Rohit-led AI automation service

Pattrn Data services

Data Analytics Consulting for AI-Ready Reporting

Data work is not separate from AI work. If the numbers are disputed, duplicated or buried in spreadsheets, AI will just make bad assumptions faster.

Find the drag

Map the process as it actually happens, including handoffs, waiting time, exceptions and duplicated data entry.

Design the guardrails

Decide what can be automated, what needs review, what data is allowed and who owns the output.

Prove the change

Build the smallest useful version, test it against real examples and measure whether it saves time or reduces risk.

Who this service is for

This page is for leadership, operations, finance and client service teams that need reporting they can trust before they automate more work who can see the pressure building. More clients, more admin, more compliance evidence, more systems, and not enough calm time to improve the way work moves through the firm.

Pattrn Data takes a practical route. We look at how your people actually work, where information gets copied, where clients wait, where partners become bottlenecks, and where AI could help without creating new risk.

What the work usually covers

Before tools, prompts or agents, we map the real process and decide what should happen, who owns each step, what data is needed, what should never be automated, and where a person must review the output.

Typical work includes discovery interviews, process mapping, opportunity scoring, supplier and data review, prototype design, implementation support, staff training and governance documentation.

What is safe

  • Start with one named process
  • Keep a human review point for judgement-heavy work
  • Use approved data sources and clear permissions
  • Measure time saved, rework reduced or response time improved

What is not safe

  • Buying tools before mapping the process
  • Putting client data into unapproved AI tools
  • Automating exceptions no one understands yet
  • Treating governance as a policy PDF nobody uses

Useful use cases

Management reporting cleanup

A practical candidate for scoping, testing and governance before wider rollout.

Dashboard redesign

A practical candidate for scoping, testing and governance before wider rollout.

KPI and metric definition

A practical candidate for scoping, testing and governance before wider rollout.

Spreadsheet risk reduction

A practical candidate for scoping, testing and governance before wider rollout.

Client service reporting

A practical candidate for scoping, testing and governance before wider rollout.

AI readiness evidence packs

A practical candidate for scoping, testing and governance before wider rollout.

Questions clients ask

Clear answers before you commit.

How does data analytics consulting support AI?

AI systems need reliable inputs, clear definitions and known data boundaries. Analytics work exposes the gaps before automation starts making decisions from weak data.

Do you build dashboards?

Yes, where a dashboard is actually the right answer. The first job is deciding what decisions the reporting should support and which data can be trusted.

Is this only for large firms?

No. Smaller firms often have the worst spreadsheet sprawl because data work grew informally around busy people.