Most enterprises run one stack for reporting, another for machine learning, and a scramble of tools for AI, with governance bolted on at the end. The Databricks Data Intelligence Platform replaces that with one governed lakehouse for data engineering, analytics, and AI. Flentas designs, migrates, and operates it on AWS, so your teams work from a single, trusted copy of the data, all the way through to production AI.
Most enterprises run one stack for reporting, another for machine learning, and a scramble of tools for AI, with governance bolted on at the end. The lakehouse replaces that with one governed platform, end to end.
Data engineering, analytics, machine learning, and AI on a single open lakehouse, not three systems stitched together after the fact.
Unity Catalog governs data, models, and metrics in one place, with lineage, fine-grained access, and consistent metric definitions.
Built on Delta Lake and open formats, so your data stays portable and your options stay open.
Mosaic AI and Agent Bricks build domain-specific AI agents grounded in governed lakehouse data — not disconnected from it.
We prove value on one governed foundation, then extend the same platform and pattern to analytics and AI. It is the discipline behind data and AI programs that scale rather than stall.
We review your data and AI estate, design the target lakehouse architecture, and hand you a prioritized roadmap with the first use cases and a business case.
We stand up the lakehouse on AWS, migrate priority workloads, build governed pipelines and Unity Catalog governance, and put a first analytics or AI use case into production.
We extend into self-service analytics with Genie and production AI with Mosaic AI and Agent Bricks, and operate the platform with cost and quality under control.
Delta Lake and open formats, standing up the platform on AWS and migrating off legacy warehouses.
Streaming, batch, and change-data-capture pipelines with Lakeflow, with data quality and observability built in.
Unity Catalog governs data, models, and metrics with lineage, fine-grained access, and consistent definitions.
Databricks SQL for BI, and AI/BI Genie for natural-language questions from business teams.
Mosaic AI trains, serves, and governs models; Agent Bricks builds production-ready, domain-specific AI agents.
Right-sized serverless compute and visible spend, so scale does not mean runaway cost.
Aging, costly, and rigid warehouses migrated to the lakehouse with tooling-assisted, predictable migrations.
Aging, costly, and rigid warehouses migrated to the lakehouse with tooling-assisted, predictable migrations.
Models and agents are ready, but the governed context they need is not
the lakehouse gives them trusted, well-described data to ground on.
One stack for reporting, a different one for machine learning, and ad-hoc AI tooling
unified onto a single governed platform.
Financial services and other regulated sectors need governance and lineage built in from day one, not stitched on after.
Fintech / Payments
100%
Cashew Payments had no data-lake strategy and reporting queries were degrading production performance. Flentas built change-data-capture into a governed S3 data lake with a Delta Lake layer — a ready bridge to a Databricks lakehouse.
Enterprise / Security
500M+
HPE needed unified governance and open formats at high volume. Flentas built a cloud-agnostic, OCSF-governed data platform across roughly 2,500 accounts processing more than 500 million events per day.
“We had a warehouse for reporting, a separate cluster for ML, and three different opinions about what "active users" meant. Flentas stood up the lakehouse, put Unity Catalog around all of it, and for the first time the data science team and the BI team are querying the same governed tables. Our first agent went from pilot to production in six weeks.”
Head of Data PlatformData-Intensive Enterprise, AWS Estate
A data and AI assessment reviews your estate, designs the target Databricks lakehouse on AWS, and hands you a prioritized roadmap from first pipeline to governed AI.