Everyday AI use
ChatGPT, Claude, Gemini, meeting note-takers, document summarisers and AI features inside existing software can all help, or quietly create risk, depending on the data, prompt, review and handoff.
Clearer decisions.Safer AI.Less wasted admin.
Before another tool gets added to the pile.
AI automation and data support for businesses, starting with the work worth improving. Explore AI automation consulting.
Founder, Pattrn DataThe work stays human.
The system gets better.
See the Pattrn Protocol
For advice-led, data-heavy and risk-sensitive work.
AS SEEN IN
THE WORK BEFORE THE TOOL
AI is already in the business.
Is the work any clearer?
You do not need another vague transformation pitch. You need to know where time is being wasted, which clients or enquiries need attention, where data is not trusted and where AI can safely help.
The starting point is the outcome you need: clients answered sooner, better enquiries reaching the right person, reports people can trust, and AI use that does not put client data or professional judgement at risk. We work back from that, then decide what is worth changing. Every implementation follows The Pattrn Protocol, my governance framework that keeps AI transparent, auditable, and defensible.
Your team has tried ChatGPT, Copilot or other AI tools, but results vary by person.
Important work still depends on spreadsheets, inboxes, documents and memory.
Reports exist, but decisions still depend on chasing people for context.
Nobody wants another dashboard, agent or automation that gets ignored.
Client or customer work needs judgement, evidence and clear ownership.
You need clarity before committing to licences, suppliers or a build project.
Before we automate anything, we agree what better looks like.
The problem is not usually one broken tool. It is clients waiting, staff repeating admin, reports being questioned, or leaders not knowing what deserves attention next. Pattrn helps you decide what should improve, what should stay human, and what is safe enough to build.
The real question is not “Can this be automated?” It is “Should this be automated, and what needs to stay visible?”
THE TOOLS ARE ALREADY HERE
ChatGPT, Claude, Gemini and Copilot are not the strategy.
Start by finding out which AI tools staff use, which supplier features are enabled and whether Microsoft 365 Copilot is relevant to the work. The question is not which tool is fashionable. It is what work it touches, what data it sees, who checks the output and whether the work is worth improving in the first place.
Microsoft Copilot decisions
Copilot and Copilot Studio need a clear use case, permission model and evidence trail before licences turn into value. Otherwise they become another expensive layer on top of messy work.
Technical and coding agents
For technical teams, the same rules apply to Codex, Claude Code and other developer agents: define the task, protect secrets, review changes and keep humans responsible for judgement and release.
Pattrn's role is to separate useful operational change from tool-chasing, then put the right controls, measurement and human review around whatever AI belongs in the business.
WHERE PATTRN FITS BEST
Built for advice-led, data-heavy and risk-sensitive work.
Pattrn is strongest where client trust, professional judgement, operational handoffs and messy data all meet. The common thread is not the sector label; it is work that needs evidence, ownership and control before AI or automation is added.
Financial Advisers
IFAs, wealth managers and advice firms
Pressure: slow follow-up, evidence gaps, adviser capacity and compliance-sensitive handoffs
Pattrn helps: map the client journey, protect judgement points and build controlled follow-up/reporting support.
Accountants
Accountants, bookkeepers and tax advisers
Pressure: records chasing, deadline bottlenecks, inbox admin and partner capacity
Pattrn helps: make repeated work visible, clean up data flows and put AI-assisted checks where they are safe.
Legal Services
Solicitors, law firms and legal practices
Pressure: intake, document control, supervision, evidence trails and fee-earner admin
Pattrn helps: clarify handoffs, review points and ownership before AI or automation touches client work.
SMEs
Founder-led and operations-heavy businesses
Pressure: manual reporting, scattered systems, unclear ownership and AI use growing without rules
Pattrn helps: find the first piece of work worth improving, then choose the right route: clarity, audit, build or governance.
If your business is document-heavy, advice-led, regulated, operationally messy or already being touched by AI, the same principle applies: find the work worth improving before buying or building more tools. If the issue is a simple task or a one-off setup, I will say so rather than turn it into a bigger project.
THE PATTRN PROTOCOL
Useful AI.
Visible control.
We do not automate just because it is technically possible. We agree what better looks like, then build only what can be owned, checked and improved.
BUSINESS INPUTS01 / Inbox02 / Documents03 / System data
AI ASSISTANCEA useful draft.Sources kept visible
THE HUMAN CHECKPOINTYour judgement.Review · approve · redirect
CONTROLLED OUTPUTA checked action.With an evidence trail
- 01Understand
Start with real work.
Map the people, systems, data and decisions. Find the copying, chasing and repeated checks before recommending a tool.
Work + data + people - 02Draft
Give AI a defined job.
Use AI to help with repeatable work, with clear data boundaries, tests, evidence trails and fallback paths.
A draft, not a decision - 03Check
Keep judgement human.
Make approval points, exceptions and ownership visible. Risky decisions stay reviewed and owned by the business.
Human checkpoint - 04Improve
Act. Measure. Learn.
Approve the action, keep the evidence and measure practical outcomes before making claims about the saving.
Approved action + evidence
Illustrative operating model — not a product demo or a client result.
Explore the frameworkFROM FIRST CONVERSATION TO CONTROLLED CHANGE
How we find the work worth improving
Start small enough to be honest, but serious enough to make the next decision obvious.
Start with one real area of work, team or report
- Use the AI assessment or a discovery call to choose a sensible starting point
- Name the people, systems, data and decisions involved
- Separate genuine pressure from a supplier pitch or fashionable tool
Map what is happening before recommending a tool
- Find copying, chasing, repeated checks, inbox work and spreadsheet workarounds
- Check where data comes from, who trusts it and what happens when it is wrong
- Identify where AI could help and where human judgement must stay in control
Choose the right route
- AI Clarity Session for a specific decision such as Copilot, a supplier pitch or a stuck process
- AI Risk & Efficiency Audit when the business needs a fuller baseline and evidence pack
- Implementation, recovery or governance support when the work is ready to build, repair or control
Build only what can be owned, checked and improved
- Create maps of the work, data sources, risk/effort/value scoring and a pilot plan
- Add approval points, exception handling, logs, tests and fallback paths where needed
- Measure practical outcomes: fewer repeated admin tasks, clearer ownership and fewer good opportunities left waiting
We do not automate just because it is technically possible.
- We will not recommend AI where a simpler fix is better.
- We will not remove human judgement from high-risk decisions.
- We will not connect systems without knowing the source of truth.
- We will not call something done without testing and evidence.
- We will not leave you with a black-box process nobody owns.
- We will not turn the business into an experiment just to prove a tool can do something.
WHAT WE CAN BUILD TOGETHER
Make AI useful by clarifying the work around it first.
Pattrn starts with the business pressure leaders can recognise: slow follow-up, repeated admin, untrusted reporting, unclear ownership and AI use nobody has properly checked. Then we design the controlled systems your team can rely on.
AI Operating Model Design
Decide where AI belongs, where it does not, what data it can use, who checks the work and how the team should rely on it safely.
- Copilot, ChatGPT, Claude and Gemini adoption
- Shadow AI discovery and usage rules
- Human review points and escalation paths
Data-to-Decision Systems
Turn scattered spreadsheets, reports, exports and system data into decision-ready information people can trust and act on.
- Source maps and metric definitions
- Dashboard and reporting layers
- Refresh, ownership and checking routines
AI Help Your Team Can Check
Use AI to help with repeatable work while keeping risky decisions visible, reviewed and owned by the business.
- Approval gates and exception handling
- AI agents and Copilot Studio use cases
- Testing, evidence trails and fallback paths
Business System Integration
Connect the tools your business already depends on so information moves cleanly between people, systems and decisions.
- CRM, finance and document flows
- Forms, inboxes and operational handoffs
- APIs and database/reporting pipelines
Evidence, Risk and Control
Add the evidence trails, approval points, ownership and review checks that make AI, automation and reporting safe to rely on.
- Audit trails and data boundaries
- Governance checklists and responsibilities
- Operational evidence packs
We do not map processes and automate tasks for the sake of it.
We understand how your work, data, decisions, AI tools and human judgement fit together, then design controlled systems that make the business easier to run.
START AT THE RIGHT SIZE
A clear first decision.
Not a bigger tool stack.
Start with a free 30-minute conversation, use a paid Clarity Session when one decision needs written analysis, or move into audit, implementation or governance when the work is broader.
You do not need to choose between a dozen disconnected services before you know what is actually wrong. Start by understanding the work, choose the right level of evidence, then only build what the business can own and check.
Free
AI Assessment + Discovery Call
A low-friction way to identify the first area of work, report or AI decision worth reviewing.
Start with the AI assessment Or book a free conversation01
Find the right first move.
AI Clarity Session
£497
02A focused working session for a Copilot, supplier, data, risk or AI decision that needs a clear next step.
Make the case with evidence.
AI Risk & Efficiency Audit
£5K–£25K+
03A fuller evidence pack for teams that need baseline measurement, opportunity scoring and a defensible business case.
Build something you can own.
Implementation, Recovery & Governance
From £15K / retainer options
Build, repair or govern controlled AI and data systems once the work is clear enough to change safely.
Paid Clarity Session gives a short written recommendation; the free call helps decide fit and scope before anyone pays.
The Pattrn Protocol governance framework is built into implementation and recovery work. Evidence, ownership and review are part of the system, not a separate compliance afterthought.
I will not invent ROI before we know your baseline. Any business case is calculated from your actual process, capacity, risk and revenue data during assessment.
PERSONALLY LED. PRACTICALLY BUILT.NOT A BLACK BOX. A WORKING RELATIONSHIP.
You work with me.
Not a sales handoff.
I'm Rohit. I help financial advisers, accountants, law firms and founder-led SMEs get clearer answers, better-fit enquiries, safer AI use and less wasted admin before another tool gets added to the pile.
I've spent 10+ years in the corporate world watching companies waste millions on AI projects that never delivered results, and often made things worse for the people using them.
I started Pattrn Data because I saw small and medium businesses facing the same problem from a different angle: AI tools were getting easier to buy, but the underlying work, data and decision paths were still unclear.
I believe AI should help humans do better work, not replace them. I lead every project personally, supported by hand-picked specialists I know and trust. Together, we focus on practical systems that respect your people, protect your clients, make the evidence visible and avoid solutions looking for problems.
I've seen too many businesses get burned by tool theatre: impressive demos that fall apart in real use, black-box systems nobody understands, and consultancies that create dependencies instead of capabilities. I built The Pattrn Protocol because I believe AI should be transparent, auditable, and make sense to the humans using it. Not because it sounds impressive, but because that is how you build AI, data and operational changes people can own, check and improve.
I'm UK-based, I understand UK businesses, and I only take on work where I'm confident I can deliver real value, both in pounds saved and in human impact.
More about Rohit & PattrnEVIDENCE BEFORE CLAIMS
Measure the work.
Then talk about the result.
No invented testimonials. No anonymous ROI theatre. If a result is published, it needs a real client, a clear measurement method and approval.
Read the case studies- Time
Where is the team losing capacity?
Intake admin, record chasing, reporting, status updates and repeated handoffs. We measure the drag before claiming the saving.
- Money
Where are enquiries, work or margin being missed?
Slow response, poor follow-up, missed evidence and unclear ownership can all cost money. We separate real missed value from optimistic spreadsheet theatre.
- Risk
What has to stay under human control?
Client data, judgement calls, compliance evidence and approval routes need boundaries. That is why the governance is built in, not added after launch.
ONE REAL PIECE OF WORK. ONE SENSIBLE NEXT STEP.
Before you buy more AI.
Let’s find what’s worth changing.
Start with one team, reporting process or piece of repeated work. We will look at the work, the data, the people involved and the decisions being made, then show you what to improve first.
Practical next steps, not a tool pitch. If AI is not the right fix, I will say so.
You'll speak with me, Rohit, directly. I lead every project personally.