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Syzygy Rosetta

AI governance middleware for trustworthy AI-assisted financial systems. Rosetta evaluates every AI interaction, applies policy rules, and generates tamper-evident evidence records.

What is Syzygy Rosetta?

Syzygy Rosetta is an AI governance middleware platform. Its role is to sit between your applications, AI models, and business workflows — evaluating every interaction and generating authoritative evidence records.

Think of Rosetta as a policy enforcement and documentation layer for AI-assisted decisions. Every time an AI model makes or assists a decision, Rosetta captures:

  • What the AI model did
  • Why it acted (policy context)
  • What policy was applied
  • What risk signals were detected
  • Whether human review occurred
  • What the final decision was
  • A cryptographic seal for tamper evidence

Where Rosetta Sits

Rosetta is designed as a middleware layer that integrates with existing infrastructure without requiring major architectural changes.

Applications
Loan origination systems, fraud detection platforms, underwriting engines, AML monitoring tools
Rosetta — Governance Middleware
Policy evaluation, risk scoring, evidence generation, audit receipt creation
AI Models
Decision models, risk engines, ML pipelines, third-party AI services
Human Review & Compliance
Compliance teams, risk officers, internal audit, regulators

Current Status

Partner Preview

Rosetta is in development. We are offering partner preview access to select financial institutions.

Funding Stage

We are actively fundraising to accelerate development and expand our team.

Seeking Pilot Partners

We are looking for financial institutions to participate in our pilot program.

Important Note

Syzygy Rosetta is currently in development and is not publicly available. The information on this page represents our design and architecture vision. Features, APIs, and implementation details may evolve significantly during development. We do not claim production readiness at this stage.

Key Design Principles

Middleware-First

Integrate with existing infrastructure without major architectural changes.

Policy-Driven

Every evaluation is governed by configurable policies that reflect regulatory requirements.

Evidence-Centric

Every interaction produces a tamper-evident record suitable for audit and compliance.

Human-in-the-Loop

Support review workflows that ensure appropriate human oversight of AI decisions.