Flagship service
AI implementation, end to end
We take a defined business problem to a tested, deployed and documented AI-enabled solution, connected to the systems your team already uses. One accountable partner from the first workshop to handover.
How it fits together
An implementation, from request to recorded result
Input
Work arrives
Emails, documents, forms or system events
AI step
AI does the reading
Classifies, extracts, summarises or drafts
Rule / check
Rules check it
Validation, thresholds and business logic
Human approval
People approve
Consequential actions and exceptions
Output
Systems updated
CRM, ERP or records, with a full log
The shape most implementations share. The AI step is usually the smallest part; the integration, rules and approvals are what make it dependable.
When to use it
Problems this service is built for
A costly manual process
A workflow that eats hours of skilled time every week: reading, sorting, re-keying, checking.
An AI pilot that stalled
Something that worked in a demo but never reached production because integration, testing and ownership were missing.
Too many disconnected tools
Information that has to be carried by hand between email, documents, the CRM and the ERP.
Growth without more headcount
Volume is rising, and the team cannot keep doing the same steps by hand at that scale.
Typical use cases
What implementations usually look like
Every project is scoped to your workflow. These are common starting points.
- Document intake: invoices, orders, applications or claims read, validated and recorded
- Inbound request handling: enquiries classified, answered in draft and routed to the right person
- Internal knowledge assistants that answer from approved documents, with sources
- Sales operations: leads structured, qualified and recorded, with replies drafted for approval
- Reporting: data pulled from several systems and summarised on a schedule
Scope
What an implementation can include
What is included in your project is set out in the written scope. Nothing is open-ended.
- Business and workflow discovery, and an AI opportunity assessment
- Technical feasibility analysis and solution architecture
- AI model and platform selection, with the reasons documented
- API and business-system integrations
- Workflow or agent implementation, with permissions and access controls
- Testing and evaluation, error handling and monitoring
- Documentation, handover and team enablement
- An agreed post-launch support period
Security and reliability
Designed for the day something goes wrong
Least access
The solution gets only the permissions its job needs, ideally through accounts you own and control.
Human approval
Actions with consequences, like sending to customers or posting financial records, wait for a person unless you decide otherwise.
Failure handling
Low-confidence results, missing data and system errors are routed to a person, not guessed at.
Logging and monitoring
Every run is logged, so you can see what happened, why, and what it was based on.
Engagement models
Start small, then scale what works
Each engagement is priced against an agreed scope in a written proposal.
AI Opportunity Assessment
Find and prioritise where AI fits. Ends with a roadmap and a recommended first project.
AI Implementation Sprint
One bounded workflow, agent or integration, from design to deployment and handover.
AI Systems Implementation
Several related workflows and integrations delivered as one programme.
AI Operations and Optimisation
Ongoing monitoring, fixes and improvements after launch, on agreed terms.
Technology selection
The right tools for your situation, not ours
We are vendor-neutral. Models, automation platforms and cloud services are chosen against your systems, data requirements, running costs and how easily your team can support them, and the reasoning is written into the design.
Implementation process
Five steps from problem to production
Every project runs through the same steps, scaled to its size. Each step ends with something concrete in your hands.
- 01
Discover
We map the workflow as it runs today: who does what, in which systems, and where the time and errors go. Then we agree which problem is worth solving first.
You getA prioritised list of use cases and a recommended starting point.
- 02
Design
We design the solution around your existing systems: the data flow, the AI steps, the rules, who approves what, and what happens when something fails.
You getA written design with scope, exclusions and acceptance criteria.
- 03
Build and integrate
We build the workflow or agent and connect it to the tools your team already uses, with access limited to what the job needs.
You getA working solution in a test environment.
- 04
Test and deploy
We test against real examples, including the awkward ones, then deploy with logging and monitoring in place.
You getA deployed solution, test results and documentation.
- 05
Adopt and optimise
We train the people who use it, review how it performs, and improve it within the agreed support period.
You getA team that uses the system, and a clear handover.
Questions
Common questions
How big is a typical first project?
What do you need from our side?
Who owns what you build?
What happens if the AI gets something wrong?
How are implementation projects priced?
Ready to take one process from pilot to production?
Tell us about the workflow, the systems involved and what a good outcome looks like. If AI is the right tool, we will show you where to start. If it is not, we will tell you.