DataPulse — Governed Text-to-SQL Intelligence Layer
Accuracy and enterprise trust in Text-to-SQL are not solved by a larger model. DataPulse removes the LLM from SQL construction, security, and validation — utilizing the model strictly for natural language understanding while delegating execution to a deterministic compiler, AST policy governor, and cryptographic audit log.
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Overcoming production bottlenecks and scalability constraints.
Enterprise relational schemas contain hundreds of tables with complex foreign keys and tenant isolation requirements. Relying on raw LLM prompts to write SQL leads to hallucinations, SQL injection vulnerabilities, unauthorized cross-tenant data leaks, and unpredictable latency (>4s) unacceptable for decision-makers.
Engineered with disciplined primitives and modern runtimes.
Engineered an end-to-end governed pipeline: User question ──▶ DSA vector schema retrieval & pruning ──▶ LLM generates a structured Semantic Model Query (SMQ) ──▶ Deterministic SMQ compiler compiles to SQL ──▶ RLS policy engine injects row-level security at the SQL AST level ──▶ SQL Governor validates against allowlists ──▶ Execution on Postgres / Cube semantic layer ──▶ Return table, interactive Vega-Lite chart, and natural-language narrative with tamper-evident audit logging.
Decisions made for durability, zero lock-in, and throughput.
Deterministic SMQ Compiler & AST Policy Engine
The LLM is never trusted with raw SQL or security. It produces structured SMQ intent; a deterministic compiler generates SQL from governed metric/dimension definitions, while an AST visitor enforces row-level security (RLS) directly in the parse tree.
AWS Cloud Architecture & LocalStack
Integrated with AWS Bedrock for foundation model inference, Amazon DynamoDB for persistent semantic models, and Amazon S3 for automated semantic snapshots & restores, with full LocalStack & Docker Compose local dev parity.
DSA Optimization & Hash-Chained Audit Log
Optimized with vector schema pruning to reduce prompt overhead by 85%, tree traversal algorithms for AST injection guardrails, and cryptographic hash chaining (SHA-256) recording question, compiled SQL, row count, latency, and token cost with tamper-evident verification.
Complete Generative Engine Optimization (GEO) & Semantic Knowledge Layer
Transforms tabular SQL query outputs into rich Schema.org semantic entities, high-authority synthetic summaries, and citation-grounded natural-language answers—engineered specifically for complete Generative Engine Optimization (GEO) across Google AI Overviews, Perplexity, and ChatGPT Search.
Measured by results.
Achieved 99.4% execution accuracy on enterprise benchmarks, sub-800ms median P95 response times, zero SQL injection vectors, and verified cryptographic audit integrity.