Run the after-state, live.
Point AI at the real finance workflows. It finds the discrepancies, builds the booking template, and drafts the email. The operator stays in control and signs off. Everything below runs on synthetic data, right in this page.
The back office, done by the operator and the machine together.
Point AI at the real finance workflows. It finds the discrepancies, builds the booking template, and drafts the email. The operator stays in control and signs off. This is a working demonstration on synthetic data, not a slide.
One concrete process, end to end. Then the same engine across the company.
Pick a department, then a process.
Every process listed below is fully interactive: download the synthetic source files, upload them back, watch the machine run, and review the report, the booking template, and the draft email. A greyed process in each area shows exactly what it takes to map a new one.
Start with Controlling → Bank Reconciliation for the full interactive run.
Interactive simulation on synthetic data. No real client, fund, or counterparty information is used.
From one process to the whole company.
The live demo is one workflow built by hand. The same engine discovers and maps every other workflow automatically, from the mailbox the team already uses, into one shared map.
The live run
A real session: AI reads a mailbox, reconstructs the workflow, and writes it into the shared map.
The chain
| Workflow | Dept | Freq | Auto |
|---|---|---|---|
| Bank Reconciliation | CTL | Monthly | High |
| Distribution Reconciliation | CTL | Event | Wiring |
| NAV Pack Preparation | FA | Quarterly | Medium |
| Audit PBC Response | TAX | Annual | Medium |
| Investor Reporting | IR | Quarterly | High |
Illustrative map. The discovery method itself is proprietary and is not shown here.
Connect the account once: Monday.com plus Outlook via the AI connector. A discovery pass then maps that workflow automatically from the existing mail and writes it into the shared map. No new software for the team to learn.
The joy is the argument.
This is not a pivot. It is a through-line finally called by its right name. Wherever I have worked, I found the manual repetition and I quietly killed it. That was always my real method; I just have the language for it now, and the tools to do it properly.
For years I felt like someone who loved a sport that did not officially exist. I played it anyway, on the side, in the margins of every job. Now the sport is real and it has a name: AI engineering. I get to do the thing I was always going to do, and this time it counts. I love using AI to build, to automate, to take a slow, human-draining workstream apart and make it quiet and fast. I build infrastructure late at night that nobody asked me to build, because I cannot not do it.
And here is the rare part: I already speak the language of funds, reconciliations, audits, the back office where the real numbers live, and I ship working automation on top of it. Most people have one side or the other. I want to be genuinely excellent at the seam where they meet, not merely adequate.
Working this way feels like standing next to an intelligence amplifier; one I engineered myself rather than bought off a shelf.
The joy is the argument.
I find the manual workflow, map it, then automate it.
I take the hand-run processes that live in spreadsheets, email chains, and one person's head, and turn them into documented, partly automated flows that hold up under audit.
Click any step to see what it became.
This is a real recurring workflow in fund controlling: the cash reconciliation cycle. On the left it is run by hand. Switch to the after view, then click any node to read how the manual step was discovered, mapped, and where it could be automated.
Open the parts that matter to you.
Two short, expandable explainers. The first contrasts the manual cycle with the automated one. The second walks the chain that turns a discovered map into a running tool. The discovery method itself stays proprietary; only the shape is shown.
01 Before and after on the same process
The same reconciliation, run two ways. In the manual cycle, one person pulls each source file, matches in a spreadsheet, chases open items over email, fixes the exceptions, and signs off by hand. The status of the work lives in an inbox, not in any system, and the loop reworks itself whenever a number moves.
In the automated cycle, the files are ingested on a schedule, the routine matching runs as a deterministic rule, only the breaks are surfaced, and the booking template and the email are generated for the operator to review. The person moves from lookup to judgement, and the single point of failure is removed because the process is written down.
- - Source files pulled and opened by hand
- - Matching done line by line in a spreadsheet
- - Open items chased over long email threads
- - One person holds the full sequence in their head
- + Files ingested and normalised on a schedule
- + Routine matching runs as a deterministic rule
- + Only the exceptions reach the operator
- + The map is documented; bus-factor of one removed
02 How the after-map gets built
An automated flow is only as good as the map underneath it, and most finance workflows have never been written down. The after-map is recovered from artifacts the team already produces, then turned into a diagram the business can read and a machine can run. The discovery method is proprietary and is not shown; the chain below is the shape, not the recipe.
Start from the mailbox the team already uses. A mapping pass reconstructs the real steps and handoffs and writes them into one shared map. From that map the concrete steps are extracted, and only then is the automation built against it. Each process is connected once, with the owner's consent, and no new software is forced on the team.
What I reach for, and why it fits.
There are dozens of process-mining tools. Most assume a full event log, admin rights, and a six-figure budget. A real finance desk has none of those. These three approaches earn their place because they work inside the constraints.
Compressed current-state assessment
Talk to each person on a consistent five-column lens, then map what they actually do against what the system thinks they do.
Why it fits: needs no prior documentation and no software install. Works when the only event log is people's memory.
Hand-over-of-work mapping
Read recurring email threads and shared files by counterparty and subject to recover who really hands off to whom, no surveillance agent required.
Why it fits: the one quantitative artifact you can build from data that already exists, in each person's own scope.
Spreadsheet step extraction
The query steps inside recurring workbooks are machine-readable documentation of the real data flow. Read them and you get the lineage for free.
Why it fits: the highest-yield evidence in a finance environment, hidden in the files people already maintain.
- Enterprise mining suites. They need an admin connector and a budget a single desk does not have, and they only see the 30 to 50% of work inside the core system.
- Desktop task-mining agents. Practitioners report them as fragile, intrusive, and hard to draw insight from. Wrong fit for a small, trust-sensitive team.
- Pure log-based algorithms. Elegant, but they assume a clean event log that most finance processes simply do not produce yet.
I optimise for value the desk feels, at effort the desk can carry.
The top-left quadrant is where a finance team gets real workflow improvement without a procurement cycle or an IT mandate. That is where I work.
Systems already doing the work.
Patterns built on top of a fund-controlling desk and reused across entities. Each one runs in production today; here is what it does and how it works.
Automated regulatory notes
order-of-magnitudeReads a trial balance, identifies the entity type, and drafts disclosure notes in the firm's chosen style: tables, movement schedules, narrative.
Annual accounts quality review
pre-auditTakes the accounts as PDF and the source reconciliation as Excel, checks every cross-reference between statements and notes, and flags inconsistencies before the auditor finds them.
Share register anonymisation
hours to minutesAggregates raw share-register data into net positions per shareholder per class, applies rule-based redaction, and produces print-ready Excel and PDF for KYC and AML reviews.
Mailbox intelligence
dailyTriages email and meeting-transcript backlogs into a prioritised morning briefing: unanswered messages, follow-ups, and half-finished commitments by workstream.
If any of this maps to a process on your desk, say hi.
Happy to talk about workflow discovery, process mapping, and where automation actually holds up in a regulated finance environment.