.png)
Just Ask.
Datalysis sits on top of your existing lakehouse. Business users ask plain-language questions; the platform writes the SQL, runs the query, returns the answer, and exports straight to the dashboards and reports you already use. No new warehouse. No new analytics tool. No analyst queue.
Most business users still cannot answer their own data questions.
Enterprise data investment has scaled. Lakehouses are in place. Dashboards are built. BI licenses are paid for. But the day-to-day reality for most business teams has not changed. When the dashboard does not answer the question, they email an analyst. When the analyst is busy, the question waits. By the time the answer arrives, the decision has been made.
Adding another self-service tool rarely solves it. Business users do not want to learn SQL, drag-and-drop a query builder, or wait for IT to model a new dataset. They want to ask the question they have,
Built directly on the SQL endpoint. Tuned to the data, not the demo.
Datalysis started from a working hypothesis. The natural language layer should not own the semantic model. It should read the same lakehouse the analysts already trust, write queries against the same SQL endpoint, and return answers that survive the validation step. The platform should be a layer over the data warehouse, not a parallel one.
The research focused on three things. How to give a foundation model enough domain context to write queries that actually work, without retraining the model. How to validate the queries before execution to catch hallucinated joins and incorrect filters. And how to expose the working set of business terminology so that an operations director and a finance lead can ask different questions and both get useful answers.
We tested retrieval-augmented generation against fine-tuning, prompt engineering against schema injection, and multiple foundation models against the same real client datasets. The architecture that emerged is deliberately thin: a RAG layer with domain-specific embeddings, query validation, and an output renderer. Most of the work is in the catalogue and the validation. The model itself is replaceable.
Compared RAG, fine-tuning, and prompt engineering on real client lakehouses
Built query validation to catch hallucinated joins and filter errors
Engineered domain-specific embeddings on business terminology and schema
Tested across Databricks, Snowflake, MS Fabric, and BigQuery endpoints
Designed the model layer to be swappable as foundation models evolve
A thin, accurate layer over the lakehouse you already have.
Datalysis does the work between a business question and a useful answer. It interprets what was asked, finds the right data, writes the query, validates it before execution, and returns the answer as a chart, a table, an Excel export, or a dashboard widget. The platform sits over your existing lakehouse without replacing anything.
Plain-language queries
Business users ask questions the way they would ask an analyst. The platform handles the translation to SQL and the joins behind it.
Query validation
Every generated query passes through a validation layer that catches hallucinated joins, incorrect filters, and aggregation errors before execution.
Multiple output formats
Charts, tables, Excel exports, dashboard widgets, or PDF reports. The output matches how the user wants to act on the answer.
Source citation
Every answer carries the underlying query, the data sources, and the timestamp. Users can verify or hand off to an analyst with full context.
Domain tuning
The platform learns your terminology. "Revenue" means what your finance team means by it, not what a generic model assumes.
No lakehouse migration
Datalysis sits on the SQL endpoint of your existing platform. No replatforming, no parallel pipelines, no duplicate governance.
Sits over your lakehouse. Connects to your tools. Operates inside your governance.
Datalysis is designed to integrate with the data and analytics stack you already run. The platform reads from the same SQL endpoints your analysts use and writes outputs into the same tools your business teams already work in.
Language models
Multi-model orchestration
OpenAI · Anthropic · open-source
Swappable as models evolve
Lakehouse endpoints
Databricks · Snowflake
Microsoft Fabric · BigQuery
Standard SQL warehouses
Output integrations
Power BI · Tableau
Excel · Google Sheets
Email and scheduled reports
Validation layer
Query parsing and verification
Join and filter checks
Result sanity tests before delivery
Security
ISO 27001 controls
Inherits lakehouse row-level security
SSO · MFA · audit logging
Deployment
Client tenant or managed service
Azure, AWS, hybrid
Inside your existing data perimeter