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The foundation that everything else demands.

Without clean, governed data, AI is theater. This is the layer that supports agents, automation and analytics.

Assess readiness Other solutions
What we do

Data that serves the model, and the business.

We build the data layer with consumption by both humans and agents in mind — governed and versioned.

Data pipelines

Ingest, transform, quality. The layer that makes any reliable model viable.

Enterprise RAG

Vectorized stores, per-document access control, verifiable citations.

Semantic layer

Versioned metrics and dimensions. Single source, queryable in natural language.

Conversational dashboards

Self-service AI analysis. Auditable answers, with generated and validated SQL.

Thesis

AI without a data layer is theater.

Good model on top of bad data only amplifies noise. Before any agent, automation or conversational layer, comes governance, quality and semantics. It's the foundation that decides whether AI will be a differentiator or technical debt.

By the numbers

What governance and semantic layer unlock.

−0%time to first insight
0%data quality on critical pipelines
<R$ 0average cost per semantic query
Zerosensitive data in prompts after redaction
How we build

From ingestion to conversational consumption.

  1. D1

    Readiness diagnostic

    Source map, quality, current governance and gaps. We identify where friction will appear before starting.

    Output Per-domain readiness score.

  2. D2

    Pipelines with contracts

    Every transformation has declared tests. Pipeline only advances on contract pass.

    Output Quality SLOs per dataset.

  3. D3

    Semantic layer

    Versioned metrics and dimensions, queryable in natural language. Single source of truth.

    Output Governed semantic layer.

  4. D4

    Enterprise RAG

    Vectorized stores with per-document ACL, synced from AD/Workspace in real time.

    Output Auditable per-user citations.

  5. D5

    Conversational analytics

    Askable dashboards. SQL generated and validated, with answer explanation.

    Output Auditable self-service.

Under the hood

Modern data, no black box.

We work with the stack you have. Where it makes sense, we recommend additional layers — always justifying cost and benefit.

Warehouse

  • Snowflake, Databricks, BigQueryCover 90% of cases.
  • Postgres / AuroraSmaller workloads.
  • Iceberg / DeltaOpen format.

Pipelines

  • dbt + AirflowIndustry standard.
  • DagsterAsset-based.
  • Fivetran / AirbyteSaaS ingestion.

Vector + RAG

  • pgvector, PineconeVector store.
  • Cohere rerankRetrieval quality.
  • Unstructured.ioDocument preprocessing.

Semantic + BI

  • Cube.devVersioned semantic layer.
  • Lightdash, MetabaseConversational BI.
  • Hex, StreamlitAd-hoc analysis.
In production

The layer that sustains everything else.

−85%

Corporate semantic layer

Versioned dimensions and metrics, queryable in natural language. Finance team stopped requesting reports from data team and started asking directly.

99.4%

Auditable-quality pipelines

Every transformation has declared quality tests, and the pipeline only advances on contract pass. Failures alert before reaching downstream consumers.

100%

Enterprise RAG with per-document ACL

Each user only sees citations from documents they have permission for. Permissions sync from AD/Workspace in real time.

Vs. alternatives

When custom build beats off-the-shelf.

LA AI · custom buildOwn semantic layer
SaaS toolLooker, Sigma, etc.
Legacy BIPower BI, Tableau standalone
Per-domain customization
Enterprise-RAG integration
Per-document LGPD governance
Predictable cost per query
Time-to-first-insight
−85%
−40%
baseline
Frequently asked

About data & analytics.

Does it work with our current stack (Snowflake, Databricks, BigQuery)?

Yes. We work with the common warehouses. Generally we preserve what's well implemented and add the semantic layer + RAG on top.

Do you do data warehouse migrations?

When it makes sense. Migration only for AI rarely justifies. With another driver (cost, performance), we add it to the roadmap.

How do you ensure privacy in enterprise RAG?

Per-document ACL synced from AD/Workspace, automatic PII redaction in prompts, and per-query logs with user + retrieved documents. Auditable.

How much does a semantic layer cost to run?

Cost varies with query volume. For 500+ person companies, typically below R$ 0.01 per query — fraction of a one-off dashboard.

Next step

Ready to take your AI from the lab and into production?

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Data & Analytics · The foundation AI demands