Every stalled AI programme we are called into has the same root cause: data that cannot be found, trusted, governed or afforded at inference time. We fix the foundation so the use cases above it stop failing.
AI does not fail on the model. It fails three layers down — on lineage nobody can trace, access controls that force a copy, quality that was never measured, and a compute bill that makes the use case uneconomic at production volume.
Make it findableWith a catalogue and lineage a data scientist can trust without asking a person.
Make it usableWith quality contracts, freshness guarantees and a semantic layer that means the same thing in every use case.
Make it governableWith unified access control, masking and audit that satisfy privacy and sector regulation without duplicating the estate.
Make it affordableWith workload isolation, storage tiering and FinOps discipline that keep unit economics viable as AI volume grows.
Make it AI-readyWith vector, feature and retrieval infrastructure that sits inside the governance perimeter rather than beside it.
Medallion-structured lakehouse on Databricks with Delta Lake, designed for both analytics and AI workloads rather than retrofitted for the second.
Unified catalogue, lineage, fine-grained access control and audit across data, features, models and AI assets — one perimeter, one policy set.
Batch and streaming ingestion, CDC from systems of record, orchestrated transformation with tested, versioned pipelines and enforced data quality contracts.
Certified business definitions and a managed feature store, so a metric or feature is computed once and reused across dashboards, models and agents.
Vector indexes, chunking strategy, embedding lifecycle and retrieval evaluation — governed under the same catalogue as everything else.
Cluster policies, workload isolation, autoscaling discipline, storage tiering and chargeback reporting. Typical realised savings on optimised Databricks estates run 20–40%.
| Step | Focus | What good looks like |
|---|---|---|
| 01 · Ingest | Getting data in reliably | Declarative, monitored pipelines; schema evolution handled; source-to-bronze lineage automatic |
| 02 · Curate | Making it trustworthy | Quality expectations enforced in-pipeline; silver and gold layers with owners and SLAs |
| 03 · Govern | Making it safe to use | Unity Catalog access model, masking and row-level policy, full audit trail, no shadow copies |
| 04 · Activate | Making it earn | BI, ML, retrieval and agents all consuming the same governed assets with predictable cost |
Most of this work happens on a live estate. We run foundation programmes as parallel-build migrations: new platform stood up alongside the incumbent, workloads moved in dependency order with automated reconciliation on every cutover, and decommissioning only after two clean reporting cycles. Business reporting does not pause while the foundation is rebuilt underneath it.
A dedicated Databricks practice covering lakehouse architecture, Unity Catalog governance, migration and platform FinOps — with delivery pods, not just certifications.
Healthcare, life sciences and financial services estates where PHI, PII and audit obligations shape every design decision. InSilico, our pharma data intelligence solution, was built on this practice.
We report platform unit economics from the first sprint. A foundation that cannot be afforded at scale is not a foundation.
Onshore-offshore pods through AgileSourcing and our Hyderabad and Pune delivery centres, or a dedicated GCC under a Build-Operate-Transfer model where you want the capability in-house.
No. The architecture principles — governed catalogue, quality contracts, semantic layer, cost isolation — apply on any modern platform, and we deliver on the major clouds. Databricks is where our deepest practice and our sharpest cost results sit.
Use cases usually start before the foundation is finished. We sequence the foundation by use case dependency, so the data a prioritised use case needs is governed and ready in the first eight to twelve weeks rather than at the end of the programme.
Yes — managed operations, or a dedicated team transitioned to you under a Build-Operate-Transfer arrangement typically over 36 months, with zero capital outlay during the operate phase.
Talk to an Adroitent AI lead. We will come with a point of view on your estate, not a generic capability deck.
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