Process discovery that finds the real bottlenecks without weeks of workshops.

When you've committed to an AI rollout and need to show it is working, Workstuff speaks to the people on the ground and comes back with the use cases worth building and the evidence to measure the change.

Book a demo →See how it works

Trusted by global engineering companies, training firms, and consultancies.

What we hear from AI automation leaders

“We're automating a process before we know how it really runs.”

“
There's a date on the wall for the first agents, and I need to know how 200 reps actually run their deals and where the pipeline leaks before we build any of it. Not the playbook version, the real one. There's one of me, and the calendars alone would take a quarter.
Revenue operations director, enterprise software

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We were about to automate a process off an SOP somebody wrote two years ago. The people doing the job say it isn't how the work actually runs, but nobody had ever asked them.
Operations director, financial services

How it works

Tell Workstuff who to talk to and what to find out. It runs an AI voice interview with each of them and reports back everything it learns.

Workstuff starts from your project: the goal, the documents you already have, and the gaps in what you know. Your methodology gives every interview a consistent structure. Workstuff works out who to ask and what to ask them, learning from each conversation to ask better questions of the next person. Run it from the app, or from Claude/Codex via MCP.

workstuff

> Find out how our claims process really works. Ask everyone in claims ops.
● Planning interviews
  · 180 people across 6 teams
  · 12 questions, voice or text
  · portal at acme.workstuff.ai
● Portal live. Invitations sent to 180 people.
● 148 accounts reconciled into 47 steps, 9 decisions, 12 exceptions.
>

What it becomes

Capture human intelligence from every stakeholder through AI voice interviews. Identify the opportunities and generate structured, client-ready deliverables.

Build your methodology into reusable templates for a standardized way of working across teams and engagements. Workstuff turns what the interviewer learns into deliverables in your own format, carrying the context from discovery through design, delivery and ongoing improvement. Each stage builds on what you already know.

AI Roadmaps and Use Cases

Rank every workflow in the business on how ready it is for AI and how much time it would save, based on what the people doing it told you.

Process Maps and Bottlenecks

Map each process as it runs and expose where it slows down, jams, or breaks, from the people doing it.

Consulting Proposals

Run a deep discovery to surface the real pain points and quantify the value.

Knowledge Bases and Documents

Turn what's in people's heads but never written down into documentation the team can actually use.

Context for Agents

AI agents are good at following a clear process; they cannot invent one. Give an agent the real picture of the work, so it starts on facts instead of assumptions that break it.

An Ontology of the Organization

A digital map of how the client's organization actually works: the people, the processes, the decisions and the challenges.

The solution

An AI interviewer that finds the use cases worth building, and the evidence to measure the change.

Send a link. 100 people talk through how the work really gets done, and the AI probes for detail.
Your questions guide the interviews. Workstuff reconciles every account into one version, and flags where they clash.
When the build team asks how the process runs, you have the answer, with a name attached.

An AI-led interview running on a phone, by voice or text

100 people interviewed in 6 days. No meetings booked.

100People interviewed

6 daysTo a full library

Every answerTraced to who said it

Build-readyA spec, not a slide deck

See which processes are actually ready.

AI agents are good at following a clear process; they cannot invent one. Turn the interviews into a scored view of every process: documented steps, an owner for every decision, and a known list of exceptions, including the work that never made it into the SOP. You know which one to automate first, and which one will bite you.

Process health scored across steps, decisions, exceptions and readiness to automate

And how the work actually gets done.

Not one person's version. Every account of the same process, reconciled into one map. Where people agreed, it is settled. Where they did not, you see both versions rather than the loudest one.

A current-state process map with numbered steps, a loop and a revenue leak

Process steps

What actually happens, in order, including the step that never made it into the SOP.

Decisions

Who decides, on what basis, and who they go and ask when they aren't sure.

Exceptions

The cases that don't follow the rule. The ones that break an agent on its first day.

Pains

Where the work is slow, done twice, or quietly hated by everyone doing it.

Ideas

What the people doing the job would fix first, which nobody has ever asked them.

The difference

The same process, two very different builds.

Build from an SOP somebody wrote two years ago

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Build from what the people doing the job said last week

Discovery quality depends on which analyst you can spare

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Your best analyst's method in every conversation, whoever runs it

Debrief after every workshop and hope the notes are good

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Every conversation comes back structured: the steps, the decisions, the exceptions

Ship the agent and find the edge cases in production

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Know the exceptions before anyone writes a line of it

Send a survey, get a 20% response rate, get charts

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Send a link, get 80% completion, get the story behind the numbers

Every process starts from scratch

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Every process builds on the library you already have

Show the AI program is real: use cases identified, change measured.

Book a demo and we'll walk you through a real capture: the steps, the decisions, and the exceptions that would have broken the build.

Book a demo →