A clear path from a useful question to a dependable system
We help you decide what to do first, design the boundary and acceptance criteria, build and deploy the right-sized system, then keep improving it. A service starts with a scope and can stop when the evidence says it should.
Start with an assessmentOne lifecycle, explicit decision gates
- 01
Assess
Map the problem, data, workflow and constraints before choosing a model.
Use-case priority, feasibility and risk list
Continue, collect more data, or do not use AI yet
- 02
Architect
Compare Edge, Local, Cloud or hybrid options against the agreed boundary.
Target architecture, decision record and TCO assumptions
Approve a build scope and acceptance test plan
- 03
Build
Implement the smallest useful system around real data and a named owner.
Pilot or production candidate with acceptance criteria
Review evidence before expanding the scope
- 04
Deploy
Install at the agreed site, connect the existing workflow and document handover.
Versioned release, runbook and rollback path
Accept the release or return to a defined change
- 05
Operate
Monitor health, incidents, resources, integrations and model signals within scope.
Health reports, incident records and review cadence
Change, recover or keep the current baseline
- 06
Improve
Use measured issues and operator feedback to prioritise the next safe change.
Owned improvement backlog with evidence and assumptions
Start a scoped change request or stop
Choose the service that matches your next decision
Assessment
Find out whether the problem, data and workflow are ready for AI.
- Use-case priority, feasibility and risk
- Site/workflow map · readiness scorecard · recommended next step
- A scoped Solution Architecture, a data-preparation task, or no-go
- Pricing and response targets depend on scope, hardware, SLA, site and support window. Examples are structures, not guarantees.
Solution Architecture
Turn a real constraint into an implementable architecture and decision record.
- Architecture, model/hardware decision and TCO assumptions
- Data-flow · decision matrix · evaluation and acceptance plan
- An approved Pilot/Build scope with owner and assumptions
- Pricing and response targets depend on scope, hardware, SLA, site and support window. Examples are structures, not guarantees.
Pilot / Build
Prove the system with real data, human review and acceptance criteria agreed first.
- A test system that can be judged against the agreed method
- Working slice · test result · limitations · handover/rollback plan
- Deploy, revise the scope, or stop based on evidence
- Pricing and response targets depend on scope, hardware, SLA, site and support window. Examples are structures, not guarantees.
Maintenance Agreement
Keep an accepted system observable, recoverable and ready for scoped change.
- Monitoring, incident, patch, drift, backup and report cadence
- Baseline · health report · escalation matrix · improvement backlog
- A separately approved change request or the next review cycle
- Pricing and response targets depend on scope, hardware, SLA, site and support window. Examples are structures, not guarantees.
What is explicit before work moves on
- Data boundary: what stays on site, what telemetry is allowed out, and who approves remote access
- Evidence boundary: test method, conditions, limitations and review date before any result is called proof
- Ownership boundary: named customer owner, decision maker, access responsibility and handover artefacts
- Change boundary: feature work, retraining and hardware changes are scoped change requests, not an automatic promise
Pricing and response targets depend on scope, hardware, SLA, site and support window. Examples are structures, not guarantees.
Not sure which service is right yet?
Start with the problem and its constraints. We can recommend Assessment, Architecture, Pilot or a support-scope review — including when the right answer is to collect data first.
Book the first assessment