DataFab / Platform
One platform · three layers
Inside the platform, layer by layer.
DataFab is a foundation, a builder and a set of governed utilities on one platform. The foundation draws the enterprise from the systems already running. The builder lets the enterprise formalise and execute how it works. The utilities ship ready. And every governed result flows back, so the whole thing compounds.
Everything, working together
The whole machine.
Sources stay in place and are discovered and resolved into one governed knowledge state. Agencies, built in the Studio, ground on it and run — with people at the gates. Utilities ship on top. And every governed result flows back, so the knowledge state is cumulative, not static.
Connect the enterprise
Point the fabric at a source you already run. Direct database, API, event stream or MCP. No extract, no copy, no migration, and no system asked to change.
Understand it
Discovery catalogues the estate. Entity resolution collapses the twelve copies of one customer into one golden record. A schema-bounded ontology is derived from what was found.
Build how it works
Your own people compose governed agencies in the Studio — in language or with full control — and publish them, scoped, sandboxed and audited.
Compound every result
Governed schemas, accepted relationships, approved decisions and outcomes cascade back into the knowledge state with provenance, authority and version attached.
Layer 1 · the foundation
Knowledge Fabric
A data fabric that gives unified access to distributed data assets — maintaining mappings and relationships rather than duplicating data. It is an access enabler, not a repository: one source of truth, unified analytics, nothing moved.
Discovery
Crawlers and profilers catalogue the estate — metadata and statistics only, no manual inventory.
Entity resolution
Blocked, scored, clustered, reviewed and merged into one authoritative golden record with full provenance.
Derived ontology
The semantic layer emerges from your sources — schema-bounded, human-approved, versioned.
Persistent graph
Corporate memory as a four-level knowledge tree: attributes, relations, keywords, communities.
Active metadata
Classification, quality monitoring, lineage and change detection — the knowledge state maintains itself.
Governed enrichment
A registry of approved external sources, each field schema-bound and traced from source to graph.
Layer 2 · the builder
Knowledge & Agentic Studio
The build surface of DataFab — where the enterprise composes its own governed agencies from its own documents, in its own language, and on its own schemas. Every agent schema-bound, sandboxed, human-gated and audited.
Business user · no code
Build it in language.
- Describe it in words — planning drafts the agent
- Upload your documents — discovery extracts the schemas
- A sentence becomes a pipeline you can preview
- Start from a template of pre-built workflows
Expert · full control
Compose it node by node.
- Visual builder with live validation on every node
- Custom logic and scripts inside the query plan
- Author your own connectors in MCP Studio
- Define schemas and query plans directly
- Step-by-step debugging with breakpoints and traces
Both routes converge on one governed agent. A pipeline generated from words imports straight into the visual builder, and the same draft-and-publish lifecycle carries it between them.
Layer 3 · out of the box
A utility is the platform, pointed at a problem.
A utility is agencies, workflows and widgets composed in the Studio and published — the same build surface, packaged for a domain. As it runs, its governed decisions and outcomes enrich the Fabric, so the platform gets better at the work the more work it does.
Financial crime & compliance
Alert triage, sanctions and adverse-media screening, investigation console, and case to filing with the narrative linked to source.
Due diligence
Counterparty, transaction and onboarding due diligence over the resolved graph — ownership, control and ultimate beneficial owner.
Car-finance remediation
Cohorting affected populations, deterministic redress calculation, AutoFab outreach and audit-ready packaging.
Client lifecycle management
Extraction of parties, terms and dates; obligations tracked and alerted; clause-level review; intake through renewal.
Insurance claim handling
Intake and extraction with per-field confidence, coverage checking against clauses and endorsements, prospects, fees, drafted response.
Government intelligence
Entity and network resolution across sources, governed OSINT enrichment, case management with chain of custody.
How much it runs itself
The level of automation is a dial.
DataFab does not force one mode. Each use case — and each step within it — runs at the level of autonomy its risk allows. You put the automation where the work is routine, and keep the control where the risk is.
Deterministic
Fixed steps, reproducible, no model discretion in the flow.
- Where it fits
- Fee checks, redress calculations, statutory schedules, fixed procedures.
- Model discretion
- None. The rule decides, every time, identically.
- What it leaves behind
- Reproducible to the penny. Replayable under the rule version in force.
Process-governed
A defined business process routes the work; rules drive the flow.
- Where it fits
- Regulated workflows, service-level agreements, sign-off chains.
- Model discretion
- Routing only, inside a declared process.
- What it leaves behind
- The process is the audit trail. Every branch taken is recorded.
Human in the middle
Agencies do the work; a person approves at each gate.
- Where it fits
- Coverage decisions, conflicts, filings, anything contestable.
- Model discretion
- Prepare and propose. Never dispose.
- What it leaves behind
- The gate blocks until approved, and the approver and their basis are logged.
Self-organising
Agencies route and adapt within policy and schema bounds.
- Where it fits
- Triage, enrichment, discovery, evidence gathering.
- Model discretion
- Chooses its own path — inside the bounds you set.
- What it leaves behind
- Every path taken is recorded; the bounds themselves are versioned.
Fully agentic
Autonomous reasoning inside the sandbox and the schema.
- Where it fits
- Extraction, summarisation, low-stakes work.
- Model discretion
- Full, within an isolated runtime and a bound schema.
- What it leaves behind
- Post-hoc audit. It cannot invent entities outside the schema.
And it is set per step — one written claim, eight decisions, five different levels
Connectivity · MCP First
Every system, one protocol layer.
The fabric both consumes and generates Model Context Protocol connectors for federated queries. When a firm-specific system has no built-in connector, you do not wait for the platform — you author one, have it validated, and publish it to your own catalogue.
| Connection pattern | Use case | Severity | Remediation SLA |
|---|---|---|---|
| Direct TLS | Cloud-hosted sources | Critical — flow stopped | 24 hours |
| VPN tunnel | On-premises sources | High — degradation | 72 hours |
| Private link | Same-cloud sources | Medium — workaround exists | 7 days |
| Agent-based | Air-gapped environments | Low — cosmetic | Next release |
Release monitoring is continuous, breaking-change alerts are issued as announced, compatibility is reviewed per vendor release, and pre-release testing is performed where the vendor makes it available.
FabriCloud · the private data cloud
Bring the cloud to your data.
FabriCloud is the infrastructure layer DataFab runs on, and a product in its own right: cloud-grade data and AI — analytics, applications and agents — running inside your own environment. Not a public cloud. Not a data warehouse. A private cloud that comes to your data, bringing cloud-grade services to the data you already control.
The common approach
Many sources × many use cases = endless projects.
- Every source wired to every jurisdiction wired to every use case
- Each line a separate project
- Each re-solving data quality, governance, context and cost
- It never ends
FabriCloud
Woven once · governed once · served everywhere.
- Every source — databases, APIs, documents, streams — woven into one fabric
- Only metadata and control signals leave; raw data is processed in place
- A data plane stands up in hours; first sources typically same-day
- Cloud, VPC, on-premises or fully air-gapped, control plane self-hosted where required
Trusted by default
One governed, lineage-tracked view for business intelligence and reporting — without another migration.
Permissioned context
Agencies act only on data they are allowed to see — governed tools, full audit. MCP First: every governed dataset, connector and tool exposed as a native Model Context Protocol tool, with access enforced at the source.
Ship features, not plumbing
Real-time, access-controlled data through one uniform API.
FabriCloud is the private data cloud — the boundary, the planes, the governed activation layer. DataFab is what runs on it: the Knowledge Fabric that resolves your estate, the Knowledge & Agentic Studio your people build in, and the governed utilities on top. Same architecture, same certifications, same boundary raw data never crosses.
Deployment
Deploy it where you need it.
The same governed platform, delivered against your estate, your controls and your operating model. The control plane is an operating model, not a place your data goes; the data plane runs inside your environment.
Single-tenant, your region
DataFab-hosted, single-tenant, deployed in the region you choose, under your residency rules.
Your account, private link
Runs in your own cloud account and VPC, over private connectivity, with your keys.
Inside your data centre
Deployed on your infrastructure, reading in place to the systems you already operate.
Isolated and sovereign
Full functionality inside an isolated network. Agent-based and outbound-only where a boundary must be crossed at all.
Managed service & BPO
Price the outcome, not the headcount.
Delivered as a managed service or inside a business-process operation, the governed agencies do the work and a person sits on the gate. That moves pricing from per full-time equivalent to per case — and because the fabric compounds, unit cost falls as volume grows instead of staying flat.
Today · priced per person
100% of cost per case
- Cost driven by analyst time
- Margin competed away head by head
- Unit cost flat as volume grows
- More throughput means more hires
Governed · priced per case
A designed margin, with a floor
- Agencies do the work; the analyst sits on the gate
- Margin designed in rather than competed away
- Unit cost falls at volume as the fabric compounds
- Throughput scales without headcount
Illustrative model, not a quote. The shape of the economics is set per engagement against your case mix, volumes and clearance policy.
What it builds
Three layers. One asset.
Every layer of this platform exists to build one thing: resolved institutional context that belongs to you. The foundation makes it reachable, the builder makes it executable, the utilities put it to work — and each governed result makes the asset worth more than it was the day before. That is the difference between a tool you renew and an asset you hold.
Next step
See it running against your own estate.
One urgent use case, a handful of critical sources, and a governed agency live inside your boundary. That is the shape of a first engagement.