One model behind every number

When two teams report two different numbers for the same word, the problem is the model, not the dashboard. Brainforge builds business marts, a governed semantic layer, and definitions with owners so reporting and AI answer from the same logic.

In plain terms

What Data Modeling means in practice

Data modeling services from Brainforge. We build marts, semantic layers, and governed metric definitions so every team and every AI query agrees on the numbers.

Where teams get stuck

Build marts, semantic layers, and governed metric definitions teams can trust.

Every dashboard shows a slightly different number for the same metric.

Analysts rebuild the same logic in every tool because there is no shared model.

Nobody owns metric definitions, so arguments repeat each quarter.

Legacy models are undocumented and too fragile to change.

What we deliver

Core Data Modeling

Business and mart modeling

Business-ready marts and models that finance, marketing, and operations can query without waiting on an analyst.

Semantic layer and metrics

Governed metrics, dimensions, and entities that BI and AI consume from one shared definition.

Modeling modernization

Legacy models refactored and standardized so your team can change them safely.

How deep it goes

Capabilities behind the work

The same delivery primitives (context, controls, and review) show up across every engagement.

Business and mart modeling

The modeled layer that makes reporting consistent instead of dependent on one person's query.

Dimensional modelingMart designProduct and event models

Semantic layer and metrics

Agreeing what each number means before anyone builds a tile.

Metric definitionsSemantic layer designDefinition ownership

Modeling modernization

Cleaning up legacy logic so the model can evolve without fear.

Legacy refactorStandardizationTesting and documentation

Model reliability

Tests and contracts that keep the model dependable as it grows.

Data testsData contractsLineage

What changes

Outcomes you can point to

Marts and models business teams can query directly.
One semantic layer with governed dimensions and metrics.
Documented definitions with a named owner per metric.
Modernized models your team can change safely.

Proof in production

See where this work shows up.

From single warehouse to a nationwide fulfillment advantage

A unified shipment data model that gave full visibility across 3PLs and carriers.

Read the shipment data model case study →

How we saved a retailer hundreds of thousands

A governed warehouse model that put a true cost per shipment behind every carrier decision.

Read the governed model case study →

Common questions

Data Modeling, straight answers

What is a semantic layer and do we need one?

A semantic layer defines metrics, dimensions, and entities once so BI, analytics, and AI query the same logic. Teams need one when the same metric is defined differently across tools and dashboards.

Do you only work in dbt?

No. dbt is common, but we also work in warehouse-native transformation and other modeling tools. The choice follows your stack and team, not ours.

Can you clean up models we already have?

Yes. Refactoring legacy marts and standardizing logic is a core engagement. We document what exists and change it safely rather than rebuilding for its own sake.

How do you stop metric definitions from drifting?

We assign a named owner per metric and set a review cadence, so definitions have a home and a process. Drift is a governance problem before it is a tooling problem.

How is this different from analytics engineering?

The work is the same discipline. We package it around outcomes, a governed model set and a semantic layer, with a bounded scope your team can take over.

How we work