Sri
CTO, Engineering & Platform Transformation
Services · Modernize and build
Data you can trust, ready for AI
Data Modernization as a Service: move off legacy data estates onto a modern platform, then keep it reliable, governed and cost-efficient, run by one accountable team.
AI is only as good as the data under it.
Most organizations want AI in production. Most of their data is not ready for it: spread across legacy warehouses, held together by fragile pipelines, and defined differently by every team that uses it.
Modernizing the platform is only half the job. The other half is keeping data reliable, governed and affordable every day after the migration.
That is the half we stay for.
Before and after
From data you can't trust to data that's ready for AI.
Before
- Legacy warehouses that are slow and costly to change
- Pipelines that break and nobody notices until a report is wrong
- The same number calculated three different ways
- Data locked away from the teams and agents that need it
- A migration project, then a separate team to run it
On the factory
- A modern platform on the cloud data stack you choose
- Pipelines monitored and fixed as part of the service
- One governed definition for the numbers that matter
- Access controlled, so people and agents get what they are allowed to see
- The same team migrates it and runs it, no handoff
Measured on outcomes agreed up front, such as pipeline reliability, data freshness and cost per workload.
Where are you starting from?
Starting point
Legacy warehouse
We plan the move workload by workload, migrate without stopping the reports people rely on, and retire the old platform when the new one has proved itself.
Starting point
On-premises databases
We bring scattered databases onto one governed platform, in the cloud or on-premises, with access rules carried over.
Starting point
Already on a modern platform
You have Databricks or Snowflake but not the results you expected. We stabilize pipelines, cut cost and run it as a service.
What's included.
Migration and the run that follows, as one service.
Assessment and roadmap
What you have, what to move first, and what to retire.
Migration
Workloads moved in planned waves, with reconciliation so numbers match.
Pipelines and orchestration
Reliable, monitored pipelines, including Airflow where it fits.
Airflow Managed Services
Data governance
Shared definitions, lineage and access controls.
Cost management
Compute and storage watched and tuned as part of the service.
Run and improve
An embedded pod keeps data fresh and reliable, with agents on routine fixes.
Ready for AI
Modern, governed data is what Enterprise Intelligence and production agents run on.
Changing the applications too?
One team from migration to run.
No handoff after go-live. The team that moves your data keeps it running.
Meet the leadership team
Data Modernization as a Service is delivered by our Engineering & Platform Transformation practice
FAQ
Questions buyers ask about Data Modernization as a Service
Which platforms do you migrate to?
The modern data platform that fits your estate, including Databricks and Snowflake, in the cloud or on-premises.
Will our reports break during the move?
We migrate in planned waves and reconcile numbers before switching anything off.
We already moved to a modern platform. Can you help?
Yes. Many clients come to us to stabilize pipelines, cut cost and run the platform as a service.
How does this help with AI?
Agents and Enterprise Intelligence are only as good as the data under them. Governed, reliable data is the foundation.
How is it priced?
On outcomes agreed up front, such as workloads migrated, pipeline reliability and cost per workload.
Start with the data that matters most.
Tell us which workload or report you can't afford to get wrong. We'll show you how we would move and run it.