A clear breakdown of what Brainforge fixes, how fast, and what it actually delivers.
Lakehouse implementation
Unity Catalog, workload organization, and data architecture so the lakehouse is safe to use broadly.
Databricks consulting that turns a powerful platform into a governed system
Databricks can run anything, which is exactly why it gets messy. Brainforge delivers the discipline that makes lakehouse platforms work: Unity Catalog governance, workload planning, cost controls, and Mosaic AI enablement grounded in governed data.
In plain terms
What Databricks means in practice
Where teams get stuck
Databricks lakehouse setup, Unity Catalog governance, Mosaic AI, and cost management.
Databricks spend is unpredictable and clusters are left running.
We have a lakehouse but no governance, so nobody trusts it.
AI experiments don't connect to governed production data.
We're comparing Databricks to Snowflake and need an honest architecture answer.
What we deliver
Core Databricks
Cost and workload optimization
Cluster sizing, workload review, and governance that keeps Databricks spend predictable.
Mosaic AI enablement
Agent and GenAI foundations built on governed lakehouse context, with evaluation baked in.
How deep it goes
Capabilities behind the work
The same delivery primitives (context, controls, and review) show up across every engagement.
Lakehouse architecture
Unity Catalog, medallion architecture, and workload organization built to scale.
Unity CatalogMedallion architectureWorkload organization
Cost governance
Cluster sizing, tags, and consumption reviews that bring Databricks spend under control.
Cluster sizingConsumption reviewBudget guardrails
Mosaic AI enablement
Agent and GenAI foundations grounded in governed lakehouse context.
Mosaic AIAgent foundationsEvaluation
Migration and consolidation
Move from legacy platforms, or from a competing warehouse, onto one governed lakehouse.
Migration planningValidationStack consolidation
What changes
Outcomes you can point to
- A governed lakehouse with clear data ownership.
- Predictable compute costs and right-sized clusters.
- AI and BI workloads running on the same trusted data.
- Mosaic AI workflows with evaluation, not just demos.
Common questions
Databricks, straight answers
How much does Databricks consulting cost?
We scope by outcome — a lakehouse audit, a Unity Catalog governance sprint, or a Mosaic AI enablement build. Each is fixed-scope, so you approve a bounded cost before work starts.
Can you reduce our Databricks bill?
Yes. We run consumption and workload reviews that right-size clusters, add budget guardrails, and put cost visibility in front of owners so spend stays predictable.
Do you work with existing Databricks environments or greenfield?
Both. A common engagement is rescuing an existing lakehouse that grew without governance, adding Unity Catalog and structure without throwing out what works.
Can you help us decide between Databricks and Snowflake?
We can. We run honest architecture comparisons grounded in your workloads, AI needs, and team skills rather than vendor preference.
Do you build AI features on Mosaic AI?
We do, with the same discipline as our other AI work: governed data context, clear evaluation criteria, and human-in-the-loop controls.
How we work
A path from pressure to a working system
01
Review workloads and spend
We map clusters, workloads, and governance gaps to find where Databricks is leaking cost and trust.
02
Build the governed lakehouse
We set up Unity Catalog, architecture, and controls so the platform is safe to adopt broadly.
03
Enable AI and analytics
We wire BI and Mosaic AI to the same governed data with evaluation in place.
Our Trusted Partners
We only bring the best of the best
READY TO PUT
Databricks to work?
Databricks to work?
In one working session we'll name what's broken, what's possible, and the first system worth building.
