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Trustworthy data: governance AI actually needs

Lineage, quality SLAs, and consent tracking on the same pipelines AI consumes—so models don't fail audit and analysts don't lose trust.

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The problem

Governance theatre breaks when AI shows up

  • Lineage exists only in a PowerPoint

    The architecture deck shows clean data flows, but no one can answer "which source fed this column on this date" without paging the engineer who built it three years ago.

  • Quality SLAs are invisible until they break

    Freshness, completeness, and accuracy live in tribal memory. Analysts notice issues by anomaly in a chart; the platform never warned them.

  • PII handling is ad-hoc

    Sensitive columns are masked in some pipelines and not others. Consent flags live in CRM but don't propagate to the lake, so AI models train on data they shouldn't see.

  • Governance arrives after AI ships

    Legal and risk get involved late, the model passes pilot but fails audit, and rework lands on the team that already shipped.

How it works

A governance layer that lives in the pipelines, not in slides

Step 1

Instrument & catalog

Auto-discover sources, tag PII, capture column-level lineage, and assign ownership—so the catalog reflects reality, not last year's diagram.

Step 2

Enforce quality & consent

Quality SLAs as code, consent and policy checks at ingest and serving, and alerts when a dataset drifts from contract.

Step 3

Audit & explain

Every model, report, and dashboard traces back to inputs, owners, and policy decisions—one click to the audit-ready report.

Layer plugs into your existing warehouse, lake, and BI tools.

Trustworthy data: governance AI actually needs

What's included

Everything you needso governance keeps up with AI

Lineage, quality, consent, and audit—delivered as one operating layer your data, AI, and risk teams share.

Column-level lineage

Trace every column back to source systems and forward to dashboards, models, and exports—automatically refreshed.

Data quality SLAs

Freshness, completeness, schema, and distribution checks as code—with named owners and alerting when contracts break.

PII detection & masking

Sensitive-data discovery, classification, and policy-based masking across pipelines and serving endpoints.

Consent ledger

Track customer consent state and propagate it from source-of-truth to every downstream model and report.

Glossary & ownership

Business glossary, dataset owners, stewards, and certification status—surfaced where analysts work.

Audit reports

One-click lineage, quality, and consent reports formatted for internal audit and external regulators.

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Results

What changes when governance runs alongside the data

Results vary by context, data maturity, and scope. We scope honestly before we promise precisely.

70%

less effort preparing audit and lineage reports

Orientative—depends on starting documentation maturity.

Hours

to answer regulator questions that previously took weeks

Orientative—based on early implementations.

Lower

risk of AI models training on non-consented or PII data

How we work

From baseline to operating governance—without a big-bang program

Assess

Week 1–2

Inventory critical data assets, current lineage gaps, PII exposure, and regulator requirements.

Design

Week 3–5

Define quality SLAs, lineage scope, consent model, and ownership map with data, AI, and risk teams.

Build

Week 6–10

Instrument pipelines, populate catalog, enable policy enforcement, and deliver first audit-ready report.

Operate & expand

Week 11+

Hand over to data governance team and expand coverage across domains by quarter.

Timelines vary by source-system complexity, regulatory scope, and existing catalog tooling.

Get started

Ready to make governance part of how data moves, not a slide?

No commitment. We start with a scoped session to map your data, regulations, and audit pain.

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