The Modernization Risk
Government legacy modernization projects have a long history of failure. Big-bang migrations disrupt citizen-facing services, exceed budgets, require years of delivery, and demand large teams of expensive specialists. When a critical system fails during a modernization, real people are affected — benefits stop, permits stall, services go dark.
The standard approach — rip and replace — trades a known, manageable system for an unknown, unproven one. The risk is enormous and the timeline is long.
"Every government agency has a legacy modernization story that went wrong. Fractal's parallel deployment model means you never have to bet your agency on a 'go-live' date."
Parallel Deployment — No Risk, No Disruption
Fractal deploys alongside your existing systems, not instead of them. Your production data flows into the Fractal digital twin continuously. AI workloads run on the twin in parallel with your existing environment. Your users, your staff, and your citizens see no change.
You run both environments side by side — comparing performance, cost, and data integrity in real time, with your data, on your workloads. You decide if and when to transition, on your timeline, with full visibility into the numbers.
90-Day Timeline
The 90-day deployment is structured in three phases, each with a clear deliverable:
1–14
Twin Setup & Sync
Fractal connects to your source systems and the digital twin begins populating with your production data. Nothing changes for your users or existing systems. Your IT team is not disrupted.
15–60
Parallel AI Operation
AI workloads run on the twin alongside your existing environment. Cost, speed, and data integrity metrics accumulate in real time — with your data, on your actual workloads.
61–90
Validation & Decision
Review the full comparative dataset — performance, cost, risk posture. Decide whether and when to transition workloads, on your timeline, with complete visibility into the numbers.
90
Phased Transition
Move workloads as the agency chooses. Legacy licensing and data center costs come off the books incrementally. Your source systems remain untouched until you decide otherwise.
Side by Side
| Dimension | Traditional Modernization | Fractal Parallel Deployment |
|---|---|---|
| Deployment timeline | 18–24 months | 90 days to full parallel operation |
| Disruption to existing systems | High — migration requires cutover | Zero — runs alongside existing systems |
| Team required | 18 high-end consultants | 1 programmer |
| Energy footprint | Full data center operation | 10 small computers — 95%+ reduction |
| New feature delivery | 1–6 months per feature | Hours or days |
Sustainable Computing
Fractal's architecture runs an entire enterprise AI platform on 10 small commodity computers. The energy footprint is a fraction of a traditional data center — with no cooling infrastructure, no raised-floor space, and no UPS systems required at scale.
For agencies with sustainability mandates, Fractal's hardware profile delivers measurable, documentable reductions in energy consumption and physical infrastructure. The transition from a data center to 10 small computers is itself a sustainability milestone.
Workforce1 Programmer Replaces 18 Consultants
Fractal's platform is managed by a single programmer. There is no large consultant team to onboard, no specialized knowledge that leaves when a contract ends, and no procurement cycle for new capabilities. New AI applications are delivered in hours or days — not months.
For agencies constrained by hiring freezes or small IT departments, Fractal fits within existing staff capacity. The skills required to operate the platform are standard software development skills, not exotic AI specializations.