Mithril Data Group moves legacy data to the cloud and puts clean, governed data behind the AI tools your teams want to use. Architect-led, from the first assessment to production.
Most AI projects stall on the data, not the model. We fix the foundation first, then build on it.
Move ETL workloads and Oracle, SQL Server and DB/2 data from on-premises systems to the cloud, or between clouds such as AWS to Azure, with parallel runs and row-level reconciliation so nothing is lost at cutover.
on-prem → Azure / AWS · AWS → Azure · PowerCenter → IDMC
Conceptual, logical and physical models, including canonical models that give every system one shared definition of customers, products and transactions. Then profile, clean and document the data so AI answers can be traced and trusted.
canonical modeling · data quality · lineage · governance
Private knowledge assistants that answer from your own documents and databases, cite their sources, and run in your cloud tenant or on your own hardware.
retrieval-augmented generation · LLM APIs · vector search
Design the schedules, dependencies and monitoring that keep data moving on time across on-premises systems, cloud services and new AI workloads.
Stonebranch UAC · Informatica · Pentaho · SLAs · runbooks
Many firms begin with a model and go looking for data to feed it. We begin with the systems you already run: the databases, the integration jobs, and the business rules buried inside them.
Once that data is mapped, cleaned and moved where it needs to be, AI has something reliable to work from. The result is AI your auditors, analysts and executives can trust, because every answer traces back to a governed source.
Oracle · SQL Server · DB/2 · files and application feeds
Informatica PowerCenter and IDMC · Pentaho · Stonebranch orchestration
Microsoft Azure · Amazon Web Services · governed, reconciled data
Retrieval assistants · LLM integrations · answers with sources
Assessments, migrations and data models led directly by Mithril's principal architect, from the first workshop to the final handover.
For larger AI and integration builds, Mithril pairs its architecture lead with an experienced partner development team. You get one contract and one point of accountability.
Experienced data, integration and ETL professionals placed on your team on contract, selected and supported by Mithril.
A fixed-scope review of your data estate. You leave with a clear picture of what is ready for AI today, what needs work, and a prioritized plan to get there.
Hal founded Mithril Data in 2007 and leads every engagement personally. His career spans airlines, banking, healthcare, pharmaceuticals, insurance and utilities, with projects in the United States, Europe, the Caribbean and Japan.
His work covers the full path data takes: database design, canonical data modeling, Informatica integration, workload orchestration, cloud migration and, today, the AI systems built on top of it.
A migration deadline, an AI idea that needs better data, or a pipeline that keeps breaking.
