How It Works
Fractal creates a synchronized, hyper-optimized digital twin of your structured data — then runs all AI processing on the twin, never on your systems of record.
The Digital Twin
A Fractal digital twin is a continuously synchronized replica of your databases and systems of record. It is not a backup, a snapshot, or a data warehouse. It is a live, real-time copy that maintains full fidelity with your production data — housed in a separate, protected environment.
Data flows one way: from your source systems into the twin. AI operates exclusively on the twin. If an AI agent hallucinates, gets injected, or produces unexpected writes, the damage is contained to the twin. It never reaches your production databases.
Results can be promoted back to source systems through a controlled, auditable process — but nothing writes back without explicit authorization. This is not a guardrail. It is an architectural guarantee.
The Architecture
Traditional databases are designed for transactional workloads — point lookups, ACID compliance, concurrent writes. The twin is built for the wide-scan, aggregate, and transform workloads that AI demands. The result is 100× to 1,000,000× better AI performance.
The twin is distributed across a network of Fractal agents — small, self-contained programs each holding the full application stack for a slice of the data. Agents coordinate peer-to-peer. No central broker, no middleware, no orchestration layer.
The twin runs on commodity hardware you control. There is no centralized database, no middleware layer, and no external cloud dependency. Licensing costs for Oracle, VMware, and cloud service layers are eliminated entirely.
Concerned about the transition? Our onboarding team provides bespoke guidance on twin setup, AI integration, and deployment — so you are never navigating it alone.
Side by Side
Measured results from production deployments at Fortune 500 scale over five years. Not projections.
| Dimension | AI on Source Systems | Fractal Digital Twin |
|---|---|---|
| Data Corruption Risk | Present — AI has direct write access to production databases | Zero — AI operates exclusively on the twin |
| Application Speed | Constrained by database I/O and middleware latency | 100× — 1,000,000× faster via Locality Optimization |
| Infrastructure Cost | Data center or cloud: $millions per year | 10 small computers: $10,000 total |
| Downtime | Hours per month | Less than 30 seconds per year |
| Vendor Lock-In | Oracle, VMware, cloud-specific services | Eliminated — runs on any hardware |
| Time to Market | New AI applications in months | New AI applications in hours or days |
Results reflect measured production deployments at Fortune 500 scale over five years — not projections.
Proof of Concept
A 90-day proof of concept deploys the Fractal digital twin alongside your current environment, processing your production data. No disruption to existing operations.
Schedule a Call →Onboarding team connects Fractal to your source systems and establishes the digital twin. Your production data begins flowing into the twin. Nothing changes for your users or existing systems.
AI workloads run against the twin in parallel with your existing systems. You watch cost, speed, and safety metrics accumulate in real time — with your data, on your workloads.
Review the full comparative dataset — performance, cost, and risk posture. Decide whether and when to transition workloads, on your timeline, with full visibility into the numbers.
Move workloads over as you choose. Legacy licensing, cloud spend, and data center costs come off the books as you go. Your source systems remain untouched until you decide otherwise.
A 90-day proof of concept alongside your existing systems. No disruption. Your data. Real numbers.