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About Falcon Veritas Intelligence

We saw a clearer way to explain reconciliation.

We are a small engineering team. As we examined reconciliation work, we saw the same opportunity: connect disconnected records, unclear matching decisions, and recurring exceptions to a clearer path from source to evidence.

The Aperture, Precision, and Vision methodSource records pass through a sequence of review points toward a recorded outcome. The illustration is a conceptual view of the product approach.NOSTROSECURITIESINTERCOGLSOURCE FIELD01020203REVIEWED · RECORDED

The opportunity we saw

Reconciliation can be difficult to explain after the work is done.

Our mission is to make reconciliation easier to explain, review, and govern. The gap is often the distance between records, reasoning, exception work, and oversight.

01

Match without meaning

A result may arrive without a plain account of the factors and rules behind it.

02

Silicon, then a person, then a spreadsheet

When proposed actions leave a governed workflow, decisions and context can end up in manual hand-offs.

03

AI bolted on afterwards

When intelligent guidance sits beside a separate matching path, reviewers need context about what influenced a decision.

04

Your data through their inference

Institutions should understand where data is processed, which model is used, and what deployment choices are available.

Designed for cross-border operations

One reconciliation can cross many jurisdictions.

Financial institutions work across local systems, regulations, and data requirements. FVI is being designed with these five regions in view.

  • IndiaEngineering / delivery
  • United KingdomEMEA engineering
  • EuropeDORA / GDPR context
  • SingaporeAPAC engineering
  • United StatesUS market engineering

We are designing one platform for regional data residency, customer-managed encryption keys, and single-tenant deployment options, without separate product forks.

How we work

Three disciplines, in order.

Aperture, Precision, and Vision describe an ordered approach. Each begins with context, makes decisions legible, and brings assistance into the work with human control.

01

Aperture

widens the view so related records can be understood together

Consider cash, securities, intercompany, and general ledger records in their wider operating context.

  • Cash
  • Securities
  • Intercompany
  • General ledger
02

Precision

makes matching logic clear, including what still needs attention

Use deterministic and probabilistic matching approaches while keeping rules, tolerances, and unresolved differences understandable.

  • Rules
  • Tolerances
  • Unresolved differences
03

Vision

brings intelligent assistance into the work, while people retain judgment, review, and approval

Assistance can help people examine patterns and prepare next steps. Human ownership remains clear throughout the process.

  • Assistance
  • Human judgment
  • Review

What guides the design

Commitments behind the approach.

Provenance

Keep the path visible

FVI is designed around a traceable path from source records through matching logic to an outcome a person can review.

Sovereignty

Make deployment choices explicit

Regional residency, customer-managed keys, and single-tenant deployments are design objectives for one platform, subject to technical validation and agreement.

Evidence

Treat evidence as part of the work

Control needs, audit expectations, and recordkeeping should shape the workflow from the beginning, not arrive as an afterthought.

Where we are

Pre-launch, with claims kept precise.

Falcon Veritas Intelligence is in pre-launch. It holds no SOC 2 Type II or ISO 27001 certification. Any performance figures presented on this site are design targets, not independently audited production results.

We want each evaluation to focus on the product, its deployment requirements, and the evidence a bank needs to make its own assessment.

Start a conversation

If your closes are hard to explain, that is the problem we set out to fix.

Bring a reconciliation you cannot currently evidence. We will show you what a defensible answer looks like on your data, not on a demo dataset.