The data and computing foundation for enterprise AI.
Fractal runs enterprise applications and their data across many small computers working in parallel. AI works on a live twin of the systems of record, so every process can run on all of the data in minutes while production systems stay untouched.
Applications
Operational work that runs on all of the data
In most enterprises, quality checks, forecasts, simulations and customer analysis run on samples, in overnight batches or through outside consultants, because running them on every record has been too slow and too costly. Fractal removes that constraint.
- VerificationEvery bill, meter read and transaction checked in every cycle.
- SimulationRate and policy changes tested against the whole customer base.
- ForecastingForecasts built from 100% of the data, inside the same window.
- DetectionFaults and anomalies found across all assets before they fail.
- Customer insightEvery source combined for each customer, with a full audit trail.
- ModernizationLegacy applications replaced without a multi-year rewrite.
Fractal publishes solution data sheets for 11 industries.
Architecture
How Fractal makes this possible
The language model does only the first and last steps. Fractal does the heavy lifting in between.
- The systems of record are left in place.Data is copied from them in one direction only. Nothing is ever written back.
- Fractal maintains a live digital twin.The twin is updated continuously and runs on commodity hardware the organization controls.
- The twin is divided across many small instances.Each instance holds one shard of the data and a complete software stack, so processing happens where the data is.
- The model writes a small program, and every instance runs it at once.Results are deterministic: the same question returns the same answer, and every figure can be traced to its source.
- Only results return to the model.Fractal aggregates the results and the model presents them in plain language. Source data is never sent to the model.
The model handles only the request and the result, so token costs depend on the question, not on the volume of data.
Source data stays on hardware the organization controls and is never passed to the model.
The full architecture covers the digital twin, deterministic AI and token economics.
Results
Measured in production
Fortune 500 deployments in utilities, telecommunications and financial services, compared before and after the move to Fractal.
Data center90+ hours
10 small computers9 minutes
| Measure | AI on source systems | Fractal digital twin |
|---|---|---|
| Data corruption risk | Present: AI has direct write access | Zero: AI never touches source systems |
| Deployment timeline | 24 months | 90 days |
| Team required | 18 high-end consultants | 1 programmer |
| Infrastructure cost | Data center: $ millions | 10 small computers: about $20,000 in total |
| Software licensing | Database, virtualization and others | Eliminated |
| AI token cost | Grows with every record the model reads | Flat: depends only on how complex the question is |
| New feature delivery | 1–6 months | Daily |
Electric utilities10M customers billed on $20K of hardwareOver 1,000× faster, with zero risk to the systems of record.
TelecommunicationsApplication runs over 1,000× faster90% less storage and zero downtime.
Water utilitiesRate validation across millions of customers$500K in consulting saved.
Gas utilitiesReal-time rate simulationAcross the entire customer base, in minutes rather than months.
Financial services360-degree customer insightFrom every data source, in real time, with a full audit trail.
Fractal’s published minimums are 10× faster and 50% lower cost. These production results exceed them.
Runtime, cost, team size, reliability and corruption risk, measured side by side.
Adoption
Adopted one application at a time
Fractal runs beside the systems an organization already operates. Each stage stands on its own, and nothing is retired until its replacement has been proven on live data.
-
Stage 1 · within 90 days
AI on a digital twin
Fractal builds a twin of one application and adds AI capabilities to it. The twin runs beside production and is measured against it every day.
AI at production scale -
Stage 2
Hot backup
The twin stays in sync with production and can take over if the original system fails.
Failover at about one-tenth of the cost -
Stage 3 · on the organization’s timeline
Replacement
Once the Fractal version has been proven on live data, workloads move to it and the original is retired.
≥ 50% lower cost, ≥ 10× faster
Discuss a first application
A 30-minute conversation about your environment and the application where a 90‑day twin would show the most.