Automation is only as good as the evidence underneath it.
Stereologic uses a compound AI system to discover and understand real operational work — the business process maps, the data, and the decisions across and within your applications — and turns them into measured cost and capacity, detecting delay and value leakage. It is the context layer AI automation needs before automation decisions are made.
AI recognizes. A person approves. The model then behaves deterministically.
37%
In McKinsey & Company’s global survey “The state of AI in 2026: On the road to ROI” (25 August 2026, n=1,719), 37% of respondents reported that AI had contributed positively to their organization’s EBIT.
Most programmes automate a process nobody measured, so the saving can never be proven. Automation candidates get chosen from opinion, the current state is never captured, and there is no before-state to prove the result against.
Which number are you expected to move, and by when? Most leaders know the number. The harder question is what inside the operation is driving it.
Most automation programmes are constrained less by the automation layer than by the input to it. Robotic process automation, document processing and workflow engines all assume you already know how the work actually runs.
Process documents hold high-level information — the intent, not the execution. They do not show the swivel-chair steps between applications, the rework loops, or the judgement calls people make on screen, which is usually where the time and the capacity go.
Automate the process you actually have, not the one on the wall.
Back-office customer management operations at Pitney Bowes. Figures are drawn from Stereologic’s published Pitney Bowes case study. Read the full story and method.
Operations leaders share what they saw, what changed, and why they chose to work with Stereologic. Roles are given as held at the time of each engagement.
18 years spent on one problem: the work an organization cannot see3 granted patents on the discovery method itself
“AI-powered” on its own tells an evaluator nothing, and running everything through a model is neither cheap nor reliable. Stereologic applies AI deliberately: to the recognition problems where it genuinely outperforms an analyst, on top of proprietary patented algorithms that do the rest. Nothing is hidden inside a black box.
Plenty of tools can tell you an employee spent three hours and fifty-two minutes in the claims application. That does not tell you whether they processed forty claims or one, approved or rejected them, or got stuck. Our compound AI system is aimed at the second question, because that is the one the business is actually asking — and answering it takes several specialised models and algorithms working together, not one general-purpose model.
This is the spine of the granted U.S. patent portfolio: the narrative, the architecture and the intellectual property describe the same thing.
Non-install, application-agnostic recording. Human operational work is observed as it is really performed — no-install, with non-intrusive desktop-recording software and no Administrator-level endpoint access.
AI recognises applications, screens, business data, detailed activities and decisions. A process engineer reviews and approves at defined checkpoints.
Process Intelligence. Approved outputs feed deterministic assembly — complete case traces, combined into end-to-end paths and variants. Where event logs already exist and are usable, Process Mining is layered on to extend coverage into the system-recorded layer.
Deep Task Mining™ and, where event logs exist, Process Mining create the AI-Enabled Operating Model.
Deep Task Mining™ builds it. Where event logs exist and are usable, Process Mining extends it. This is the destination, and it is the reason the three stages exist. Not a report that describes your operation, but a working model of it: the applications and activities, the business data, the cases, the process maps and documentation — assembled into something your teams keep using after the engagement ends.
A client described the goal in their own words: not a diagnostic, but an operating model the business could run on. That is what the six stages are built to produce.
Before AI automates your business, Stereologic understands how it really runs.
This is the part most automation programmes discover late. An agent does not evaluate whether the work it has been given is sensible — it executes it. Point one at a process nobody has measured and you have automated the workarounds, the duplicate checks and the rework, faster and at scale.
Baseline first. Automate second. Stereologic measures how the work is actually performed, so the automation programme is designed against reality and the result can be demonstrated afterwards.
The first three stages produce evidence. The fourth is where the operation actually changes and the saving is realized — that is the point of the exercise.
Capture work as it is really performed across web, desktop, legacy and office applications — with non-intrusive, no-install desktop-recording software and no Administrator-level endpoint access, and without integrating source systems first.
Join fragmented activity into cases, end-to-end processes, handoffs and variants, including the interruptions and workarounds that never reach a system record.
Explain why the operation behaves as it does: where time goes, why the same work takes longer in one team, and what is driving delay, rework and lost capacity.
Change the process, standardize it, remove unnecessary work or automate it — then measure the operation again to confirm the improvement was actually realized.
System records show one layer of the process. Stereologic connects that layer with the human work and the business activity happening inside and between those records.
Compare Stereologic with Celonis, Skan.ai, Mimica and Apromore
A workshop shows you a clean five-minute task. The real operation contains interruptions, other cases, email, spreadsheets, waiting, handoffs and corrections. Optimize the clean task and you save minutes. Reconstruct the real operation and you can explain the week.
Value leakage is not a number you hand to someone else to fix later. Stereologic measures where effort, capacity and customer time are escaping, explains the cause, and supports the change that recovers them.
Measure where operating effort is consumed, recover capacity, and size the opportunity before deciding what to change.
Establish a measured baseline of how the operation runs today, so a transformation programme is designed against reality.
Separate what should be standardized, improved, automated or kept human before more investment goes into AI or automation.
Expose variation, workarounds and undocumented steps, and separate the deviations that exist for a real reason from the ones that do not.
Built for enterprise environments where deployment, data residency and security review matter from day one — including where the AI processing happens.
No Administrator-level endpoint access required.
On-premises or within the approved hosted or cloud environment.
Processing stays within the approved deployment environment.
Security documentation available for procurement and technical teams.
Reduce cost. Improve service. Eliminate errors.
Back-office customer management at Pitney Bowes. Results achieved in under 90 days. Winner, 2014 Process Excellence Award for Best Process Improvement Project under 90 days.
Nationwide retail-banking transformation.
Loan-origination platform stabilization across the branch network. Diagnosed and resolved in under six months. Bank not named at its own request.
Medicare claims processing, onshore and offshore.
3,053 claims across 107 subtypes. Identified opportunities, not realized savings; the 69% applies to active time only, which was 65% of total time.
Bring one operational scope and the number you are being asked to defend. We will show how that work can be observed, reconstructed and measured — and what it would take to recover the loss.
Not ready to meet? See Customer Results