AI for Finance, Reporting & Operations

The challenge

Adoption is near-universal; impact is not. Around 60% of finance teams are piloting or running AI, yet only about 7% of CFOs report a strong impact. The gap between those two numbers is the problem we solve.

‍Two things explain most stalled projects:

About us

It's the data, not the model.

Most finance teams run on systems never built to talk to each other. Companies that standardise their chart of accounts and ERP first reach full automation in about 6 months; those with fragmented data need 12–18. Data readiness is the single biggest driver of ROI — and the difference between top-quartile returns and bottom-quartile ones.

Trust and control.

In finance, a hallucinated entry isn't a bug — it's a material misstatement. General-purpose models can't simply be pointed at the books. Traceability, explainability, and a clean audit trail are what make automation safe to run in a regulated environment.

What we build

1. Financial operations compliance

Scalable cloud AI built on top of legacy systems, with governance designed in from the start: every action traceable to source data, every decision explainable, every output audit-ready.

2. AI-based automation of business processes

Agentic analysis and support for complex deal flows and the month-end close. We start where the volume and repetition are, then scale toward full workflow automation.

3. AI-based software factory for reporting

A consolidated data layer with agentic access, so non-coder analysts can build their own reports — on top of a governed layer, not around it.

FAQ

We have AI pilots that haven't delivered. How do you get them moving again?

Most stalled pilots have one of two root causes: fragmented data that the model can't work with, or a lack of trust and control that prevents anyone from actually deploying the output. We start with a frank diagnosis — what's stuck and why — then rebuild around the foundation that was missing. Often the original concept is sound; the surrounding scaffolding wasn't there.

Our chart of accounts and ERP setup is fragmented. Do we need to fix that before we start?

No, but you'll get there faster if you sequence it right. Companies that standardise their chart of accounts and ERP first reach full automation in about 6 months; those with fragmented data need 12–18. We typically run the data foundation work in parallel with a contained, high-value pilot — proving value while the cleanup happens, rather than waiting for perfect data before starting.

How do you prevent AI from making errors that end up in our books?

Agentic systems in finance work inside a constrained environment — they read source data and propose actions, but they don't post unreviewed entries. Every action is traceable, every decision explainable, every output audit-ready. The AI does 80% of the work and flags the 20% that needs human judgement, rather than replacing the controls that keep your numbers right.

Do we need to replace our ERP or core systems to use AI in finance?

No. We build on top of what you have. Most of our work sits as a cloud AI layer over existing ERPs and legacy systems — the goal is to make those systems more useful, not to rip and replace. ERP standardisation helps, but it's not a prerequisite to start.

How is this different from buying an off-the-shelf finance AI tool?

Tools solve a known problem with a fixed workflow. We build for the parts of your finance operations that don't fit a standard workflow — your specific deal flow, your specific close process, your specific reporting needs — and for the governance layer that makes any AI in finance defensible to auditors and regulators. We often work alongside off-the-shelf tools rather than against them, integrating them into the broader system.

What about EU AI Act compliance and other regulations?

The EU AI Act applies to most finance-related AI deployments, and we build for it as standard — risk classification, technical documentation, human oversight, transparency. Same for sector-specific frameworks (GDPR, MiFID II, AMLD, and GAMP 5 where life-sciences finance is in scope). Compliance isn't bolted on at the end; it's a constraint we design around from the first sketch.

What's a realistic timeline to see results?

Reporting and variance analysis is the fastest-paying use case — typically 3 to 6 months to value. Month-end close automation usually delivers in 6 to 12 months and can cut a 12-day close to 3. Larger transformations (AP automation, agentic deal flow, full compliance overhaul) run 12 to 18 months. We can scope concrete numbers for your situation in a first conversation.

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