RE-ViVE reconstructs how work actually executed across your bank, using events and relationships already captured by operational systems. Rather than inferring a representative process, it deterministically rebuilds observed execution — and lets the process map emerge from the evidence.
What does the process usually look like?
What happened to this loan, this payment, this customer, and why?
The first question is answered with a picture. The second needs evidence. Banks are judged on the second: by customers, by examiners, and by their own boards. RE-ViVE is built to answer it.
RE-ViVE deterministically reconstructs execution from operational events and their relationships, rather than starting with an assumed process model and trying to fit behavior around it.
The process map is the result of that reconstruction — not the assumption that drives it.
That difference makes individual paths, independent activities, exceptions, changing structures and source-level evidence central to the analysis, not afterthoughts.
Process mining offers many discovery and modeling techniques. RE-ViVE takes a different starting point: operational events and their relationships are used to reconstruct each observed execution deterministically. The resulting maps can be analyzed without relying on a predefined ideal process or a statistically inferred representative path.
In some discovery-centered views, aggregation, filtering and the choice of case perspective can obscure low-frequency behavior or cross-object relationships.
No predefined ideal process. No statistically representative path. The map is a result, not an assumption.
In a banking evaluation reported by RE-ViVE, our team reconstructed more than 100 workflows from operational data. According to the evaluation account, another approach received comparable or more extensive data but had not delivered a completed workflow after approximately three months.
Real operational data, many banking workflows, each verifiable at the source.
About three months in, on comparable or more extensive data.
We'll take a side-by-side on your workflows. Agree the data, the success criteria and the time window up front, then compare completeness, traceability, accuracy and time to a useful answer.
Evaluation reported by RE-ViVE; the bank is not named.
Researchers continue to improve how process analysis represents concurrency, multidimensional context and evolving relationships. RE-ViVE addresses these needs through its deterministic execution foundation, rather than requiring the same discovery-and-abstraction assumptions.
A directly-follows graph can suggest loops when independent activities finish in different orders. RE-ViVE identifies same-level activities and distinguishes parallel work from actual repetition.
For example: sanctions screening and ID verification can finish in either order without either check being repeated.RE-ViVE implementations support dozens of process-context dimensions, not just filters on time or amount. Teams can examine relationships across product, channel, branch, policy, risk and other business perspectives without separately rebuilding the underlying execution for each view.
For example: Product, channel, branch, risk tier, relationship manager, policy version: same data, any view.RE-ViVE captures changing relationships and reconstructs the execution of each period as observed. New activities, retired steps and altered routes can be compared over time without forcing them into a fixed process template.
For example: A new fraud check added in Q2 shows up in Q2, not as noise across the year.The same reconstruction extends to AI agents, tool calls and traces. Where business keys and telemetry exist, agent behaviour links to the loans, cases and payments it touched.
For example: An extraction agent's retries traced to the disputes that missed their deadline.Parallel activities are recognized as peers, context dimensions expose different populations, and period comparisons reveal new or retired steps. None requires inventing a representative execution.
Independent checks, not a false rework loop.
Different analytical perspectives on the same observed executions.
New steps emerge as the operational process changes.
Rare paths are where the risk, the cost and the customer complaints live. RE-ViVE keeps the whole in-scope population and shows exactly what was and wasn't accounted for.
Paths come from identifiable relationships in the records, not from a statistically representative sample.
The source population is checked against the result: what was represented, what was unmatched, what evidence was incomplete.
Go from a pattern to a single request and its activity instances, then open the source system and confirm it.
Banking teams have traced individual transactions and requests surfaced by RE-ViVE back to their underlying operational systems to verify what actually happened.
Whether the record originates in loan origination, core banking, payment processing, or a case-management platform, the investigation can move from the execution insight to the relevant source evidence.
That matters wherever a workflow touches customers, controls, service levels or regulatory obligations. A finding you can trace to the source is a finding you can act on, defend, and fix.
Loan files, disputes, onboarding and payment exceptions are already being handed, step by step, to AI agents working beside people. A dashboard of agent metrics can't tell you whether the loan closed on time. RE-ViVE connects the two: the business execution and the agent execution, on one reconstructed timeline.
How work ran, how paths and structure changed, and which exceptions are new.
Refreshed or real-time views, as fast as source data and deployment allow.
Business steps linked to agent runs, tool calls, retries and traces, wherever telemetry and business keys connect them.
Built in banking. Not limited to it.
These independent publications document challenges in process representation, multidimensional analysis, adoption and AI. They do not evaluate or endorse RE-ViVE; our architecture and deployment results are the evidence for our approach.
Every path, every exception, every agent step, each one traceable to the record behind it.
Set up a side-by-side →