SOLUTIONS

Your data warehouse, reimagined with AI.

Migrating a legacy DWH, documenting undocumented systems, or building reconciliation logic from scratch — AI can do in hours what used to take months. We make that happen for your team.

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AI-powered data warehousing visualization

Legacy data is valuable. Dealing with it shouldn’t be painful.

Most enterprises are sitting on years — sometimes decades — of data warehouse code, pipelines, and business logic that nobody fully understands anymore. Documentation is incomplete. Migrations feel impossible. And adding anything new means risking what already works.

We’ve been living in these systems for over 10 years. And now, with AI, we can help you move faster than you thought possible — without cutting corners.

HOW WE USE AI

Four ways AI accelerates your data project.

01

Legacy DWH Documentation

Finally know what your legacy system actually does.

We use AI to automatically analyse your existing code, pipelines, and data flows and generate structured, human-readable documentation: data dictionaries, lineage maps, business logic descriptions, and dependency graphs.

  • Full data lineage from source to report
  • Business logic extracted from legacy code
  • Data dictionary for all key entities
  • Dependency maps across systems and layers

02

AI-Assisted DWH Migration

Move off legacy systems faster — and with confidence.

We use AI throughout the migration process to compress timelines and reduce risk — from the first gap analysis to the last test script. Migrations that used to take 18 months can now be delivered in a fraction of the time.

  • Automatic lineage tracing across legacy systems
  • AI-generated gap analysis between source and target
  • Target model proposals based on business rules
  • Generated ETL/ELT scripts and automated test suites

03

Automated Reconciliation

Know your data is right — automatically.

We build AI-powered reconciliation frameworks that continuously monitor your data flows, detect anomalies, flag mismatches, and — in many cases — automatically resolve or escalate them.

  • Less time spent on manual data checks
  • Earlier detection of data quality issues
  • Audit trails that satisfy compliance requirements
  • Confidence that your DWH reflects reality

04

Semantic AI Layer

Let your team ask questions in plain language.

A semantic AI layer sits on top of your data warehouse and translates business questions into data queries — without requiring anyone to know SQL. Business users get direct answers from data, without waiting for the data team.

  • Semantic model mapping business terms to data
  • Natural language interface connected to your DWH
  • Role-based access so the right people see the right data
  • Integration with Power BI, Looker, and other BI tools

HOW WE WORK

We start with your system. Not a template.

We don’t point AI at your systems and wait. Before any component runs, our senior consultants map your architecture, extract your business rules, and define exactly what AI must — and must not — do.

AI executes within that framework. Every output is reviewed by the same person who designed the task. The result is speed without guesswork — and a system your team can actually trust.

  • AI is configured and directed by senior DWH consultants — it doesn’t operate independently
  • Business rules and data logic are defined by our experts before AI executes anything
  • Every output is reviewed and validated by the consultant who designed the task

STEP 1

Understand

We map your current state: systems, data flows, pain points, and goals. This is where we figure out where AI will have the most impact.

STEP 2

Build

We implement the AI-powered components — documentation, migration tooling, reconciliation, or semantic layer — with your team involved at every step.

STEP 3

Hand over with confidence

You don’t just get a delivered system. You get documentation, training for your team, and a setup you can maintain and evolve.

Have a legacy DWH that needs attention?

Whether you’re planning a migration, trying to document what you have, or exploring what a semantic layer could look like for your team — we’re happy to have an honest conversation about what’s possible.

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