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AI agents for finance teams, with every payment approved

Agents that prepare the numbers your team signs off

Agents match invoices, reconcile accounts and draft close commentary, and a person on your team approves every payment before it goes out.

Book a free consultation How we build agents
ap-desk

  1. AP specialistYour team

    A batch of supplier invoices came in overnight. Can the invoice agent match them before the payment run?

  2. Invoice agentAgent

    1. inbox.read_invoices PDFs and email attachments
    2. erp.get_purchase_orders open orders and receipts
    3. checks.three_way_match invoice, order and receipt

    Most invoices match. One charges more than its purchase order, so it is held until someone decides.

    Needs a personInvoice above its purchase orderPay or hold, with the order attached
    Approved by Finance controller

  3. Ledger agentAgent

    1. erp.post_invoices matched invoices to the ledger
    2. payments.draft_run next scheduled payment run

    Matched invoices are posted and added to the next payment run, with the approval note on file.

Example data. An agent at work in a team channel, with a person approving the step you choose.
Mixam: conversion to a final sale from about 20% to about 40%
Where agents help

Finance teams spend the month chasing numbers.

Agents do the matching and preparation, and your team keeps every sign-off.

  1. 01

    Where it stallsInvoices are keyed in and matched by hand.

    What we do Agents read each invoice, match it to the order and receipt, and post it when the numbers agree.

  2. 02

    Where it stallsThe close stalls on reconciliations and commentary.

    What we do Matching rules run as code, and an agent drafts variance commentary from source data for review.

  3. 03

    Where it stallsSimple data questions wait days for an analyst.

    What we do A data agent answers questions from your ledger in plain language and shows the query behind each answer.

What we build

What we build for finance teams

Whatever comes in, every project is built from the same parts below, set up for your files and your systems.

Finance work we take on

  • Supplier invoices
  • Expense claims
  • Bank reconciliation
  • Month-end close
  • Payment runs
  • Budget questions
  • Audit requests
  • Financial reports

How we build it

  • Invoice and expense processing

    Invoices, receipts and statements read into fields and checked against orders before anything is posted.

  • Reconciliation and close preparation

    Matching rules in tested code, with variance commentary drafted for a controller to review.

  • Finance data questions in plain language

    A text-to-SQL agent answers questions from your ledger and ERP and shows the query it ran.

  • Approval limits and audit trail

    Payment limits and separation of duties enforced in the system, with every action logged.

Not seeing your case? Bring the finance task that eats most of your month and we start from that.

Tell us about your workflow
How we build agents

Agents that work as part of your team

They work in the tools your team already uses, take on whole tasks and stop for a person where you decide.

  1. Routines: agents that start on a schedule, on new GitHub issues or on an API delivery.
  2. Connected systems: GitHub, Linear, Notion, Slack, Jira and other tools an agent may call, each connected with its own sign-in.
  3. Approvals log: each action that waited for a person, with who asked, who decided and when.
Routines: agents that start on a schedule, on new GitHub issues or on an API delivery.
Connected systems: GitHub, Linear, Notion, Slack, Jira and other tools an agent may call, each connected with its own sign-in.
Approvals log: each action that waited for a person, with who asked, who decided and when.

Screens from the workspace our agents run in, with example data.

Industries

Industries we build agents for

Each page shows where agents fit in that industry and what we have built there.

All industries
Engineering in the open

We help build what agentic AI runs on

Open standards, open-source tooling and published research, all public, so you can check our engineering before you hire us.

Shaping agentic AI

Agentic AI Foundation

Vstorm was the first agentic AI engineering company to join the Agentic AI Foundation and received its 2026 Open Agentic AI Innovation Award.

Read the announcement

Open source

We build open-source agent tooling that other engineering teams install

10M+ PyPI downloads
4,000+ GitHub stars
33 public repositories

Framework contributors

When the framework breaks, we fix it at the source

We maintain our own open-source agent framework and contribute fixes to the frameworks our agents run on, through official partnerships with their teams. Breaking changes reach us before they reach your codebase.

Research

Our engineers and PhDs publish peer-reviewed research

Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results.

Track record

Frontier AI teams now build on what we shipped first

Our client agents were in production before most of today's agent frameworks had a stable release. You get a team that already knows what breaks between a promising demo and a system people rely on every day.

Case studies
All case studies
Engagement models

Ways to start

Each path starts with a free call with a founder or senior AI engineer. A build goes ahead only after its Proof of Value meets the agreed threshold.

Start with one workflow

We map one workflow with your experts and agree a success threshold before any build.

Best whenYou want the success test agreed before you commit to a build.

Book a free consultation
  1. Process redesign and build Best whenYou want one team accountable for the outcome.
FAQ

Questions about AI agents for finance teams

What can AI agents for finance teams do? +
AI agents for finance teams do the preparation work behind the numbers. They read invoices and statements, match them to orders and receipts, prepare reconciliations, draft variance commentary and answer data questions from the ledger. Calculations and matching rules stay in tested code. A person on your team approves payments, postings outside the rules and anything that goes into reported figures.
Which finance workflows should we automate with AI agents first? +
Start where volume is high and the rules are clear: accounts payable invoice matching, expense checks, bank reconciliation and recurring data requests from other teams. Pick one, agree an accuracy threshold with your controller and test the agent on last quarter's documents. Month-end commentary and forecasting support usually come second, once your team trusts the matching.
Have you built AI agents for finance teams before? +
We have no published case for a finance team workflow such as invoice matching or the month-end close yet. The closest builds are a claims system for a US health insurer, where processing fell from 3 hours to 8 minutes with a specialist still deciding, and a text-to-SQL agent that answers questions over 473+ columns across 15 database views for a US manufacturer.
How do you stop an agent from getting a figure wrong? +
The agent never does the arithmetic. It reads documents and picks the right records, and tested code calculates and compares the amounts. Where two sources disagree, the item is held for a person. Every reading is linked to its source document and every action is logged, so your auditors can trace each posting back to the invoice, order and approval behind it.
How do you know it works before the full build? +
We agree a success threshold with your team before the build and test the agent on your own cases in a Proof of Value. It measures accuracy and running costs, and the build goes ahead only when the threshold is met. Corrections from your team go back into the test set, so every later version is checked against them.
Who owns the system, and which models does it run on? +
You own the code, prompts, evaluation sets and rules by contract. The agents run in your environment on hosted or open-weight models. The model sits behind an interface in code, so you can change it when accuracy, speed or your hosting rules change, without rebuilding the agent.
Start with one workflow

Test one workflow on your own data

A Proof of Value shows accuracy and running costs before the full build.

Book a free consultationRead case studies
From the blog

Reading on AI agents in finance

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