APPLIED AI · RAG · ANALYTICS

We design AI data analytics systems

RAG systems connected to your documents, drives and databases. Natural language questions, cited answers and dashboards generated over live data.

Request a quote How we connect your data

We build in

Sources

Indexed

Google Drive
✓
SharePoint
✓
PostgreSQL
✓
BigQuery
✓

docs indexed1.2M

AI Analyst

Live

RAG

Your question

Revenue by channel in Q3 vs Q2?

Answer

Q3 closes at €2.84M, +18.4% over Q2. Marketplace and direct retail drive 71% of the growth.

Q3-Report.pdf·p. 12

Revenue by channel

MarketplaceRetail

Q1Q2Q3

Cited fragment

“Marketplace accounts for 42% of Q3 revenue, with 23% year-on-year growth driven by the new categories.”

Q3-Report.pdf

Finance · p. 12 · 2025

METHODOLOGY

We connect your data, reason over it and ship product

Ingestion and vector store over your real sources, RAG with verifiable citations and a product your team can operate.

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Data

Standard MCP servers for Drive, SharePoint, Confluence and Notion
Custom MCP servers for ERPs, CRMs and internal systems
Read access to PostgreSQL, BigQuery and Snowflake
Incremental ingestion, embeddings and production vector store
Permissions inherited from the source

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RAG

Hybrid retrieval: semantic and lexical
Reranking and filters by metadata and permission
Verifiable citations: document, page and fragment
Frontier models via API or self-hosted
Continuous evaluation with in-house datasets

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Product

Analytical chat embedded in your intranet or portal
Dashboards generated from natural language
Custom MCP servers to execute actions on your systems
Observability: traces, cost and quality per query
Roles, audit log and record of every answer

VALUE PROPOSITION

Your data, in natural language

RAG connected to Drive, SharePoint or your databases. Every answer cites its source, every dashboard regenerates over live data.

RAG over real data

CITED RETRIEVAL

We index your documents, contracts, reports and databases. The model answers with your business context, citing document, page and exact fragment.

AI-generated dashboards

CONVERSATIONAL ANALYTICS

Ask for a visualization in natural language and the AI builds the chart over live data. Iterate until the dashboard fits your team, no BI ticket required.

Connected to your sources via MCP

MCP · DRIVE · SHAREPOINT · SQL

Drive, SharePoint, Notion, PostgreSQL, BigQuery or Snowflake connected through MCP servers. When an internal system has no MCP server, we build one. AI reads where the data already lives, without duplicating pipelines or breaking permissions.

Verifiable answers

TRACE AND AUDIT

Every answer links to the document, row or SQL query that supports it. Traces, cost per query and a record of every interaction ready for audit.

SUCCESS STORIES

Companies that trust our team

Companies that trust us.

La Liga

Fermax

Casualarena

Azona

Pavasal

FAQ

Frequently Asked Questions about AI data analytics

Answers to common questions about sources, connectors, privacy, and production rollout.

How does this differ from traditional BI?
Traditional BI needs data models, prebuilt dashboards and a ticket every time the question changes. Here you ask in natural language and the AI retrieves the data, reasons over it and generates the chart. BI still wins for the fixed operational boards; AI covers the new question.
How do you avoid hallucinations?
Three layers: retrieval with reranking over your sources, instructions that force the model to cite, and an evaluator that flags answers without document support. If the data isn't there, the model says so. Every answer links to the fragment that supports it.
Which sources do you support and how do you connect them?
Drive, SharePoint, Confluence and Notion for unstructured data. PostgreSQL, MySQL, BigQuery and Snowflake for structured data. We connect through standard MCP servers when they exist, and we build custom MCP servers for ERPs, CRMs and internal systems without an open protocol. Your AI speaks one language across your entire stack.
What about data privacy?
Permissions inherited from the source: each user only sees what they already had access to. Deployment on your cloud or dedicated infrastructure, private model options when the data cannot leave, and a full record of every query for audit.
Who maintains the index and the connectors?
We deliver a system your team can operate, with scheduled sync, quality metrics and alerts when a source changes. We can keep it under SLA if you prefer, optional.

TECHNOLOGIES

Technology stack

Production RAG stack: frontier models, orchestration with standard and custom MCP servers, scalable vector stores and direct connectors to the sources where data already lives.

Frontier models