Rohit-led AI automation service

Pattrn Data services

Data Integration Consulting for Automation and AI

When client, finance and operational data sits in separate tools, people become the integration layer. That is slow, risky and a bad base for AI automation.

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 firms with data spread across CRM, finance, case management, practice, inbox, spreadsheet and reporting systems 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

CRM and finance data joins

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

Client onboarding data flows

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

Reporting data pipelines

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

API and low-code integrations

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

System handoff cleanup

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

AI knowledge-source preparation

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

Questions clients ask

Clear answers before you commit.

What is data integration consulting?

It is the work of deciding which systems should talk to each other, what data should move, who owns it, and what controls stop errors spreading.

Is data integration the same as automation?

No. Integration makes data move reliably between systems. Automation uses that movement to reduce manual work. The two need to be designed together.

Can you work with messy existing systems?

Yes. Most useful projects start with imperfect systems. The point is to reduce the fragile manual bridges, not pretend the stack is clean.