ddyta.ai
/services/data-insights/services/data-insights/natural-language-to-sql
coreSTATUS: PIPELINEIn-house delivery

Natural-Language-to-SQL

Let non-technical staff query data in plain English

We build a semantic interface between your team and your database. Non-technical users ask questions in plain English and get accurate, validated SQL results — with guardrails to prevent unsafe queries, cost overruns, or data leaks.

OpenAIPostgresdbtPython
// service_overview
Status
Pipeline
Tier
core
Delivery
In-house
Steps
4-phase engagement

[STATUS: PIPELINE] — This capability is in active development. Design-partner slots are open. Contact us to co-develop it against your workload.

/01Capabilities

What we deliver.

Every capability is engineered for production from day one — tested, documented, and ready to deploy.

  • Schema-aware natural language query synthesis
  • Query validation and cost guards
  • Row and column level access enforcement
  • Explainable query plans in plain English
  • Continuous accuracy improvement from user feedback
/02Engagement Process

How the engagement runs.

P.01

Schema Modeling

Map your database schema and business metrics into a semantic layer.

P.02

Query Engine

Build the NL-to-SQL synthesis pipeline with validation guardrails.

P.03

Access Controls

Implement permission-aware query routing and cost limits.

P.04

Rollout

Onboard users, monitor accuracy, and tune the system.

/03Deliverables
D.01

Natural language query interface

D.02

Semantic layer configuration

D.03

Access control and audit logging

D.04

User onboarding guide

Ready to put this into action?

Tell us about your project and constraints. We'll respond with a technical point of view.

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