Fractal lets your organization put AI to work on its core business systems without risking those systems, its data or its budget. Fractal provides a controlled path to AI applications that cost half as much and are ten times faster.
Every enterprise is under pressure to add AI to the applications that run the business. The usual approach connects an AI model directly to production systems and data. That approach is risky, because an AI model that makes a mistake can damage the records the business depends on. It is expensive, because AI providers charge for every piece of data the model reads. It also exposes sensitive data to the model.
Fractal takes a different approach. It builds a digital twin: a live, continuously updated copy of your existing systems, running on inexpensive hardware you control. AI works only on the twin, so your production systems cannot be harmed. The AI model only translates requests into instructions and turns results into plain-language summaries, so it never sees your private data and your AI costs stay small.
Starting with that first, low-risk step, Fractal can go on to provide a hot backup of your existing applications and, when you are ready, replace them with faster, lower-cost Fractal versions. The first step is a 90-day deployment at production scale that runs alongside your existing systems.
Performance and cost figures are minimums. Fractal-reported results from Fortune 500 production deployments in utilities, telecommunications and financial services are far larger (see the technical companion).
Each benefit below links to the part of the technical companion that explains how it works.
AI works on the Fractal digital twin, never on the systems of record. Data flows only one way, into the twin. Nothing the AI does can corrupt your production data or applications.
How the digital twin works →The AI model writes a small program; Fractal runs it. The same question gives the same answer every time, and every number can be traced and audited, like any other enterprise software.
Deterministic AI →Private and sensitive data is never passed to an AI model. The model sees only the request and the summarized results. The data stays on hardware you control, which guarantees data sovereignty.
AI at the edges →With the model at the center of the work, AI costs grow with every record it reads. With Fractal, cost depends only on how complex the question is, not on how much data is behind it. A large expense becomes a negligible one.
Token economics →The twin is a live copy of your applications and data, so it can serve as a hot backup and failover system. It costs about one-tenth as much as the systems it protects.
Live digital twin →Fractal runs on-premise, in the cloud, or on devices distributed around the world, wherever you interact with customers, partners and the outside world. The same application runs unchanged on all of them.
One stack, any platform →You don't have to bet the business on a cutover. Each stage delivers value on its own, and your existing applications keep running until you decide to retire them.
Fractal builds a twin of your existing systems and adds AI capabilities to it. The twin runs in parallel with production and is measured against it every day.
Result: safe AI capabilities, at production scaleBecause the twin stays in sync with production, it can take over if an existing system fails. It provides resilience you would otherwise have to buy separately.
Result: failover at 1/10 the costOnce the Fractal version has proven itself against live data, move workloads to it and retire the original. There is no hard cutover date.
Result: ≥ 50% lower cost, ≥ 10× fasterWhy it is low risk: at every stage the existing applications remain in place and unchanged. Decisions to move forward are based on results measured against your own production data, not on projections.
Technical detail The 90-day parallel proof of concept → How the digital twin stays in sync → Where the cost and speed gains come from →
Approve a 90-day Fractal deployment for one business application. Fractal will build the digital twin on commodity hardware, add AI capabilities, and run it at production scale alongside the existing system. At day 90 you will have measured performance, accuracy and cost data from your own operations, and a clear basis for deciding how far to go.
The technical companion describes the 90-day plan in detail.