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Know where every number came from. Prove it.

Lakehouse platforms, catalogs, lineage and access governance that make data findable, trusted and safe for AI to use.

Let's Build Together

One call with a senior engineer. A straight answer on what it would take.

Illustration: Data architecture, governance & data platforms

Where you are. Where you’ll be.

You need this if

  • Two departments report two different revenue numbers
  • Nobody can say which datasets contain personal data
  • You want RAG over company documents but don't know where to start

What changes for your business

  • Every metric traceable from dashboard back to source
  • Sensitive data protected from both staff and AI misuse
  • Teams find and reuse data instead of rebuilding it

What we hand over

  1. Target data architecture and platform build (Databricks, Snowflake, Fabric or BigQuery)
  2. Data catalog, lineage and ownership model
  3. Sensitive-data classification and access policies
  4. Retrieval and vector infrastructure for AI use cases

What it is

Data architecture is how a company's data platform is structured: warehouse, lakehouse or mesh, and how data flows through it. Data governance decides who owns each dataset, what it means, how sensitive it is and who may use it. AI raises the stakes, because agents read whatever they are allowed to.

AI agents will read anything you let them. Governance decides what they should. We design the platform (lakehouse, warehouse or mesh, depending on your scale and teams) and the governance that sits on it: a catalog people actually use, column-level lineage, data ownership, classification of sensitive fields and access policies that apply to people and AI alike.

Why now
60% of AI projects unsupported by AI-ready data will be abandoned through 2026. Gartner, 2025 (opens in a new tab)
Last reviewed

How it runs

  1. 01

    Diagnose

    Typically 2–4 weeks

    We map the problem, your data and your systems, and agree the one number that defines success.

  2. 02

    Prove

    Typically 4–8 weeks

    A working pilot on your real data, measured against that number. Not a slide demo.

  3. 03

    Ship

    Scoped to the outcome

    Production build with security, monitoring, cost controls and documentation included, not upsold.

  4. 04

    Run

    Ongoing, optional

    We operate what we built against clear service levels, or train your team to. Your call. No lock-in.

Questions you’ll ask

Lakehouse, warehouse or data mesh?
It depends on your scale, your teams and your workloads. Smaller estates often do best with one well-run warehouse; large organizations with many domains may need mesh-style ownership. We recommend the least complex option that fits, with the trade-offs written down.
Why does governance matter for AI agents?
An agent or RAG system will read anything its permissions allow. Classification, lineage and access policies decide what it should see, and let you prove afterwards which data an answer came from.
Why do two departments report different revenue numbers?
Usually because each built its own definition from different sources. A governed semantic layer with one owned definition per metric fixes it, and that becomes the version finance, dashboards and AI assistants all use.

Sound familiar? Let’s fix it.

One call with a senior engineer. You’ll leave with a straight answer on what it would take.

Let's Build Together

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