to steady operation.
The same onboarding we use for every managed service: assess, transition, stabilise, optimise.
Always included
- One business metric agreed upfront and reported every week
- A senior squad of two to four people, with a demo every week
- Evaluations, guardrails and security built in from day one
- All code, models and documentation owned by you once invoices are settled
Typical tools
01
Weeks 1 to 2
Assess
We review the AI systems, how they are monitored and what can go wrong.
- Architecture, models, prompts and data sources reviewed
- Current monitoring, evaluations and costs checked
- Risks and gaps ranked with you
02
Weeks 2 to 4
Transition
We take over operations in stages, with your team alongside.
- Monitoring, alerts and dashboards set up
- Runbooks and escalation matrix agreed
- Access, on-call and SLAs in place
03
Weeks 4 to 8
Stabilise
We fix the issues that cause the most incidents and cost.
- Evaluation sets extended to cover live failures
- Top incident causes fixed
- Cost and latency baselines recorded
04
Ongoing
Optimise
Steady improvement, reviewed every month.
- Planned model and prompt upgrades
- Cost and quality improvements
- Monthly review with leadership
Where we apply it
Industries we know well.
Further reading
From the blog.
Enterprise AI Agents: How to Build Them for Production
A practitioner's guide to taking enterprise AI agents from demo to production: scope, architecture, guardrails, evaluation, security and rollout.
Why AI Projects Fail Between Pilot and Production, and How to Fix It
Most AI pilots stall before production for predictable reasons. Here is how to spot them early and design pilots that are built to ship.
FAQ
Questions we hear a lot.
What does managed AI operations include?
Monitoring of answer quality, drift, latency and cost; evaluations run on live traffic and before every change; incident response with on-call cover and runbooks; planned model, prompt and security updates; and a monthly review with your leadership. In short, everything needed to keep an AI system working well after the launch team has moved on.
What is the difference between LLMOps, MLOps and managed AI operations?
MLOps covers the tooling and practices for training, deploying and monitoring machine learning models. LLMOps applies the same ideas to large language models, adding prompts, retrieval and evaluations. Managed AI operations is the service of actually running all of that day to day for you, with people on call and accountable for the result.
What response times do you commit to?
Our SLA targets are a 15 minute response and 4 hour resolution for P1 incidents, and a 30 minute response and 8 hour resolution for P2. Lower priority issues have longer targets, set out in the agreement. We agree the severity definitions with you, so everyone knows what counts as a P1 for your AI system.
Can you run AI systems that another team built?
Yes. We start with an assessment of the system, its monitoring and its evaluations, then take over in stages. If there are no evaluation sets, building them is one of the first things we do, because you cannot run an AI system safely without a way to measure it.
Fully managed or co-managed: which should we choose?
Fully managed suits teams that want the AI system run for them end to end. Co-managed suits teams with their own engineers who want to keep ownership of decisions and have us add monitoring, on-call cover and senior depth. Most clients choose co-managed, and you can move between the two as your team grows.
AI implementation services
Every part of implementation, covered.
AI Readiness Assessment
Use cases ranked, data and systems reviewed, and a costed roadmap in about two weeks.
AI Pilot to Production
One use case taken from proof of concept to a live system people use, in about eight weeks.
AI Integration Services
AI connected to your ERP, CRM, core systems and product, with permissions and audit built in.
AI Adoption and Training
Process redesign, role-based training and an AI usage policy, so people actually use what you build.
Our services
AI strategy, GenAI and agents, data platforms, analytics, MLOps and transformation.
Talk to a founder about Managed AI Operations.
Tell us where you are. We will come back within one business day with a point of view, not a sales deck.