Data Matrix
Data Matrix discovers, maps, cleanses, governs and protects enterprise data so AI built on it can be trusted, audited and adopted. Quality inputs, governed flow, trusted outcomes.
Discovery & Flow Mapping
First we map where your data lives. Then we find what you’re missing.
Every engagement starts with two moves: a live map of the data you have, and an agent that brings in the data you don’t.
Enterprise data discovery & flow mapping
We scan apps, databases, warehouses, file stores and SaaS systems to inventory what exists, then map how data moves end to end: which system produces each field, which systems consume it, where PII travels, and where copies drift apart. The map surfaces duplicates, shadow copies and ungoverned flows, and it becomes the catalog and lineage the rest of the build runs on.
Source inventory
Flow & lineage maps
PII pathways
Shadow copy detection
The Harvester agent
Your estate doesn’t hold everything your AI needs. The Harvester is a data agent that works outward: it finds and brings in additional data from public domains such as registries, filings, websites, market and open data, matches it to your records, and tags every enriched field with its provenance, so harvested data stays as auditable as your own.
Public registries & filings
Web & market data
Match & merge
Provenance tagged
What It Includes
Four components, one governed data layer.
Discovery & Ingestion
Source discovery and end-to-end flow mapping, plus connectors and pipelines that collect data from apps, APIs, documents, POS and transaction systems, IoT and third parties, then normalize and route it.
Data Governance
Cleansing, deduplication and standardization; quality monitoring; least-privilege access; catalog and lineage; SOC 2-aligned, GDPR and DPDPA retention rules.
PII Protection
Tokenizes and masks personal fields before any model, analyst or non-production environment sees them, keeping referential integrity: a safe sandbox for engineering and analytics.
Analytics & Visualization
Dashboards over governed data, plus the data agents that keep data ready for the harness: the Harvester, quality monitors and enrichment agents.
Service Tiers
Three tiers, priced on the value returned.
Foundation
Clean and controlled
Cleansing: formatting, deduplication, standardization
Data security and access management
Compliance-ready controls
For any company preparing data for AI or analytics. Every 0DB build starts here.
Governed
Protected and analyzable
Foundation, plus PII masking and tokenization
Visualization
Data agents: the Harvester, quality monitors, enrichment
For regulated or data-sensitive operators running several applications.
Extended
Across your estate
Governed, plus support for systems 0DB did not build
Catalog and lineage, zero-data-copy patterns
Bring-your-own-cloud licensing, new compliance regimes
For enterprises extending governance across existing systems.
Why Data Comes First
Data governance ensures quality inputs. AI governance ensures trusted outcomes.
This groundwork is invisible to AI users, so it is often skipped, which is a common reason AI pilots stall. 0DB does it first, on a platform it has used for twelve systems.
01
Sources
02
Ingestion
03
Storage
04
Processing
05
Serving
06
Analytics
Governance spans every stage: quality, access, catalog, lineage, retention and PII masking.
How It Starts
A data engagement starts with a sample, not a quote.
01
Data Audit. A short, fixed-fee audit of a sample (for example, 1,000 records) measuring error rates, duplicates, structural gaps and unmasked personal records. No quote before the audit.
02
Quantify the cost. Findings translated into business costs: failed deliveries, dead leads, wasted marketing spend, payment rejections, regulatory exposure.
03
Choose the scope. Base Clean, Enrich & Deduplicate, or Continuous Governance.
04
Guaranteed outcomes. Results are committed, not hours: targets agreed in writing and measured against the audit baseline.
Data Matrix is one of the seven reusable components of the platform. Work done standalone carries into a later AI build. See the full platform architecture →
Start with a sample of your data.
A short, fixed-fee audit shows what your data costs you and what it would take to make it AI-ready.