AGENTIC AI DISCOVERY AND PROCESS MINING
Process discovery and automation assessment
We use process evidence to identify where agents can help, producing a ranked opportunity backlog and a clear case for what to build first.
- Real process flow reconstructed from your own system event data
- An agent-fit test that filters poor candidates before any build
- A ranked, evidence-backed shortlist and a recommended first build
The pilot gets chosen in a workshop, not from data.
A department head is convinced their reconciliation workflow is the perfect agent pilot. Procurement wants invoice matching. Support wants ticket triage. Everyone is partly right and mostly guessing.
Nobody has the event data to say which process actually carries the volume, the rework, and the clear success criteria an agent needs. So the pilot sounds strategic, demos well, and then meets reality: the process turns out to be low-volume, the rules are actually a hundred edge cases, ground-truth outcomes are ambiguous, and six weeks in nobody can tell whether the agent is working. The failure was upstream, in choosing a process no amount of engineering could have made a good fit. This is where the Agentic AI practice starts.
Replace guessing with evidence, and a shortlist you can defend.
A mined view of every candidate, and a first build you can trust.
- A mined view of each candidate process: real flow, variants, and volumes from your own event data.
- A scored, ranked shortlist of agent-fit opportunities with the evidence behind each ranking.
- A deterministic-automation-versus-agent split, so fixed-rule work is routed to the simpler tool and agents are reserved for judgment.
- A recommended first build: the process most likely to deliver value with the fewest surprises.
- A value case per candidate: volume, cycle time, and effort baselines to measure the pilot against.
Five steps, from raw system events to a defensible shortlist.
Extract event data
From the source systems that touch the process: ERP, CRM, ticketing, and workflow.
Reconstruct the flow
The real variants, loops, and hand-offs, including the ones no process map admits to.
Quantify the economics
Volume, cycle time, rework rate, and where cases wait or bounce between people.
Locate the judgment
The steps where a human reads context and decides, which is where an agent earns its place.
Score and shortlist
Candidates ranked against the agent-fit test, with the evidence attached to each.
Not every painful process suits an agent.
We score each candidate on the dimensions that predict success or failure. Some processes that pass would still be better served by cheaper, deterministic automation.
The distinction that saves the most wasted spend: deterministic automation for fixed rules on structured screens; agents for ambiguity and judgment. A genuinely rules-based process does not need reasoning, and dressing it up as an agent adds cost and failure surface for nothing.
Where discovery leads next.
Multi-Agent System Design
Supervised coordination patterns for the systems discovery says are worth building.
Custom Agent Development
The engineering that turns your first shortlisted process into a working agent.
Agent Platform Architecture and Deployment
The governed runtime the agents you build will run on in production.
AI Enablement and Change Adoption
Getting the teams around the process ready to work alongside agents.
Agentic AI Governance and Risk Management
The limits and controls that decide which candidates are safe to automate.
Agentic AI
The parent practice this discovery work belongs to.
What leaders ask us before discovery.
No. We reconstruct the process from system event data, so the shortlist reflects how work actually flows, not how people remember it flowing. That difference is usually where the surprises hide.
We first test what can be reconstructed from ERP, CRM and ticketing timestamps. Where a source is genuinely dark, we say so and scope a lighter, interview-led assessment rather than pretend the data exists.
No. Discovery is deliberately build-agnostic. A common, and healthy, outcome is that several candidates are better served by deterministic automation or a simple integration, and only one or two justify an agent.
Let's mine your highest-volume workflows and hand you a ranked, evidence-backed shortlist.
Evidence over opinion, an agent-fit test over a workshop, and a first build likely to land.