Products · Sheaf Fusion

Sheaf Fusion

Many sources. One picture that fits together, or a clear note of where it does not.

Sheaf Fusion pulls any number of data sources into one view of a case, a customer, or a transaction. It checks that the sources agree before anything is decided on them. Where they do not, it names the sources that disagree, stops the action for a person to review, and records what it found.

The problem it solves

Most bad decisions look obvious afterwards. The rule that would have caught them is simple. They were missed because the facts were never in one place. They were spread across a customer record, a credit report, a document, and a partner's data, and often across time. Nobody was in a position to apply the rule to all of them at once.

Each source makes sense on its own. The contradictions are between sources. An AI agent that reads them one at a time takes on every one of those contradictions. Fusion reads them together, and it produces the audit record at the same time.

What it checks on every case

Agreement where sources overlap

Two sources that describe the same fact should say the same thing. Fusion finds every place where your sources overlap and checks that the fact is the same in each.

Contradictions you only see across all sources

Source A agrees with B, B agrees with C, and C contradicts A. No pair shows the problem. Fusion checks for contradictions like these directly. That check is the mathematics behind Sheaf.

Agent claims against the sources

Every claim an AI agent makes about the case is checked against the sources you name as authoritative, and against other AI models that cannot see each other's answers.

The action itself

Submitting, approving, paying, or issuing waits for a person when the facts behind it do not fit together. Sheaf stops the action and parks it. The agent cannot go around this.

Where it is used

Fusion fits any situation where a decision depends on facts from several systems at once. The first use is UK mortgage and credit broking. The broker's file, the credit report, the lender's criteria, and what the customer has told the broker are checked against each other before a recommendation reaches the borrower, in the terms of MCOB suitability and Consumer Duty.

The same check works for taking on a new customer and confirming who they are, across the credit report, the bank, and their documents. It works for an insurance claim, across the policy, the report, and the invoices. It works for an investment position, across the firm's own records, the custodian, and the other party. The sources change. The check does not.

What your compliance team receives

For every case Fusion produces a signed record of whether the facts held together, a map of exactly which sources disagreed and over which fact, a record of any stopped action and who released it, and an entry in a tamper-evident record, signed and timestamped by an independent timestamp authority. The judgment stays with your people. The evidence is ready when the auditor or the regulator asks.

One limit. Fusion uses AI models to pull the claims out of documents and agent output, so how much it catches is measured in use rather than proven in advance. Treat it as a second check that catches what your first check cannot see, and that reports how much of each case it covered.

Data handling

Fusion works on the facts a decision needs, with personal details removed: amounts, ratios, dates, bands, attributes. Names, addresses, account numbers, and dates of birth stay in your systems. Sheaf does not train on your data. Each AI model provider is named as a sub-processor in your data-processing agreement.

See it run on your sources.

Request a demo

Or read about the mathematics behind Sheaf.