Your AI is only as good as the data behind it And 80% of the work is preparing that data.
Most AI projects don't fail because of the model — they fail because data preparation is slow, ungoverned, and impossible to audit. The platform automates the entire governed data pipeline: full lineage, built-in compliance, and production-ready datasets in minutes, not months. AI tools keep changing. Your data foundation won't.
- 100% Automatic data lineage — source to AI model
- 15 min From business requirement to production
- 100% Project Success
- 400% Productivity increase across full project lifecycle
Governance baked in — not bolted on
Governance is enforced across every layer of your data pipeline — so data arrives at AI models clean, documented, and auditable by design.
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Every column traced automatically from source system to AI model input.
- Source-to-model column tracing
- Automated lineage — no manual mapping
- Full transparency — no black boxes
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Bi-temporal historization records every change — who, what, when.
- Bi-temporal historization
- Who / what / when — every change captured
- GDPR-compliant by architecture, not policy
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Built-in profiling and validation before data reaches downstream models.
- Built-in data profiling
- Pre-model validation rules
- Catch issues at ingestion, not in production
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Standardised ingestion patterns for every source system — auto-generated.
- Standardised ingestion patterns
- ETL/ELT code auto-generated
- Consistent, version-controlled, repeatable
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Deploy governed pipelines natively on the platforms where AI workloads run.
- Snowflake native pipelines
- Databricks native pipelines
- Google BigQuery native pipelines
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Compliance is structural — generated automatically, not maintained by hand.
- Role-based access controls
- Data residency controls
- Full documentation auto-generated
Built for the AI-native data team
Prepare data for AI and connect directly with the AI tools your team already uses.
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AI tools like Claude, Cursor, or Copilot speak directly to your data model.
- Exposes your data model as an MCP server
- AI tools read model structure and context
- Implement changes without switching context
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Ask your data model questions in plain language and get answers — instantly.
- Natural language queries to your model
- Full context in every answer
- Automatic implementation of instructions
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Every object carries full context so AI tools can reason and act immediately.
- Source mappings embedded
- Business definitions built in
- Dependency graph always current
Trusted by data teams in regulated and enterprise organizations
Common questions
- Yes — because the data layer is the one thing that doesn’t change. Models evolve every 6 months; your governed, documented data foundation is permanent. Teams that invest in data preparation now will feed any future model — not scramble to clean up data for each new one.
- Standard ETL tools move data. This platform governs it — automatically generating lineage, audit trails, documentation, and compliance controls alongside the pipeline. You get a governed data product, not just a data transfer.
- dbt handles SQL transformations. This platform covers the full governed pipeline: source ingestion, historization, lineage, documentation, and CI/CD deployment — in one visual tool. Most teams use both.
- Yes. It runs natively on Snowflake, Databricks, and BigQuery — the platforms where most enterprise AI workloads run. It delivers governed, documented datasets your models consume directly.
- Yes — it was built for exactly this. Bi-temporal historization, role-based access, GDPR-compliant architecture, and full audit trails are built in. A regulated bank delivered a compliant Risk Data Mart in 4 months.
Watch: AI & Data Sovereignty
See how leading data teams are building governed AI-ready data layers — and why the data foundation matters more than the model you choose. Free. No commitment.
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Live demo. Honest answers on whether it’s the right fit for your team.
Matt Collett
Sales Director
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