42% Name manual reconciliation as their biggest pain
Not as an irritation. As the thing most responsible for errors and delay in financial reporting.
Financial services
You are too complex to reconcile by hand and too small to justify the platforms that fix it.
That gap is where thirty percent of your finance team's week goes.
Book a 20-minute call Free. No demo, no slides.
54.7%
industry efficiency ratio, meaning that share of revenue is consumed by operating expense.
Source: FDIC Quarterly Banking Profile
What is happening
The core banking system is fine. The gap is everything around it.
The formats never line up
Bank statements that do not match. Part payments. Remittances with no reference. Transfers between your own entities. And ACH, wire, card, RTP and FedNow all running at once.
Enterprise platforms are priced for someone larger
The tools that solve this exist. They are sold to institutions ten times your size, and the licence alone exceeds what the problem costs you — until you count the risk.
Exceptions are tribal knowledge
One person knows why that account always breaks by a few thousand at month end. When they leave, the knowledge does.
Intelligence, plumbed in
A loan file is forty documents. Intelligence turns it into fields.
Underneath that: a connector to the core, one agreed meaning for each field, and a test set of real files we score against.
How we make AI survive real data-
Connectors -
A semantic layer -
Evals you can check
What changes
Judge us on this, not on what we built.
Days to hours Reconciliation that runs before anyone arrives
Matching rules learned from your own history, exceptions ranked by value and age, and a queue rather than a spreadsheet.
Exceptions Breaks explained, not just flagged
With the probable cause attached, drawn from how that account has broken before.
Audit Every match and override traceable
Who matched what, when, and on what basis. The thing auditors actually ask for.
Research
We wrote a full page on each of these, with the sources.
Each one carries its own figures and the citations behind them. Start with whichever sounds most like your week.
However hard, whatever it is
Reconciliation is one example. Bring the one keeping you up.
We wrote this page about matching. If yours is onboarding, collections or a report the regulator keeps rejecting, the method does not change.
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01 We sit with you
Days where the work happens, not a workshop in a meeting room. We watch the job get done and write down the shortcuts nobody wrote down.
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02 We read everything
Your data, your rules, your vendors and their documentation, and the published research on your sector. We report what is actually in there.
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03 We break it to first principles
Not which tool fixes this. What is actually causing it, taken apart until we reach the piece that cannot be divided further.
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04 Then we build
Weeks, not quarters. By this point we are not guessing what to build, and guessing is the thing that makes projects long.
Where we sit
We do not touch your core. We build the matching that happens around it.
4
What you get
- Days to hours Reconciliation that runs before anyone arrives
- Exceptions Breaks explained, not just flagged
- Audit Every match and override traceable
3
Built new for you — none of this exists in your stack today
- A loan file that assembles itself from what you already hold
- A break sheet that names the cause, not just the amount
- A branch tablet that keeps working when the line drops
2
The matching layer Reads your core and your statements. Posts nothing without a maker and a checker.
Connectors, one agreed meaning per field, and a model reading what no
field holds. Accuracy measured on your own records.
1
- Finacle
- Flexcube
- Temenos
- SAP
- Oracle ERP
What you already run — unchanged, and still yours
If your core can print it, we can read it. A green-screen terminal, a nightly fixed-width file, a scanned mandate. We have built against all three.
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No API -
No documentation -
A terminal from 1994 -
It arrives as paper -
The vendor said no -
It reports nothing
Not a list of limits. Name yours on the call.
And once we can reach it, a model can read it. Most of the value here is in the sources nobody ever structured — the note, the letter, the screen.
Who this is for
The people who feel this first
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Chief Financial Officer -
Head of Operations -
Financial Controller -
Head of Internal Audit -
Chief Risk Officer
Straight answers
The questions you would ask on the call
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We already have a reconciliation module in our core system. Why is it not enough?
Core system modules reconcile what is inside the core system. The breaks that cost you sit between systems: the bank statement, the payment gateway, the ledger, the partner file. Each one uses a different format and a different reference. That translation is the work.
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Is this an AI product?
There is machine learning in the matching, but that is an implementation detail rather than the pitch. The value is in handling your specific formats, your specific exceptions and your specific approval chain.
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How do you handle data security?
Financial data stays inside your infrastructure or a region you nominate. Access is role-scoped and logged per record, and we sign whatever data processing agreement your compliance team requires before access.
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Can a model safely touch loan files and core data?
It reads, it never posts. Extractions are scored against files your team already processed. Anything written back waits for a maker and a checker.
Next step
Start with your own numbers.
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We talk
20 minutes. Free.
You tell us what is not working. We ask how the work really gets done.
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We look at your data
A few weeks.
We read your systems, including the notes and letters no field holds. You get what is really in there, what it costs, and the accuracy we can hit.
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We build
A few months.
Only if step 2 says it is worth it. Fixed price, agreed before we start.