“What was our employee turnover over the last three months?”
Headcount, leave and employee movement questions-if the required records exist in the connected source.
ON-PREMISE ENTERPRISE AI · ThinkHub AI Studio
Query your business data in plain language.
ThinkHub AI Studio connects to your existing databases and turns business questions into queries your systems can understand. It runs on company infrastructure and uses an open-source DeepSeek model.
- On-premise
- DeepSeek
- Natural language → SQL
QUESTIONS FROM DAILY WORK
The user asks the question. ThinkHub handles the query.
Teams do not need to know table names, joins or SQL syntax. They ask about the data in the language they already use at work.
“How did purchasing trends change compared with last year?”
Supplier, category, volume and period comparisons using the company’s own definitions.
“Which expense items exceeded the planned budget this month?”
Questions across budget, invoice or payment data that the user is authorised to access.
HOW IT WORKS
What happens behind the chat screen?
ThinkHub does more than send a question to a model. It needs the right schema context, a controlled database connection and a clear way to inspect the result.
The user writes a business question in plain language.
Relevant tables, fields and relationships provide context.
DeepSeek prepares SQL for the selected data source.
The query result is returned in a readable form.
Generated SQL can be wrong. This is why the data scope, permissions and expected answers are tested before wider use.
DEPLOYMENT & DATA BOUNDARIES
Built to run inside your infrastructure.
In an on-premise setup, the application, DeepSeek model and database connection are placed within the company environment. This reduces external data exposure, but deployment location alone is not a security policy.
Read-only first
A PoC can begin with a database user that cannot update or delete records.
Defined data scope
Only the approved schemas, tables or views are made available to the product.
Query limits
Timeouts, row limits and expensive-query rules are set for the selected database.
Access and logging
Authentication, user permissions and logging are aligned with the organisation’s infrastructure.
WHERE IT FITS
A natural-language layer for data you already have.
ThinkHub does not replace the database or the reporting tools that already work. It adds a simpler way to explore recurring business questions. Data quality and access rules still come from the underlying systems.
FOCUSED PROOF OF CONCEPT
Start with one data source and real questions.
A focused PoC shows whether ThinkHub understands your schema and your company’s terminology before the scope grows.
Plan the PoC- 01
Choose one department and one database.
- 02
Define the accessible schemas, views and permissions.
- 03
Prepare real questions and expected results with the business team.
- 04
Compare accuracy, response time and access boundaries.
FAQ
Questions about ThinkHub AI Studio
ThinkHub AI Studio is designed for on-premise deployment. The application, model and database connection can remain within the company infrastructure. The final boundary still depends on the selected integrations and deployment architecture.
Compatibility depends on the database driver, API access and schema structure. We review the selected data source before a PoC instead of promising a generic connector list.
For an initial PoC, we recommend a read-only database user. Any workflow that writes or updates data should be designed separately with explicit permissions, validation and approval rules.
The model can generate an incorrect SQL query or misinterpret a business term. We test ThinkHub with real questions and expected results from the relevant team. Critical reports should continue to be checked against their source.
One department, one data source and a small set of real questions are enough. This makes it possible to evaluate query accuracy, response time and access boundaries before expanding the scope.