Managed AI Operations
We build it. Then we keep it working, improving, and expanding.
Custom software and agentic workflows are not static. APIs change. Models change. Processes change. People find new edge cases. Managed AI Operations gives you an ongoing technical and operational layer to monitor and measure what we built, govern what it is allowed to do, handle exceptions, train your team as the process moves, improve performance, and expand the agentic workflows under management as the business evolves.
Your team owns the business decisions. We keep the automation layer healthy.
Monitor
Workflows and integration points
Measure
Exceptions, effort, and outcomes
Improve
Fix, tune, and design out cleanup
Govern
Rules, approvals, and audit trails
Train
Your team and the AI steps
Expand
The next workflow, when justified
Why this exists
Production is the beginning, not the end.
The day a system touches real operations is the day it starts meeting reality. Volumes shift, exceptions appear that nobody described, and the systems around it keep moving underneath it.
A workflow can be technically “running” while quietly handing work back to people — a spreadsheet reappears, someone starts checking a queue every morning, an exception path becomes a habit. That decay is gradual, and it’s usually invisible until it’s expensive.
Vendors change
An API version is retired, a payload gains a field, an auth flow tightens.
Models change
A model is updated or deprecated and the same prompt starts behaving differently.
Rules change
Thresholds move, an approval step is added, a reason code stops being used.
People change
The person who knew the workaround leaves and the workaround leaves with them.
Edge cases arrive
Real usage finds the cases nobody described during the build.
Systems multiply
A new tool enters the stack and expects to be part of the same workflow.
What we manage every month
The layer between your systems, looked after on purpose.
Agent & workflow health
Failed runs, stuck jobs, silent retries, growing queues, and behaviour that has drifted from what was specified.
Integrations & APIs
Authentication changes, version deprecations, altered vendor behaviour, and field mappings that no longer line up.
Exceptions & edge cases
We read the patterns in what gets escalated to a human, and automate the ones that turn out to be repeatable.
AI & model behaviour
Prompt, model, and tool changes where they apply — tested against real cases before production behaviour moves.
Business rules
Thresholds, approvals, reason codes, routing, and policy changes reflected in the system instead of in a side process.
Internal software
Small fixes, forms, reports, and usability refinements to the tools your team uses every day.
Observability & audit
Logs, traces, action histories, and exception visibility so you can see what the system did and why.
Expansion
Once a workflow proves itself, the next one gets built on the same foundation instead of from scratch.
Operating model
Monitor → Measure → Improve → Govern → Train → Expand
The same cycle, every month, against the systems actually in use. It isn’t a ticket queue waiting for you to notice something is wrong.
- 01
Monitor
We watch the workflows and the integration points between systems — not just whether a server is up, but whether the work is actually completing.
- 02
Measure
We review what humans had to intervene in, how often, and what it cost — plus what has changed in the business since the system was built.
- 03
Improve
Failures get fixed, recurring manual cleanup gets designed out, and the routine path gets faster or quieter.
- 04
Govern
Rules, approvals, permissions, and audit trails stay current, so what the system is allowed to do matches what the business has agreed to.
- 05
Train
Your people — and the AI steps themselves — get updated as processes change: new cases, new prompts and tooling, refreshed documentation for the team.
- 06
Expand
When the case is there, the next workflow or system joins the automation layer. When it isn't, we say so.
What stays human
Managed does not mean uncontrolled.
We operate inside rules you agree to, and we surface what falls outside them. The system handles the routine path; your people keep the decisions that deserve a person.
- Judgment calls and anything genuinely ambiguous
- Policy, pricing, and commercial decisions
- Customer and supplier relationships
- High-impact or unusual exceptions
- Approvals that should carry a name against them
- Ownership of the business process itself
Two ways to own the system
You can run it yourself. For production AI systems, we recommend you don’t.
Handoff is a real option and we’ll support it properly. But AI and agentic systems sit on top of vendors, models, and business rules that keep moving, so the systems that stay valuable are the ones somebody is accountable for every month.
Option A · Available
Run it internally
You get the source code, the documentation, and a proper handover. Your team owns operations from there, and we’re available if you want us back later.
- Code and documentation handed over
- Your team monitors and maintains it
- Changes happen on your internal roadmap
- No ongoing commitment to us
- Best when you have in-house capacity to own monitoring and change
Option B · Recommended
Managed AI Operations
The path we recommend for anything running in production. We stay responsible for monitoring, measuring, governing, improving, and expanding the automation layer, so keeping it healthy isn’t added to somebody’s day job.
- We watch the workflows and integration points
- Exceptions get reviewed, not just logged
- Recurring manual cleanup gets designed out
- New workflows join the same foundation
Good fit
This makes sense when the work never stops changing.
The workflow touches more than one system
The process changes several times a year
You don't want internal staff babysitting automations
There are AI or agentic steps that need monitoring and exception review
The value comes from repetitive work staying reliable
There's a roadmap of further workflows worth automating
Not the same as IT support
Different job, sitting next to a good IT team.
Your IT provider keeps the estate running, and that work matters. We look after something else entirely: the custom logic that moves work between your business systems.
Generic IT support
- Laptops, accounts, and passwords
- Endpoint management and patching
- Network and device troubleshooting
- Licensing and standard helpdesk tickets
- Keeping off-the-shelf tools available
Managed AI Operations
- The custom logic between your business systems
- Agents, integrations, and automated workflows
- Exception paths that make the routine path reliable
- Internal software built for how you actually work
- Continuous improvement and expansion of that layer
Commercial model
Monthly scope. Clear responsibilities. No mystery maintenance bucket.
Managed AI Operations is a recurring monthly engagement, scoped to the specific systems and workflows we operate for you. What’s covered is written down, so both sides know what we’re responsible for and what we’re not.
Production builds stay separately scoped and separately priced. The recurring engagement is for operating and improving what already exists — not a way to fund the next build quietly.
Scope is agreed before anything starts, and revisited as the estate grows.
What scope is based on
- Number of workflows, agents, and integrations under management
- How business-critical the workflow is when it stops
- Complexity of the systems it touches
- Monitoring, audit, and reporting requirements
- Expected rate of ongoing change in the process
Where this fits
The last step, and then the one that repeats.
01
AI Profit Scan
A free 20–30 minute conversation about where the business is constrained.
02
AI Profit Diagnostic
Top 3 profit gaps ranked, root cause named, and a recommended first move.
03
Prove it, then build it
Prove the top opportunity where practical, then a fixed-scope production build.
04
Managed AI Operations
Keep it healthy and make it smarter — this page.
If nothing has been built yet, the honest starting point is the beginning of that sequence — request an AI Profit Scan and we’ll find out whether there’s a profit gap worth pursuing first.
After the build
Don’t let the automation become another system your team has to babysit.
We can hand it over. Or we can stay in the loop, monitor the automation layer, improve what we learn from real usage, and keep expanding it as the business changes.