Data Modernization.
Warehouses, lakes, and semantic layers.
A data platform that's ready for AI — with lineage, semantics, and one price you agreed up front.
What it is
AI systems fail on their data foundation before they fail on the model. We build the foundation.
Data Modernization is the unsexy work that makes everything else possible: warehouses, lakes, semantic layers, lineage, catalogues, and the governance to keep them true over time.
We do the migrations, the schema harmonisation, and the semantic modelling that turns "raw tables" into "questions your business asks." Analysts and LLMs both benefit.
The output is a data platform your team owns — with lineage you can inspect, freshness you can trust, and semantics you can query in natural language without hallucination.
What you get
6 concrete things, on the SOW.
Every deliverable is written into the statement of work — priced, dated, and signed off by a named engineer at the relevant gate.
- 01Cloud warehouse or lakehouse, provisioned
- 02Ingest pipelines with freshness SLAs
- 03Semantic layer / metrics store
- 04Data catalogue with lineage & ownership
- 05Access controls & row-level policies
- 06Runbooks for common data ops
Where this shows up
Three shapes of engagement.
Different problems, same method. These are the concrete work shapes we typically deliver under Data Modernization.
Warehouse migration
Move off a legacy warehouse without downtime; parity-verified against the source.
AI-ready platform
Add semantic layer + vector store so LLMs can query without hallucinating.
Multi-source unification
Reconcile customer, product, and event data from five systems into one canonical shape.
The stack
Capabilities, not vendors.
The requirement picks the tool, not the other way round. Naming vendors up front would set the wrong ceiling on what we take on.
- Warehouses & lakehouses
- Ingest & CDC
- Semantic layers
- Data catalogue
- Access control
- Lineage
How it runs
Seven stages. One signature at a time.
Every Data Modernization engagement runs through the same seven-gate Aivora Delivery Engine — each stage run by specialised agents, each ending at a gate a senior engineer must sign.
Frequently asked
Questions people ask before booking.
Do you migrate our production data?
Yes, with a parity-verified cutover — the new system runs against the old in shadow mode until every dashboard and downstream job passes, then we cut over.
How long does a typical modernization take?
A single-warehouse migration is usually 6–10 weeks. Multi-source unification runs 12–20 weeks. Both are fixed-scope from day one.
Will this work with our existing BI tools?
Yes — the semantic layer is BI-agnostic. Same metric definition, whichever tool your analysts already use.
Ready when you are
Bring us the hard bit.
Ninety-minute kickoff. Five-day audit. Fixed quote for Data Modernization — in writing, before we build.