For finance and fintech teams we turn reconciliation, onboarding checks and reporting into monitored, auditable pipelines with the controls a risk team will sign off. On our accounting project the client month closed in four hours instead of three days, and an incoming document took two minutes of manual work instead of twelve.
Teams match transactions across systems in spreadsheets, slowly and with errors that surface late.
Document checks and data entry stall onboarding and frustrate customers at the worst moment.
One-off scripts move data between core systems and break silently when an API changes.
Month-end and regulatory reports are assembled manually under deadline pressure.
Match transactions across PSPs, banks, and ledger automatically with exceptions flagged.
Extract and validate IDs and documents, run checks, and route exceptions to compliance.
Assemble recurring and regulatory reports from source systems with audit trail.
Resolve account and transaction questions with AI, escalating sensitive cases to agents.
per incoming document
Client under NDA. The figures are the client’s own, comparing the periods before and after launch.
This is the project behind the case above, described the way it actually happened rather than as a method. An outsourced accounting firm, 10–15 people, 30–40 clients on the books. The bottleneck was not reconciliation and not the complexity of the accounting — it was that qualified people spent half the day retyping counterparty, amount, VAT and date out of PDFs. The order of the work was set by the money: first the loop that removes the manual entry, then reconciliation, then reporting.
Email, messengers and uploads land in a single queue. A document stops getting lost between channels, which is the failure nobody budgets for.
Details are pulled automatically and the document reaches the ledger without retyping. Twelve minutes became two.
A person no longer looks at everything, only at what the model is unsure of or what disagrees with the contract. Low confidence is not an error, it is a route.
Lines match as statements arrive instead of at the close, so a discrepancy surfaces the day it appears rather than on day three.
Statements go out from the system and the replies land in the same discrepancy queue.
Recurring reports assemble themselves with an audit trail: every figure can be traced back to the documents it came from.
Three months of work, of which most went into two unglamorous things: the exchange with the accounting system, and the source-document formats, which differ for every client. Closing a client month went from three days to four hours. What deliberately stayed with people: the disputed document, the methodology for a non-standard transaction, the conversation with the client, and the signature under the reporting.
One to two weeks. Fixed, and credited in full against the build.
A fixed quote against the scope the audit produced.
On this project. Two to four months is the range across our cases.
Model and infrastructure usage plus maintenance. Moves with volume.
Every automated decision keeps its inputs, its rule version and its timestamp, because here the ability to reconstruct a decision is part of what you are buying.
Payment runs, journal postings and limit changes need a second human approval by design. Automation prepares and validates; it does not release.
Where a rule can be expressed deterministically, it stays a rule. Models are used for reading and drafting, not for the control a regulator will test.
Processing region, retention and access segregation are set per legal entity, because a group structure rarely has one answer.
High volume, clear rules and a saving that is easy to measure. The most common first project in the sector, by a distance.
The matched items were never the problem. Automating the investigation of breaks is where the hours actually are.
Collecting, reading and validating what onboarding requires, with everything below threshold routed to an analyst.
Credit decisions, trading signals and anything with a direct regulatory consequence: we build the plumbing, not the decision.
If the chart of accounts and master data disagree between entities, fix that first — automation multiplies the disagreement.
We do not connect to core banking systems without the vendor supported interface and your risk function on board.
Each page states the workflow, the systems it integrates with, what it costs and when it is the wrong choice.
Reads incoming invoices, checks totals, tax and supplier against the ERP, and posts only what clears the rules.
Pulls figures from the accounting stack into a recurring report, with variance explained in plain language.
Answers questions across your contract base with the document, version and clause attached to every answer.
Every pipeline is observable and idempotent, with full logs, role-based access, and reproducible runs. We document controls and produce evidence your risk and audit teams can sign off.
We don’t propose an off-the-shelf product either. The audit is there precisely to see where your process differs from the ordinary one, and the build is shaped around that difference. And if it turns out a standard tool covers you well enough, we’ll say so plainly — the process map stays with you in any case.
It’s a fair thing to ask, and responsibility matters more here than accuracy. A person answers for it, and the system is built so that they can: a doubtful case is never posted quietly, it goes to a review queue. Low confidence isn’t an error for us, it’s a route. Around 70% clears straight through and a person looks at the rest — and where exactly that line sits is yours to decide.
That’s a common requirement, and a reasonable one. We fit the setup to your data rules: if nothing may leave, the model runs locally inside your own perimeter, and the documents never cross it.
That’s not unusual, and it’s fine. Integrating without an API is the most underestimated line in a quote, which is why integration surface comes first among the four cost drivers, and why we don’t name a fixed price before the audit. Working without an API is perfectly possible — files, exports, email — it simply costs more, and it’s better to know that early. Which is why, fairly often, we build the API for you.
It happens often, and usually for one reason: the pilot was measured on the happy path and the exceptions were left for later — though the exceptions are where most of the work turns out to be. So we look at the share that clears without a person rather than at extraction accuracy, and we agree in advance who handles the rest, and how.
No, that isn’t what this is about. What goes is the retyping, not the people: decisions stay with a person — the disputed document, the non-standard transaction, the conversation with the client, the signature under the reporting. On one accounting project, closing a client month went from three days to four hours, not because anyone was let go, but because a qualified specialist stopped keying in details by hand. The time that frees up most often goes into growth: more clients with the same team, without the costs rising alongside.
It’s a fair question, and sometimes doing it yourselves really is the right answer. We say so when the volume doesn’t justify a build: below roughly 300 documents a month the arithmetic usually doesn’t work. The calculator on this site includes running costs, so you can weigh that up before you ever talk to us.
They do change, and that’s exactly what we build for: the model is a replaceable part here, not the foundation. The source of truth is our own database — the document, the counterparty, the entry. The model is attached to the side, and we update it whenever something better appears; for you that’s planned maintenance, not a rebuild.
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