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Conversational Data Intelligence – Text-to-SQL Analytics | MindzKonnected
Case 04 Text-to-SQL agents & self-serve analytics

Conversational data-intelligence platform

A conversational agent that turns plain-language questions into governed SQL against your live database, tailored to each user’s role, so non-technical users explore the data themselves.

Innovation & investment ecosystem · name withheld (NDA) Self-serve, no analyst required
ASKED IN PLAIN LANGUAGE“Which portfolio companies grew headcount last quarter?”GOVERNED SQL · GENERATEDSELECTc.name, h.deltaFROMcompanies c JOIN headcount h ON …WHEREh.quarter = 'Q1' AND h.delta > 0Role-based access checkRESULT · NO ANALYST REQUIREDCOMPANYHEADCOUNT ΔSTAGECompany A+42Series BCompany B+18SeedCompany C+9Series A
Plain language in · governed SQL out

The problem

Investors, partners, and analysts were sitting on a rich company database they couldn’t actually use. Every question meant waiting in line for someone to write SQL and build a report, so most questions never got asked and the data went to waste.

What we built

We built a conversational agent that turns plain-language questions into governed SQL against the live database, tailors every answer to the user’s role, and remembers context across a whole conversation. Now non-technical users explore, sort, count, and group the data themselves, with guided prompts to get started and a saved, searchable history.

Results

  • Natural-language questions translated into governed SQL over your live database
  • Role-aware answers tailored to each type of user
  • Multi-turn conversation memory with saved, searchable history
  • Self-serve discovery and matching, with no analyst or hand-written SQL required

Deployment

Web-based chat layered over your existing structured data, with role-based access control.