What AI is actually good at in finance
There is a great deal of noise about AI in finance, and most of it is unhelpful. The useful applications are specific. Language models are good at reading unstructured documents and turning them into structured data, at drafting narrative from numbers, at classifying things according to examples, and at answering questions when given the right context. They are not good at arithmetic, at being right every time, or at knowing when they are wrong.
So the right design pairs the model with the systems that are good at those things: a database that holds the numbers, deterministic code that does the calculations, and a review step where a person confirms anything consequential. We build to that pattern.
Finance workflows we automate with AI
Our AI engagements start from a finance process that is slow, repetitive or dependent on reading documents, and automate it end to end.
- Supplier invoice and receipt processing: extract, code and match to purchase orders, with confidence scores and review queues
- Transaction coding and bookkeeping: classify bank and card transactions against the chart of accounts from historical examples, with exceptions routed to a person
- Variance and performance commentary: first drafts of month-end narrative from the data, for a finance manager to edit rather than write from scratch
- Client and stakeholder queries: assistants that answer questions about the accounts using governed data, with citations back to the source
- Contract and document review: extract key terms, dates and obligations from leases, loan agreements and customer contracts into structured records
- Finance agents: multi-step automations that gather data, prepare a journal or a report, and present it for approval before anything is posted
- AI bookkeeping for specific sectors, such as our own Subready product for CIS subcontractors
Governance is not optional
Finance has a low tolerance for error and a high requirement for explainability. Any AI system that touches the accounts needs to show what it did, why, and from what evidence. We build that in from the start:
- Every AI output is logged with its inputs, so a reviewer or an auditor can trace a decision
- Confidence thresholds decide what is automated and what goes to a person
- Deterministic code does the arithmetic. The model never calculates a total.
- Data stays in your tenant, with model access controlled and documented
- Human review is designed into the workflow, not bolted on
This is the difference between a demo and a system you would let near your ledger.
AI for accountancy firms and finance teams
For accountancy practices, AI changes the economics of bookkeeping, compliance and advisory. Document capture, transaction coding and first-draft reporting can be largely automated, which frees staff for review and client work and makes fixed-fee services profitable at scale.
For in-house finance teams, the same tools remove the repetitive work around the close and give managers a faster first draft of the analysis the board wants. In both cases the goal is the same: people do the judgement, software does the rest.
Why we are credible on this
We are not a technology firm that has discovered finance. We are finance people who build software for a living, including our own AI bookkeeping product and a patent-pending revenue intelligence platform. We know which steps in a finance process can safely be automated, because we have done those steps by hand.
If you are being asked what your finance team should do about AI, we can give you a specific answer, and then build it.
Good questions, straight answers
Is it safe to use AI on financial data?
It can be, with the right architecture. We keep data within your cloud tenant, use enterprise model endpoints with no training on your data, log every input and output, and keep a person in the loop for anything that affects the accounts. We will also tell you plainly where AI is not yet reliable enough to use.
Which AI models do you use?
We are model-agnostic and choose based on the task, the data residency requirements and cost. Most client work runs on Azure OpenAI or Anthropic's Claude through enterprise agreements, combined with purpose-built document intelligence services and our own deterministic code for anything numerical.
What does an AI for finance project cost and how long does it take?
A focused automation, such as supplier invoice processing for one entity, is typically a four to eight week project. We start every engagement with a short assessment that identifies the processes with the best return and gives you a fixed price for the first one.
Sectors where this work lands
- Accountancy practices
Automated bookkeeping and document processing, client reporting dashboards and the advisory tooling that lets a practice sell insight rather than hours.
- IFAs and wealth managers
Client reporting, fee and revenue reconciliation, management information and governed AI for advice firms that want their data to work as hard as their advisers.
- SaaS and subscription businesses
SaaS FP&A, MRR and churn analytics, revenue recognition automation and investor reporting for subscription businesses from first funding round to exit.