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Replace your SaaS sprawl with apps you own.

Start from 100+ prebuilt finance and banking apps, for the close, reconciliation, cash application, treasury, KYC and more, or build your own in plain English. It's explainable AI in finance: every number is computed, not generated, and traceable to the records behind it.

   
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100+applications, 18 process areas
639agents and automations underneath
0numbers generated by a model
In short

What is explainable AI in finance?

Explainable AI in finance means every figure an AI system shows can be traced to the records and the rule that produced it. In Kognitos Apps, numbers are computed by deterministic steps written in plain English, never generated by a language model, so you can open any number and follow it back to source records.

Why Kognitos apps

AI on top. Every number inspectable underneath.

Inspect everything on screen

Open any number or chart and follow it back to the records behind it: the source, the filters, the calculation and the documents.

Audit-proof by design

The logic is written in plain English, English-as-Code, and runs the same way every time. Every figure is computed, not generated.

Neurosymbolic applications

A layer on top of AI: language models read documents and requests, and deterministic code does the math, so every step stays inspectable. What is neurosymbolic AI?

The catalog

100+ apps, built to be rebuilt.

65 finance applications across the back office, and 39 banking applications modeled on the BIAN v13 service landscape. Each one opens working, on realistic sample data, before a single system is connected.

How it compares

Where the reasoning actually lives.

 ERP + reportsAI assistant on topKognitos applications
Where the logic livesConfiguration and custom codeA prompt, and the model's judgmentA procedure in English, step by step
Who can read itThe consultant who built itNobody — it is inside the modelAnyone who owns the process
Where the number comes fromA query you cannot seeGenerated textArithmetic over records, shown on request
How far you can drillTo a listYou cannotTo the source document
What happens when it is wrongA ticket, then a releaseWrong invisiblyA named step you can open and change
Questions

What finance teams ask us

What is explainable AI in finance?

Explainable AI in finance means every figure an AI system shows can be traced to the records and the rule that produced it. In Kognitos Apps, numbers are computed by deterministic steps written in plain English, never generated by a language model, so you can open any number and follow it back to source records.

Is "neurosymbolic" a real distinction or a label?

In this stack it is enumerable. The catalog is built from 54 primitives: 29 deterministic, 4 connectors, 3 platform operations, 3 classical ML models, 1 OCR, 1 human step, 6 language-model operations, and the rest hybrids. Every step of every procedure names the one it uses — 1,225 citations across 236 finance templates. What is neurosymbolic AI?.

The symbolic layer is not a wrapper around a model. It is the majority of the system, and the model is a component inside it.

How do I verify that no number comes from a model?

Structurally, the rendering path has no model in it: the widgets read run records and compute counts, sums, averages, rates, minimums, maximums and groupings. There is no model dependency in that code and there cannot be one.

Empirically, open any tile's inspect view. It shows the population and every record's contribution. If the number had not come from those records, the arithmetic would not reconcile.

What a model can produce is a field inside a record — an extracted amount, a classification, a drafted paragraph. Those are labelled, scored, and visible as such.

So is this "hallucination-proof"?

We will not use that phrase, because it is not true and you would find out. A model can misread an invoice, misclassify a transaction, or draft something unwise.

What is true is narrower and more useful: a hallucinated number cannot reach a tile, because tiles do not render model output. Model errors are possible, localized, labelled and correctable — not impossible. The numbers are computed, not generated.

Can I really use these before connecting anything?

Yes, and that is the point. Evaluating a finance application against an empty shell tells you nothing. Every catalog application ships with a sample database holding enough realistic history to work a full close, a full exception queue or a full onboarding backlog.

None of it is presented as your data. Each dependency carries an explicit binding, sample or live, and the inspect view always states which one answered.

Why should I believe the banking applications map to anything real?

They are modeled on the BIAN v13 service landscape — 300 templates, each carrying the BIAN source identifier, the published source URL for the service domain, and the step count of the source definition. You can open the BIAN specification alongside the application and check the mapping yourself.

How does this relate to AI employees and Context Graphs?

An application is the surface a team works in. The AI employees are the roles that work the queues inside it, and the context graph is the memory they read the business from. The procedures described on this page are what all three execute.

Is the AI deterministic?

No, and we would not trust a vendor who said otherwise — frontier language models are not bitwise deterministic even at temperature zero.

The honest claim is that the parts which must be exact are exact, because they are not models. Arithmetic, filters, tolerances, routing and the audit record are all symbolic. The model-touching steps are variable, individually identified, and their outputs land in fields you can see and correct.

Retire your first point solution.

Pick one point solution your finance team pays for. Start from the closest of the 100+ apps, or build your own in plain English, and every number stays inspectable down to the document.

Figures describing the catalog (104 applications, 18 process areas, 639 agents and automations, 54 primitives, 236 finance templates, 300 BIAN templates) are counted from the shipping catalog. Every screen shown on this page is the application itself, captured from the product running on sample data. Sample data shown on this page is illustrative. Items marked † are pending sign-off in the claims register and must clear it before launch. Kognitos Context Graphs · Kognitos AI employees