Put AI to work in your business.
Find practical ways to save time and reduce manual work, without treating security, privacy, or accuracy as afterthoughts. We help you choose where to start, test the value, and build a solution your team can use.
From an AI ambition to a working prototype.
Monarch Wealth wanted a fund-research solution fitted to its own rules and operations. We helped evaluate the options, then built the bespoke workflow. It reached user testing in under four months and remained in testing as of 9 September 2026.
See how the Monarch project took shapeAI implementation changes the workflow as well as the software.
Research and review
Bring approved information together, apply relevant criteria, and prepare material a person can check.
Document operations
Assemble review packs, flag missing information, and route exceptions to the responsible person.
Reporting
Prepare recurring drafts from permitted sources and identify discrepancies before release.
Operating capability
Plan integration, user testing, training, and ongoing responsibility alongside the software.
These are examples to explore, not claims of completed projects. The scope depends on your workflow, data rights, systems, and review requirements.
Not every problem needs a custom build.
Some companies spend a year building what they could have configured in a week. Others buy off-the-shelf for a problem only a custom system solves. The right approach depends on the problem — we start there.
Use existing tools
Fastest to deploy. We configure and integrate what already exists, so you’re running in days, not months.
Customise existing solutions
Tailor existing solutions to your data, workflow, and compliance requirements. The middle ground between speed and specificity.
A custom system
For problems no off-the-shelf solution fits. A system built around your workflow. Software rights, licences, and support are agreed in the engagement.
Five steps. An exit ramp at every one.
Gates decide. Phases deliver. You invest in the next stage when the evidence supports it. Deliverables and timing are agreed for the specific engagement.
| Stage | Output to agree | Your contribution | Decision |
|---|---|---|---|
| Qualify | Workflow scope, baseline, and build/buy/no-build view | A process owner, examples, volumes, and constraints | Is there a worthwhile problem and a feasible data path? |
| Prove | Prototype and evaluation of the uncertain parts | Permitted test inputs, reviewers, and acceptance criteria | Does the evidence support proceeding, changing direction, or stopping? |
| Decide | Implementation scope, cost assumptions, and decision record | Sponsor, budget, technology, and control input | Are the benefits, costs, and remaining risks acceptable? |
| Embed | Integration, user testing, training, and operating preparation | Access, user testers, and a rollout owner | Can the workflow be introduced with appropriate controls and support? |
| Handover | Documentation and agreed operating/support responsibilities | Named people to operate and maintain the solution | Is the team ready, or is further support needed? |
A prototype is not a production rollout. Integration, data quality, evaluation, and client availability affect the schedule; we define these dependencies before committing to delivery dates.
Useful AI needs answers you can check.
Where does our information go?
Before using sensitive data, agree permitted sources, processing providers, access, retention, and deployment requirements. Security and privacy decisions shape the design.
Can we trust the answers?
Test representative tasks and difficult cases. Define how reviewers check source facts, what errors are unacceptable, and what happens when the system cannot produce a reliable answer.
Who stays in control?
Name the people who approve changes, review consequential outputs, and handle exceptions. Automation should support the decisions your team owns.
What happens after launch?
Agree operating documentation, training, monitoring, change management, and support. Governance continues when the initial build is finished.
Ask if you’re READY.
Open the READY preflight checklist
What rules apply? Are staff already using AI tools you don’t know about?
Which use cases come first? Can you build them and run them?
Who owns it, who approves it, and who makes the call when it goes wrong?
Where is it, what is its quality, who has access, what is permitted?
Without people who want to use it, AI doesn’t ship. Who champions it, and who resists?
Business, product, engineering, and governance together.
Your process owner and subject-matter reviewers work with our implementation team. We identify the technical, operational, and governance input needed for your project rather than assuming every engagement needs the same team.
Meet the people behind Muuvment