Financial Process Automation with AI Agents and System Integration

Where the Time Actually Goes

Ask a finance team where the month went and the answer is rarely analysis. It went on exporting from the ERP, fixing the export, matching bank lines to open items, emailing three people about an invoice nobody had coded, and rebuilding the same management pack that was rebuilt the month before.

Very little of that requires judgement. It requires access to the right systems and a set of rules, which is what software is for. The reason it is still done by hand in most companies is not that the tools are missing. It is that nothing connects the tools to each other, so a person becomes the integration layer.

Our work starts at that seam: the handoffs between systems where your team is currently doing the copying.

What We Automate

• Invoice capture and GL coding, including scanned PDFs
• Three-way matching across invoice, purchase order and goods receipt
• Bank reconciliation from CAMT.053 and MT940 statements
• Intercompany reconciliation and elimination
• Accrual, prepayment and depreciation schedules
• Month-end close checklists with live status tracking
• Recurring management and board reporting packs
• AP and AR follow-up correspondence
• FX rate imports and revaluation runs
• First-draft variance commentary for review

What AI Agents Are Actually Good At

An agent is a model that can use tools. It can read a document, query a database, call an API, and decide what to do next based on what it found. In finance that turns out to be useful in a narrow and valuable set of places.

Agents handle unstructured input well. A supplier PDF, a lease contract, a mailbox full of remittance advices: they turn into structured fields without a template for every vendor. They are strong at classification, such as coding a cost to the right account based on how similar costs were coded before, and they are strong at first-pass review, flagging the twelve journal entries worth a human look out of four thousand. They also write a decent first draft of variance commentary when they can see the actual numbers behind the movement.

They are poor at being the system of record. We never let an agent be the only place a number exists, and nothing posts to your ledger without a person approving it.

MCP: Controlled Access to Your Finance Systems

Model Context Protocol is an open standard for connecting AI models to the systems where your data actually lives. Rather than exporting to a spreadsheet and pasting it into a chat window, you run a small server that exposes specific, permissioned access: this warehouse table, that ERP report, these documents. The assistant works through that server and nothing else.

Two things make this worth doing. Access becomes explicit, so you can state exactly what a model may read and what it may never touch, and you can log every request it makes. And the connection is reusable — the same server serves whichever assistant your company settles on, instead of being rebuilt for each new tool.

We build and host MCP servers against ERP systems, data warehouses and document stores, so your finance team can ask questions of live data without a developer sitting in the middle.

Systems We Connect

Automation is mostly an integration problem. We work with the platforms mid-market finance functions actually run on, and we build the connections in code so they can be version-controlled, tested and handed over.

How a Project Runs

We start by watching the work. One or two close cycles, screen shares with the people doing it, and the actual spreadsheets rather than the documented process, because those two are never the same thing.

From that we pick the two or three processes where automation pays back fastest and build those first. Each one runs in parallel with the existing manual process for a full cycle, so you can compare outputs before anything is switched off.

What gets handed over is working software plus the documentation to change it: the integration code, the rules in plain language, and a session with your team on how to adjust both.

Controls, Audit and Data Protection

Automated finance processes have to survive an audit, so we design for that from the start. Every automated action writes a log entry showing what ran, on what input, and what it produced. Connections are read-only by default, and write access is granted per process rather than per system.

Anything that touches the ledger goes through a human approval gate. Where AI models are involved we use deployments configured not to train on your data, and for clients with EU data residency requirements we keep processing inside the relevant region.

If a step cannot be explained to your auditor in one sentence, it does not go into production.

What You End Up With

• A documented map of how your finance processes actually run today
• Automations deployed in your own cloud tenant or infrastructure
• Integrations maintained in version-controlled code, not in a macro on one laptop
• An audit trail covering every automated action
• MCP access to finance data for the tools your team already uses
• A team trained to change the rules without calling us back

You own everything we build. No licence, no lock-in, no per-seat fee on your own automation.