× Fractal

Fractal enterprise data

MIOSA and Fractal run dedicated Fractal instances for massive-scale structured data and computation, secure and isolated for enterprise and government.

Who it is for

Fractal is for organizations whose data work is bigger than an application database:

• Enterprise and government teams that own client-wide structured data and cannot let AI near their systems of record.
• Regulated industries: electric, gas, and water utilities, telecommunications, financial services, healthcare, insurance, logistics, retail, oil and gas, and public sector.
• Very large datasets: millions of records, accounts, meters, claims, invoices, transactions, or assets.
• High-volume computation that is trivial per row and expensive once it runs millions of times a day.

If your data fits comfortably in a MIOSA Postgres database, you do not need Fractal. Use the built-in primitives and come back when the population, the calculation, or the isolation requirement outgrows them.

What you get

How it fits with MIOSA

MIOSA is the product and operating layer. Apps, agents, deployments, review queues, and audit trails connect to the Fractal data plane, so your customers see one branded product and never touch the data plumbing underneath.

Read it left to right:

1. Customer systems of record remain the source of truth, and data flows into the instance one way.
2. Fractal turns raw source data and business rules into an explicit enterprise model: ontology, taxonomy, and schema mapping.
3. The Fractal instance runs the heavy data work against that model: high-volume calculations, reconciliation, and derived datasets.
4. MIOSA turns those outputs into a product: apps, agents, deployments, review queues, approvals, reports, and audit trails.
5. Promotion back to source is controlled and approved. The agent proposes; customer policy decides what is promoted.

The enterprise data model

The first job is not to point an agent at raw tables. The first job is to map the customer’s world into a usable data model: the entities, relationships, names, identifiers, calculations, source-of-truth rules, and approval rules the business actually runs on.

Business objects in a typical model include customer, account, claim, invoice, product, meter, asset, provider, and location. Taxonomy covers product families, claim types, customer segments, rate classes, regions, and exception categories. Governance rules mark fields read-only, propose-only, or approval-required for writeback, and set the audit requirements for each.

Governed writeback

Fractal-backed workflows should not let AI write directly to production systems. Use a propose, review, promote shape:

read scoped data
  -> run calculation or reconciliation
  -> produce finding or proposal with evidence
  -> route to human or policy approval
  -> promote through a controlled writeback service
  -> record audit outcome

Connector expectations

Do not assume a public connector contract from this page. A production design is scoped with MIOSA and Fractal, and defines:

• dataset and tenant identity;
• data residency and network path;
• scoped credentials or token exchange;
• read-only, propose-only, and approval-required modes;
• sync health and freshness;
• audit and event handoff;
• writeback ownership and rollback or compensation policy.

Fractal compared with MIOSA Postgres, Storage, and Volumes

Use the built-in primitives for normal application state. Reach for a Fractal instance when the population, the calculation, or the isolation requirement outgrows them.

Deployment options

A hybrid pilot is also common: prove the workflow on a restricted export or scoped dataset before any production promotion.

Getting started

A Fractal-backed project starts as an enterprise architecture conversation, not a connector install. The usual sequence:

1. Talk to us. Describe the workflow, the systems of record, and the data that concerns you.
2. Scope the data. Decide what the instance sees, where it runs, and how data residency is handled.
3. Map the model. Define the ontology, taxonomy, and schema mapping the workflow needs.
4. Choose the deployment and the sync, freshness, and read, propose, and writeback modes.
5. Build the product surface. Apps, agents, review queues, reports, and audit trails around the Fractal instance.
6. Prove it on real data. Run a parallel pilot before promoting anything back to source.

FAQ

Is Fractal a MIOSA primitive like Postgres?

No. Postgres, MySQL, Redis, Qdrant, Storage, Auth, and Volumes are self-service primitives you create from the platform. A Fractal instance is an enterprise engagement, scoped with MIOSA and Fractal.

Does my data have to leave my environment?

No. A private deployment keeps the instance in your environment or private network. A cloud deployment puts the instance in the partner data plane instead.

Can AI write to my production systems?

Not directly. Data flows into the instance one way, AI works on the twin, and results are promoted back to source only through a controlled, auditable process with explicit approval.

What scale does this target?

Millions of records, accounts, meters, claims, invoices, transactions, or assets, and calculations repeated millions of times per day. Fractal states that its production deployments run at Fortune 500 scale across utilities, telecommunications, and financial services.

Do you publish certifications?

This page does not list certifications. Government, defense, and regulated deployments are scoped directly with MIOSA and Fractal against your requirements.

Is there a published performance or price figure?

Fractal publishes its own figures, including 100x performance, 90 percent less infrastructure cost, and a 90-day parallel deployment, on its public site. These are Fractal’s claims, not MIOSA measurements. Treat them as a starting point for a pilot, not as a contract.

Fractal’s public claims and references

Fractal publishes the partner-side architecture as a synchronized digital twin, one-way synchronization from systems of record, controlled promotion back, locality optimization, commodity hardware, and industry-specific deployments. These are Fractal’s own claims.

Common enterprise workflows

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