Original projectNatural language to SQL90% token reductionSafe execution

LLM-Powered Semantic Query Agent

A governed natural-language query layer for large databases. It retrieves only the relevant schema, generates and dry-runs SQL inside explicit safety rules, and cuts prompt-token use by roughly 90% compared with sending the full schema.

Origin: Original project
Role: Product, design & technology
Focus: Application
Engineering Area: AI & ML
Domain: Business & operations
Status: Prototyped

Why this should exist

Natural-language data access fails when context is expensive and execution is unsafe

Sending an entire database schema to a language model wastes tokens and weakens relevance. This agent retrieves only the schema needed for each question, validates the SQL, and keeps execution inside explicit safety rules.

Problem and product

The problems

01
Large schemas consume prompt space and distract the model from relevant tables.
02
Generated SQL can be syntactically valid but operationally dangerous.
03
Users need useful answers without learning the structure of the database first.

What the product delivers

Converts plain-English questions into database-ready SQL
Retrieves only relevant tables and columns through semantic schema search
Uses rule-based intent checks before invoking the generation pipeline
Builds pgvector embeddings for schema-aware retrieval
Generates SQL with an LLM using bounded, relevant context
Runs EXPLAIN dry-run validation before executing generated queries

Technology used

A stack chosen for the product

PostgreSQL and pgvector store semantic representations of tables and columns. A rule-based intent gate controls when the LLM runs, retrieved schema context bounds generation, and EXPLAIN validation checks the query before execution.

LLMGenerative AIpgvectorPostgreSQLSemantic SearchSQLVector Embeddings

Architecture

How the system holds together

01
Intent gate
Rules identify database questions and reject requests that do not belong in the query pipeline.
02
Semantic schema retrieval
Vector search returns only the tables and columns relevant to the user's language.
03
Safe SQL path
The LLM generates within bounded context, then dry-run checks and execution policy protect the database.

Estimated goals

A governed query layer for large enterprise schemas

The approach can become a reusable service for internal analytics where accuracy, cost, and auditability all matter.

01
Evaluate retrieval and SQL accuracy across more schema patterns.
02
Add policy-aware permissions at table and column level.
03
Measure cost and latency against full-schema and manual query baselines.

Future goals are directional estimates and depend on user validation.

Extras

Product perspective

Primary audience: Analysts and internal teams querying complex databases
Measured signal: Approximately 90% fewer prompt tokens than full-schema injection
Product edge: Retrieval efficiency and execution safety form one query pipeline

Execution principle

Validate the narrow customer promise first. Add scope only when evidence shows that it improves the outcome.

Full project notes

Product and engineering deep dive

The project overview adds a concise presentation layer. The complete product details remain below.

From intent to implementation

A product direction shaped around a real use case.

An AI query agent that converts plain English into safe SQL using pgvector-based semantic schema retrieval, reducing prompt-token cost by roughly 90% compared with injecting an entire database schema.

01 · The product response

What people can do with it

Converts plain-English questions into database-ready SQL
Retrieves only relevant tables and columns through semantic schema search
Uses rule-based intent checks before invoking the generation pipeline
Builds pgvector embeddings for schema-aware retrieval
Generates SQL with an LLM using bounded, relevant context
Runs EXPLAIN dry-run validation before executing generated queries
Cuts prompt-token consumption by approximately 90% over full-schema injection

Project sponsorship

Fund this project—from prototype to production.

Help move LLM-Powered Semantic Query Agent from a promising prototype to a reliable production release.

Design validation and user research
Production infrastructure and security
Testing, accessibility, and launch readiness

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