Client
AeroSQL
Duration
Ongoing
Year
2025
Next-Generation NLP-to-SQL Architecture
AeroSQL turned natural language into a secure, production-grade data interface — with zero hallucinated SQL, deterministic JOIN logic, role-enforced access control, and the concurrency headroom to serve an entire enterprise without a single data engineer in the loop.
Challenges
Locked Data, Broken Queries, Zero Guardrails
Manual SQL Bottleneck
Business users dependent on data engineers for every query — slow turnarounds, blocked decisions
LLM Hallucinations
Vanilla NL-to-SQL models invent column names, fake time ranges, and generate silently wrong queries
Broken JOIN Logic
Flat schema context causes LLMs to write incorrect JOIN conditions, producing corrupted result sets
No Access Control
No mechanism to prevent users from querying tables or columns outside their role permissions
No Caching Layer
Every query hits the LLM fresh — redundant API spend and unnecessary latency on repeated questions
Cannot Scale Concurrently
Single-threaded inference collapses under enterprise load; no dynamic scaling for simultaneous users
Solution
A Multi-Agent NL-to-SQL Pipeline — Built for Accuracy, Security & Scale
Technovate Global designed and deployed a production-grade NLP-to-SQL engine with semantic caching, RBAC security, a knowledge graph JOIN layer, and a self-correcting validation loop — all orchestrated across a multi-model inference stack.
Prompt Expansion & Vagueness Guard
Evaluates every prompt against a required-parameter checklist; halts and requests clarification before a vague prompt ever reaches the LLM
Prompt Expansion & Vagueness Guard
Evaluates every prompt against a required-parameter checklist; halts and requests clarification before a vague prompt ever reaches the LLM
Semantic Prompt Cache
HNSW vector search returns pre-computed SQL instantly on near-identical queries (≥99.999% match) or routes to a lightweight Query Tweak Agent (90–99% match)
Semantic Prompt Cache
HNSW vector search returns pre-computed SQL instantly on near-identical queries (≥99.999% match) or routes to a lightweight Query Tweak Agent (90–99% match)
RBAC Security Layer
Role-access matrices physically starve the LLM of unauthorised table and column context — the model cannot leak what it cannot see
RBAC Security Layer
Role-access matrices physically starve the LLM of unauthorised table and column context — the model cannot leak what it cannot see
RAG Context Pipeline
Intent Agent classifies the query; Table Agent selects relevant tables; Column Prune Agent strips irrelevant columns to minimise token cost
RAG Context Pipeline
Intent Agent classifies the query; Table Agent selects relevant tables; Column Prune Agent strips irrelevant columns to minimise token cost
Knowledge Graph JOIN Engine
Deterministic ER map of all table and column relationships guarantees mathematically correct JOIN paths are injected directly into the final prompt
Knowledge Graph JOIN Engine
Deterministic ER map of all table and column relationships guarantees mathematically correct JOIN paths are injected directly into the final prompt
Self-Correcting Validation Loop
Generated SQL is executed as an EXPLAIN query against a read-only replica; any error is appended and fed back to GPT-4o to self-correct, repeated n times
Self-Correcting Validation Loop
Generated SQL is executed as an EXPLAIN query against a read-only replica; any error is appended and fed back to GPT-4o to self-correct, repeated n times
Multi-Model Cost Routing
Open-source models (Llama 3.1 / Qwen 2.5) handle all classification agents at a fraction of the cost; GPT-4o reserved solely for final SQL generation
Multi-Model Cost Routing
Open-source models (Llama 3.1 / Qwen 2.5) handle all classification agents at a fraction of the cost; GPT-4o reserved solely for final SQL generation
Enterprise Concurrency Layer
Async API threads with Kubernetes HPA auto-scaling on traffic spikes; continuous in-flight GPU batching handles thousands of simultaneous users
Enterprise Concurrency Layer
Async API threads with Kubernetes HPA auto-scaling on traffic spikes; continuous in-flight GPU batching handles thousands of simultaneous users
Transformation
Before vs. After
Business users blocked waiting on data engineers
Natural language queries resolved in sub-seconds
LLM hallucinating columns and time ranges
Prompt expander catches vagueness before it reaches the model
Broken JOINs from flat schema context
Neo4j knowledge graph guarantees deterministic JOIN paths
No access control on sensitive data
RBAC physically filters context — unauthorised data invisible to LLM
Every query billed at full LLM cost
Semantic cache returns pre-computed SQL instantly for repeated queries
System collapses under concurrent enterprise load
Kubernetes HPA and GPU continuous batching scales to thousands of users
Results
The Numbers Speak
SQL syntax errors presented to users
Query resolution on semantic cache hits
Cache match threshold for instant SQL return
Concurrent users handled via K8s HPA and GPU batching
Technology
Stack at a Glance
NLP & Orchestration
LangChain, LangGraph, Pydantic v2
LLM Inference
GPT-4o (SQL generation) + NVIDIA NIM — Llama 3.1 / Qwen 2.5 (classification agents)
Embeddings
OpenAI text-embedding-3-small
Vector DB & Cache
Milvus Serverless (HNSW semantic cache + RAG schema store)
Knowledge Graph
Neo4j (deterministic ER map for JOIN path injection)
Security & RBAC
PostgreSQL (role-access matrices) + Milvus metadata prefiltering
Validation Layer
SQLAlchemy + Read-Only DB Replica (self-correcting loop)
Infrastructure
FastAPI + Kubernetes HPA + NVIDIA NIM continuous in-flight batching
Outcome
What AeroSQL Delivers
AeroSQL turned natural language into a secure, production-grade data interface — with zero hallucinated SQL, deterministic JOIN logic, role-enforced access control, and the concurrency headroom to serve an entire enterprise without a single data engineer in the loop.
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