Revoleap + Databricks

Your data has a bigger job.
Let’s put it to work.

We build on Databricks to connect engineering, analytics and AI around trusted data. One considered foundation, with useful data products and clear responsibility for what reaches the business.

Build with usExplore the capabilities

For the teams who want their data to do more.

Platform possibility.
Revoleap craft.
Give every source a reliable path.

Ingestion, streaming and replayable pipelines.

01 / What we build

Deep work.
Across the platform.

We share the belief that data and AI belong together. Our contribution is the hard engineering between source systems, governed products and decisions people can depend on.

Explore our engineering capabilities.

01Lakehouse foundations & migrationBuild a platform around the data you need to serve.

Workspace and storage design, domain boundaries and a migration plan for existing warehouses or lakes. We validate compatibility, reconciliation and operating costs before cutting over consumers.

Delta Lake · Apache Iceberg · Cloud storage
02Ingestion, CDC & streamingKeep business data moving with a way to recover.

Source onboarding, incremental loads and streaming pipelines with explicit freshness targets. We design schema evolution, replay, deduplication and quality checks into the ingestion path.

Lakeflow Connect · Spark Declarative Pipelines
03Data products & orchestrationDeliver reusable data with an accountable owner.

Transformation pipelines, contracts, orchestration and observability. We work with business domains to define trusted outputs and the support model behind them.

Lakeflow Jobs · Spark · Data quality
04Governance & secure sharingLet the right people find and use the right data.

Catalog design, access policies, stewardship and controlled sharing. We connect permissions to actual data consumption and make ownership visible across analytics and AI.

Unity Catalog · Lineage · Delta Sharing
05Analytics & business intelligenceMake the next decision easier to explain.

Semantic definitions, performance tuning and curated analytics experiences. We evaluate natural-language answers against agreed business measures and retain traceable source logic.

Databricks SQL · AI/BI · Genie
06Machine learning & model servingCarry the model from experiment to working service.

Reproducible experiments, model registration, deployment and monitoring. We connect release criteria to data quality, inference behavior and the team that will run the model.

MLflow · Feature engineering · Model Serving
07Agents & generative AIGround AI in the data your business trusts.

Retrieval, agent tools and governed model access with trace-based evaluation. We test relevance, authorised access and downstream actions before extending a workflow.

Agent Bricks · Vector Search · AI Gateway
08Data applications & platform operationsMake the platform useful beyond the notebook.

Business-facing applications and operational state, backed by repeatable deployment. We design workspace controls, workload monitoring and cost attribution around the application’s lifecycle.

Databricks Apps · Lakebase · CI/CD
02 / Engineering blueprints

See the pieces
work together.

Starting architectures for real business needs. We shape the implementation around your systems, constraints and operating team.

Trusted data products

From source change to a shared business measure.

For enterprises replacing brittle pipelines and conflicting reports.

  1. 01 / IngestLakeflow Connect + source CDC
  2. 02 / RefineValidated Delta tables
  3. 03 / GovernUnity Catalog + ownership
  4. 04 / ServeSQL + curated business metrics

Control points

Quality gates, reconciled source counts and access policies.

What we leave with you

Versioned pipelines, contracts and a domain-owned data product.

How we validate it

Reconcile completeness, freshness and the agreed business measure.

Governed enterprise AI

Useful answers from an accountable data estate.

For teams connecting enterprise knowledge to an AI workflow.

  1. 01 / PrepareGoverned documents + tables
  2. 02 / RetrieveVector Search + permissions
  3. 03 / ReasonAgent Bricks + model access
  4. 04 / EvaluateMLflow traces + review

Control points

Permission checks, source provenance and scoped actions.

What we leave with you

Retrieval configuration, agent tools and repeatable evaluations.

How we validate it

Test answer relevance, permitted access and failure handling.

Operational data application

Put decisions and actions in the same working view.

For planning, service and operations teams needing more than a dashboard.

  1. 01 / UnderstandCurated data + business rules
  2. 02 / ExperienceDatabricks Apps interface
  3. 03 / PersistLakebase application state
  4. 04 / OperateAccess + CI/CD + monitoring

Control points

User identity, transaction boundaries and audited write paths.

What we leave with you

An application, deployment pipeline and operational runbook.

How we validate it

Validate business rules, concurrent updates and recovery behavior.

03 / Engineering with accountability

The data product is only useful when someone trusts it.

We bring platform engineering and business context into the same conversation, from the first source connection to the team using the result.

Explore Data Engineering
01 / Decide

A useful first scope.

The business task, system boundaries and acceptance criteria, agreed together.

02 / Build

Work beside your team.

Visible engineering, reviewed decisions and working increments you can assess.

03 / Transfer

Leave lasting capability.

Code, operating guidance and the knowledge to own what comes next.

Explore the platformLakeflow data engineering Applications and agents Unity Catalog Model Serving
Where to start

Let’s start with the work
that matters.

A working conversation about the opportunity, the constraints and a useful first step.

Begin a conversationThe Leap Assessment / 90 minutes