Most data platforms start clean and decay: ungoverned tables, broken pipelines, and no single definition of a metric. Brainforge builds and repairs the pipeline layer — warehouse architecture, ELT, modeling, and reliability — so every team queries the same trusted data.
In plain terms
Where teams get stuck
Our warehouse is a pile of raw tables nobody trusts or documents.
Pipelines break silently and nobody owns them.
Every dashboard shows a slightly different number.
We are blocked from AI and self-service because the data underneath is a mess.
What we deliver
Design and implementation of the platform layer — Snowflake, Databricks, BigQuery — with governed roles, ingestion, and modeling.
Reliable ingestion and transformation pipelines with contracts, observability, and owners.
dbt transformations, business entities, and metric definitions so BI and AI agree on the numbers.
How deep it goes
The same delivery primitives (context, controls, and review) show up across every engagement.
Warehouse and lakehouse design that is governed from day one, not retrofitted.
ELT and ETL pipelines with contracts, lineage, and monitoring that catch failures early.
dbt transformations and metric definitions so one number means the same thing everywhere.
Testing, lineage, and alerting that keep the pipeline layer from decaying.
Honest stack comparisons and migrations when the current platform is the problem.
What changes
Proof in production
How governed logistics data turned a fragmented pipeline into a reliable operations backbone.
Read the shipping data case study →A full data stack build from warehouse to governed BI for a high-velocity retailer.
Read the ecommerce analytics case study →Common questions
Engagements start with a scoped audit or implementation sprint, so you pay for a bounded outcome. Most teams begin with a stack audit or a first governed model set, then scale once the pipeline layer is stable.
We work with what you have. Snowflake, Databricks, BigQuery, dbt, Airflow, Fivetran, and the modern data stack tools are all familiar ground, and we recommend honestly when a change actually pays for itself.
Both. Rescuing a decaying pipeline layer is a core engagement — we add contracts, monitoring, and ownership to what exists before deciding anything needs to be rebuilt.
A hire is open-ended. A consulting engagement delivers a bounded outcome — a governed warehouse, working pipelines, a semantic layer — and enables your team to maintain it, usually faster than a first hire could alone.
Yes. The same governed data foundation is what makes RAG, agents, and AI features reliable, so we build the pipeline layer with AI consumption in mind.
How we work
We map pipelines, tables, ownership, and pain so the fix list is grounded in what is actually broken.
We stand up the platform, contracts, and modeling that make the stack safe to scale.
We hand over documentation, runbooks, and training so your team operates and extends the system.
Our Trusted Partners
In one working session we'll name what's broken, what's possible, and the first system worth building.
