Process Intelligence for Artificial Intelligence
Pi Mapper gives enterprises the process intelligence to put AI agents into live operations with confidence: where the opportunity is, how the work should be split between people, agents and existing software, and the evidence that it is safe to go live.
Why AI pilots stall at the edge of the process
Most enterprise AI programmes start with a demo that works and end with an agent that cannot be trusted with real cases. The gap is rarely the model. It is the process context the agent was never given.
Standard operating procedures describe the happy path. Event logs, emails, spreadsheets and the habits of experienced operators describe what really happens: the rework loops, the exceptions resolved by a phone call, the decisions that live in one person's head. An agent grounded in the documented process fails the moment it meets the real one.
Pi Mapper builds the layer in between. We extract how the process actually runs, decide what should change now that agents are an option, package the context an agent needs to make the right call, and prove it works before it touches a customer.
Four offerings, one operational outcome
Each can be bought on its own. Together they take a process from "we think AI could help here" to an agent running in production with the controls a regulated firm expects. Defined scope, a running agent rather than a report.
AI opportunity and process intelligence discovery
We mine your event data and shadow your teams to map the process as it actually runs, then rank where agents would remove the most manual effort at the least risk.
Output: a ranked opportunity map with volumes, variants, effort and risk quantified per candidate.
Process redesign for humans, agents and existing software
We redraw the operating model around three kinds of worker: people agents systems you already own. Every task gets an owner, a handoff and an escalation path.
Output: a target operating model with task-level ownership, controls and a build sequence.
Context collation
We gather the policies, decision rules, exception precedents and tribal knowledge an agent needs, resolve the contradictions between them, and package it as versioned, testable context.
Output: a governed context pack your engineering team or vendor can load directly.
Go-live assurance
We define what "good" means for the agent, build the evaluation set from real historical cases, set the thresholds, and run the gates that give risk, compliance and operations the confidence to sign off.
Output: a go-live decision backed by evidence, plus the monitoring that keeps it true.
How an engagement runs
A typical first engagement takes one process from discovery to a live agent, run as four connected stages. Pace flows with scope and with how quickly we can get read access to your systems.
Discover
Event log extraction, team shadowing, variant and exception analysis, opportunity ranking.
Redesign
Target operating model across people, agents and existing systems. Controls and escalation designed in.
Collate context
Rules, precedents and knowledge captured, reconciled and versioned into an agent-ready context pack.
Assure and go live
Evaluation set built from real cases, gates run, monitoring in place, controlled release.
Who we work with
Operations, transformation and risk leaders in regulated enterprises who need AI to work inside real controls.
Sectors
Tier 1 and challenger banks, specialist lenders, insurers, and the shared service functions that support them: finance operations, HR operations, customer operations, claims and underwriting.
Typical sponsors
Chief Operating Officers, Heads of Operations, Transformation Directors, Heads of Automation, and Chief Risk Officers who need a defensible answer to "how do you know it is safe?"
Where we fit
Alongside your platform choices, not instead of them. We work with ARIS, Celonis and SAP Signavio for process mining, and with whatever agent framework or AI vendor you have already selected, recommending where you have not.
Commercials
We work as a managed talent partner: our specialists embed with your team for as long as the process needs them, so the capability that builds the first agent is still there to maintain and extend it. Discovery is the natural way to start.
Insights
Points of view for people responsible for making AI work in operations. Grounded in twenty years of doing this the hard way.
Your SOPs are fiction. Your event log is not.
Why the documented process is the wrong foundation for an agent, and what to ground it in instead.
ReadWhy AI agents fail in month three
The pilot works. Then the exceptions arrive. The missing layer is context, not capability.
ReadThe third operator
Designing work for people, agents and the software you already own, without pretending any of them can do it all.
ReadFounder
Malika Bhatia has spent more than twenty years inside the operations of large financial services firms, on both sides of the transformation table.
Her career runs through IT engineering, internal consulting across group functions, Six Sigma, outsourcing, large ERP programmes, RPA and now AI, with deep expertise in banking, finance and HR operations. Pi Mapper is the consultancy she built around one conviction: agents succeed or fail on the quality of the process intelligence beneath them.
"Every automation wave I have lived through failed for the same reason. We automated the process we had written down, and the real one carried on regardless."
Start a conversation
Tell us about the process you have in mind. We will come back within two working days with an honest view on whether it is a good candidate for an agent, and what discovery would involve.
Prefer email? hello@pimapper.com