AI Consulting for Finance Teams

Modern document AI reads structured and semi-structured invoices at a level where the practical constraint is no longer whether it can read the field, but whether you can prove it read it correctly. That is why we never present extraction as a black box: every field carries a confidence score, and you set the threshold above which an item flows through untouched and below which it lands in a review queue.

When extraction is wrong, three things happen by design. The item is caught by validation rules — totals must foot, tax must reconcile, the supplier must exist, the match against PO and receipt must hold — before it ever reaches a posting. The correction your team makes is captured as training signal. And the whole event stays in the audit trail. We also refuse to quote a headline accuracy number before seeing your documents; the honest approach is to measure on a sample of your worst invoices during the proof of concept and let that number decide the threshold.

For every automated action we record the source document (stored and linked, not just referenced), the values extracted and the confidence for each, the model and version that produced them, the rules applied, any human intervention with who and when, and the final posting reference in your accounting system. The chain runs from the PDF an accountant could open to the line in the ledger, in both directions.

This is not an optional extra we add if asked. It is the reason finance automation is defensible at all, and it is scoped into the build from the first design session — including retention that matches your own document retention policy.

The architecture is deliberately system-agnostic: AI services run alongside your ERP rather than inside it, and the ERP stays the system of record. Integration uses whatever interface your platform realistically offers — documented APIs where they exist, file-based import and export, database-level integration, or a service layer we build in front of it. We have connected AI systems to modern cloud platforms and to legacy installations that offer little more than a scheduled file drop.

Two honest caveats. First, we make no claims of vendor partnerships or certified connectors — we build integrations, we do not resell them. Second, the integration path is assessed early, during discovery, because in finance projects the model is rarely the hard part and the write-back usually is. If your system genuinely cannot be integrated safely, you will hear it before you commit to a build, not after.

No, and we would rather lose a deal than pretend otherwise. What AI removes is keying, matching, chasing, and reassembling — the mechanical portion of finance work. What stays entirely human is judgment: how an unusual transaction should be treated, whether an accrual estimate is reasonable, what a variance actually means for the business, and whether a number should be signed off.

In practice the roles shift rather than disappear. An AP clerk stops typing invoices and starts handling the exceptions the system could not resolve — which is harder, more interesting work. A controller stops rebuilding a report and starts interrogating it. Teams that grow transaction volume without growing headcount get there this way. If your objective is a headcount cut rather than a capacity gain, say so during the assessment, because that changes what we would recommend and how honestly we can predict it.

They go to a person, quickly and with context — and that is the design goal, not a fallback. A good finance AI system is judged by the quality of its exception queue: how short it is, how well each item is explained, and whether the proposed resolution is usually right.

Concretely, an exception arrives with the source document, what the system understood, why it stopped (confidence below threshold, no matching PO, quantity variance outside tolerance, duplicate suspicion), and a ranked set of candidate resolutions. The reviewer confirms or corrects in one screen, and that decision becomes training data, so the categories of exception that recur shrink over time. Truly novel cases — a new supplier arrangement, a restructuring, an unusual contract — will always route to a human, and should.

Meaningfully, but not because of one big win. Closes are slow because of accumulated small dependencies: reconciliations that cannot start until postings land, accruals waiting on invoices that have not been coded, intercompany differences discovered late, and commentary written from scratch at the end.

AI attacks several of those at once. Invoices coded continuously through the month mean fewer unposted items at cut-off. Reconciliations run daily rather than in a close-week scramble. Anomalous balances surface before the close instead of during it. Variance commentary is drafted from the underlying data for controllers to edit. We start by mapping your close calendar and identifying which steps are actually on the critical path — occasionally the answer is that a process change buys more days than a model would, and we say so. Any figure we would quote before that mapping would be invented, and we do not do that.

As a German company we hold ourselves to European data-protection standards (GDPR) by default, for every client worldwide, and financial records get the strictest handling we offer. During development we work with anonymized, pseudonymized, or sampled data wherever the use case allows. Deployment can run entirely inside your perimeter — on-premises or in your private cloud — including private hosted LLMs, so an assistant over your accounting manual and board packs never sends a document to a third-party service.

Everything is covered by data processing agreements, access is limited to the engineers on your project, and where a managed service would genuinely be the better engineering choice we say so explicitly and map exactly what data would leave your control, so you decide with full information.

Start with one process, and in most finance functions that process is accounts payable. It has the properties that make a first project succeed: high volume, so the effect is visible within weeks; a structured output, so accuracy is measurable rather than a matter of opinion; an obvious baseline you already track, such as invoices processed per person per day and cost per invoice; and a contained blast radius, because a flagged invoice is a delay, not a misstatement.

It also builds the foundations the rest of the roadmap needs — document handling, the confidence-and-review pattern, the audit trail, and the integration path into your ERP. Reconciliation, close automation, and forecasting reuse all of that. The exception is a team with an acute pain elsewhere: if your cash forecast is the thing keeping the CFO awake, we start there instead.

In our experience auditors ask a consistent set of questions, and the system should answer all of them without a special project. What controls exist over the automated process, and who owns them. How completeness and accuracy are assured — the validation rules, the confidence thresholds, and the evidence that they work. Where human review sits and how segregation of duties is preserved, so the person who reviews an exception is not the person who approves the payment. How changes to the model or rules are controlled and documented. And whether an individual posting can be traced back to its source document.

We build so those answers exist as records rather than assertions, and we deliver process documentation alongside the software. One boundary we state plainly: we are AI engineers, not auditors, and we do not opine on the sufficiency of your control environment. We build the controls and the evidence your auditors and internal audit function will ask about, and we are happy to sit in the room when they do.

Usually yes, and more than teams expect. Cash-flow forecasting needs your invoice and payment history — issue dates, terms, actual settlement dates, customer and supplier identity — which every accounting system already holds. Duplicate and anomaly detection needs transaction history, which you also have. A few years of ledger data is typically enough for a credible first model.

The real work is rarely volume; it is connection and consistency — linking supplier records that exist under three spellings, or reconciling a chart of accounts that was restructured two years ago. We assess exactly that during discovery and tell you honestly whether the data supports the use case. If the foundation needs work first, we scope that instead of building a model on sand.