Industries

Where agents earn their keep.

Two decades of building financial and operational data systems taught us where the leverage lives. Four domains — expense agents, RevOps agents, AP agents, cash agents — where prepared data plus a working agent layer changes how a company operates.

The ledger

04 domains · one sequence

Finance & Expense

AI agents for expense review

What could an expense agent do with clean spend data?

ERP · Corporate cards · Ledgers · Receipts

The data problem

Spend truth is split across the ERP, the card feeds, and whatever the receipts became. Review happens quarterly, in retrospect, by people hired to do better things.

What we build

One spend model — ledgers, cards, and ERP finally speaking the same language — with AI agents for expense that read every transaction against policy as it lands.

What changes

Continuous expense review instead of quarterly audits. Policy drift, anomalies, and forming spend patterns surface the moment they appear, not months later.

Revenue Operations

AI agents for revenue operations

What if a RevOps agent kept pipeline analysis from going stale?

CRM · Billing · Product usage

The data problem

Pipeline analysis goes stale the week it ships. CRM, billing, and product usage each keep their own version of the funnel — and their own definition of every metric.

What we build

A revenue data model with one definition of every metric, and AI agents for revenue operations that watch the funnel end to end on top of it — CRM, billing, and usage in one line of sight.

What changes

Your team gets briefed on what changed and why, while it still matters. Analysis stops being a quarterly artifact and becomes a standing capability.

Procurement & Travel

AI agents for accounts payable

Who reads every invoice and itinerary? An AP agent can.

Invoices · Contracts · Itineraries · Bookings · Accounts payable

The data problem

Nobody reads every invoice and itinerary. Compliance checks and contract-versus-invoice reconciliation run as sampling exercises, because the documents were never processed into data.

What we build

Document processing that turns invoices, contracts, and bookings into structured records — then AI agents for accounts payable that check every one of them, not a sample. The same foundation that put a production invoice agent into a Fortune 100 fintech workflow.

What changes

Sampling becomes continuous coverage. Every invoice reconciled against its contract, every booking read against policy, exceptions routed to a human.

Cash Flow & Treasury

AI agents for cash flow

What would a cash agent do with a live view of your position?

Banks · AR · AP

The data problem

The cash position lives in a spreadsheet assembled by hand — accurate the day it was built, aging by the time it is read. Scenario planning waits on the rebuild.

What we build

Unified financial data across banks, AR, and AP — that part is on us — with AI agents for cash flow that assemble the cash picture daily and model payment-term scenarios on demand.

What changes

A live view of cash instead of a monthly reconstruction. Agents escalate only when something needs a human decision; the rest is quietly handled.

The pattern

Different domain. Same sequence.

None of these use cases start with a model. They start with data engineering — the same four moves in every domain, whatever the systems involved. That work is the whole difference between a demo and a platform.

How we engage →The platform anatomy →

Unify

Every silo — ERP, CRM, banks, documents — into one estate.

Process

Raw records become reliable facts. Pipelines, not exports.

Govern

One definition of every metric, legible to machines.

Build

The agent layer on top — tools, guardrails, evaluation.

The sequence is documented in practice: at a Fortune 100 fintech, one month of foundation work put an invoice agent into production in two weeks — after seven months of agent-first attempts had stalled.

Read the full account →

Not listed?

Your domain isn’t here. The pattern still applies.

Unify the data, build the platform, put agents to work — the sequence holds in any domain. Tell us what you’re operating and we’ll tell you what’s possible.

Talk to us

Engineering answer · No slideware