Service — Managed
Managed AI Solutions
AI that stays useful after launch — and stays judged. Every workflow monitored against agreed examples, poor outputs paused and reviewed by a person instead of left running, costs tracked against the work actually completed, and a review report after each cycle saying what changed and what needs your decision.
Engagement shape and timing are agreed per project after assessment; a pilot comes before full deployment, and deployment does not proceed on a pilot that failed its own measures.
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What you get first
Managed AI means the workflow is still doing the job it was built for after launch — and you can see the evidence, cycle by cycle.
1. Workflows that stay watched
Ongoing checks that workflows, integrations, and data flows keep running.
2. Poor outputs paused, not left running
If quality drops or a workflow produces an unsafe or incorrect output, it is paused or narrowed in scope while the cause is found — not left running while a fix is debated.
3. A review report after each cycle
Each cycle ends with a short review report: what was checked, what changed, what it cost, what needs a client decision, and what is recommended next.
What you receive — deliverables
Monitoring & maintenance
Ongoing checks that workflows, integrations, and data flows keep running.
Model & workflow evaluation
Regular quality checks against agreed examples and outcomes.
Cost optimisation
Tracking of model, tool, and usage costs, with options to reduce waste.
Incident handling
A defined process for failures, wrong outputs, and integration breaks.
Agent performance management
Review of accuracy, escalation rates, and human-override patterns.
Security & permission reviews
Periodic review of who and what can access data and take actions.
What we monitor
Managed AI is not a dashboard alone. It is a standing review of whether a workflow still does the job it was built for, safely and at a sensible cost.
Workflow health
Whether each step runs, integrations respond, and data moves between systems as designed — and what changed when it does not.
Output quality
Whether AI drafts, classifications, and extractions stay accurate and useful, judged against agreed examples and human review — not by how fluent they sound.
Human escalation
How often work is escalated, overridden, or corrected, and what those patterns say about instructions, knowledge, or workflow design.
Cost and usage
Model, tool, and usage costs tracked against the work actually completed, so waste — repeated calls, oversized models, unused steps — can be removed.
Data and permissions
Which systems, people, and agents can read, change, or act on data, and whether that access still matches the job.
Change and drift
Changes in your systems, your documents, or your customers' language that quietly make a previously good workflow less reliable.
Taking over an existing workflow
If a workflow is already live — built by us or by another provider — takeover follows three steps, in order:
- Review: access, documentation, and the current workflow, to establish whether a takeover is sensible.
- Agree: the examples, quality bar, and escalation rules the workflow will be judged against.
- Monitor: health, quality, cost, and permissions from the first agreed cycle, with a review report after each cycle.
Model and workflow evaluation, in practice
Evaluation is agreed before it starts, so there is no argument later about what “good” meant:
- Agreed examples: a set of representative cases, including edge cases and cases that must be escalated, reviewed before and after any change.
- Human review: a sample of real outputs reviewed by a person with authority to correct them, with corrections fed back into instructions or knowledge.
- Before-and-after checks: every instruction, model, or integration change is tested against the same examples before it reaches live work.
- Pause first: if quality drops or a workflow produces an unsafe or incorrect output, it is paused or narrowed in scope while the cause is found — not left running while a fix is debated.
No evaluation result is published as a client success figure unless it is measured against an agreed baseline and the client has approved publication.
Incident handling
When something breaks, produces a wrong output, or stops an integration, the process is:
- Detect and contain: identify the affected workflow and, where needed, pause it or route work to a human.
- Diagnose: trace the failure to a step — data, instruction, model behaviour, integration, or permission — rather than treating the symptom.
- Fix and verify: correct the cause, re-test against agreed examples, and confirm with a human review before resuming normal scope.
- Review and learn: record what happened, what was affected, and what changes so it does not recur — including whether instructions, knowledge, or monitoring need to change.
Response steps and responsibilities are agreed per contract. This page does not promise a response time, resolution time, or availability figure.
Continuous improvement
Managed AI follows a repeating cycle, not a one-off tune-up:
Each cycle ends with a short review report: what was checked, what changed, what it cost, what needs a client decision, and what is recommended next. Small, reversible changes are preferred over large, untested redesigns.
Security and permission reviews
At agreed intervals, and whenever a system, team, or integration changes, we review:
- Who — and which agent — can access each system, document set, and action, and whether that access is still needed.
- What data an agent can read, retain, or send outside your systems, and under what approval.
- Where credentials and integrations are stored, and who can change them.
- Whether logging is sufficient to trace an AI action back to the input, instruction, and approval that produced it.
We do not claim a security certification we do not hold. Where a client needs a specific standard or audit, that is scoped as a separate, agreed piece of work.
Support packages — scope, not slogans
Packages differ by scope, not by promises. Exact scope, responsibilities, and response processes are agreed per contract; no package on this page carries a contractual SLA, uptime, or response-time guarantee.
Monitor
Health, quality, cost, and permission monitoring, plus a review report after each agreed cycle and recommendations. Best for a stable workflow that needs watching, not frequent change.
Your commitment: a named approver for the review report and for any change it recommends.
Monitor & Improve
Everything in Monitor, plus agreed improvement cycles, instruction and knowledge updates, and re-testing before changes go live.
Your commitment: as Monitor, plus approval of each improvement cycle before it goes live.
Operate
Everything in Monitor & Improve, plus day-to-day operation, incident handling, and coordination with your system owners when integrations or permissions change.
Your commitment: as Monitor & Improve, plus day-to-day escalation decisions.
What we need from you
- A named approver who can confirm changes and escalations.
- Access to the systems, logs, and knowledge the workflow uses — limited to what the job requires.
- Notice of planned changes to your CRM, documents, or integrations, so workflows can be re-tested before they break.
Who this is for
Companies that already run an AI workflow — built by us or by another provider — and want it monitored, improved, and supported rather than left to drift.
Process
Each stage ends with a decision point: continue, adjust scope, or stop. A pilot comes before full deployment.
Frequently asked questions
Do you offer an SLA?
Support packages and response processes are agreed per contract. No contractual SLA guarantee is published on this site until formally approved.
What if a workflow starts producing poor outputs?
Evaluation flags the change, the workflow is paused or adjusted, and a human reviews affected cases before it resumes.
Can you take over an existing automation?
That depends on access, documentation, and a review of the current workflow. The assessment stage establishes whether a takeover is sensible.
Ready to find the first opportunity?
It is not a guaranteed proposal, price, or implementation commitment.
Before the call, it helps to bring: one repetitive task, who runs it today, and which systems hold the information. Nothing else is required — no document, budget, or technical team.