Autonomous semantic analytics engine with single-pass profiling, AST security sandbox, multi-layer context fabric (-95% token costs), and native support for 8 DBMS. No SQL hallucinations, zero raw data leakage.
NATIVE SUPPORT FOR HETEROGENEOUS ANALYTICAL AND TRANSACTIONAL DATABASES
Why Generic Text-to-SQL (LangChain / LlamaIndex) Fails in Enterprise
Feeding raw DDL schemas directly into LLM prompts without a deterministic semantic layer leads to catastrophic data inaccuracies and security risks.
How different classes of analytical requests are handled by standard LLM libraries vs OctaSQL Enterprise
| Query Class / Business Problem | Generic Text-to-SQL (LangChain / Generic AI) | OctaSQL Enterprise Platform |
|---|---|---|
|
Diagnostic Root-Cause Analysis:
“Why did revenue drop 14% in Q3?” |
Not Supported Outputs only a flat number. Unable to identify hidden root causes or multi-table drivers. |
Proprietary Causal Engine™ Automatically isolates hidden anomalies and attributes total variance into exact factor weights (logistics, price, discounts). |
|
Complex Multi-Table Ad-Hoc Joins:
10+ tables, nested CTEs, groupings |
High Hallucination Risk (40-60%) Erroneously joins tables on similar column names, creating massive cartesian product errors. |
Deterministic Semantic Graph Pre-verified schema topology eliminates invalid joins and ensures 99.8% aggregation accuracy. |
|
Predictive What-If Simulations:
“What happens to margin if discount increases 5%?” |
Not Applicable Generic Text-to-SQL can only query historical data 'as is' without scenario modeling. |
Built-in Simulation Engine Simulates business response to parameter changes in an isolated sandbox calculating price elasticity curves. |
|
Token & LLM Context Efficiency:
LLM inference cost reduction |
50,000 – 120,000+ Tokens / Query Forced to dump hundreds of table DDLs into every prompt. Massive API bills and 'Lost in the Middle' errors. |
Up to 95% Reduction (800–2,000 Tokens) Proprietary Context Fabric extracts only the surgical relevant subgraph of the schema, slashing LLM inference costs. |
|
Security & Database Integrity:
Prompt injections, DROP/UPDATE attempts |
Data Corruption Threat Generating queries directly from prompts is vulnerable to prompt injections and accidental destructive execution. |
Multi-Layer Security Perimeter Hardware-guaranteed Read-Only perimeter intercepts any modifying statements prior to database dispatch. |
|
Data Privacy & Compliance:
Banking secrecy, GDPR, HIPAA, PII |
Schema & Data Leakage Schemas and row samples are transmitted to public cloud LLM APIs without localization guarantees. |
100% On-Premise Air-Gapped Runs completely inside your corporate perimeter. Zero raw data rows ever exported externally. |
Experience OctaSQL in Action
Select a business scenario to see how OctaSQL formulates hypotheses, validates queries in the AST sandbox, and produces a mathematical factor report.
4 Steps of Deterministic Analysis
How OctaSQL separates responsibilities between statistical scanning, local state fabric, and deductive planning.
Autonomous DBMS system catalog scan. Calculates cardinality, null rates, and quantiles in a single batch query without exporting raw data.
Local columnar semantic index + BM25 domain synonym registry. Instant entity resolution in < 2 ms.
LLM proposes deductive hypotheses, while the AST validator strictly enforces Read-Only safety rules prior to query dispatch.
Mathematical decomposition of variances: calculates the exact contribution of each driver (price, logistics, demand) to the net outcome.
Connect OctaSQL directly to Claude Desktop, Cursor IDE, and custom enterprise AI agents via open JSON-RPC 2.0 protocol with zero glue-code.
{
"mcpServers": {
"octasql": {
"command": "docker",
"args": [
"exec", "-i",
"octasql-prod",
"octasql", "mcp"
],
"env": {
"OCTASQL_LICENSE_KEY": "OCTASQL-eyJhbG..."
}
}
}
}Uncompromising Data Perimeter Security
Designed for banking, retail, and healthcare institutions with strict security and isolation requirements.
Fully autonomous offline operation. Native support for local LLMs via vLLM / Ollama (Llama-3, Qwen-2.5, DeepSeek). Raw data never leaves your perimeter.
HMAC-SHA256 offline verification with hardware node fingerprinting (machine-id & MAC). Eliminates unauthorized instance duplication.
Distribution compiled into Level-2 optimized bytecode (.pyc) with all source code (.py) removed. Intellectual property is secure from reverse engineering.
Upcoming Enterprise Modules
Key architectural enhancements in active development for upcoming enterprise releases.
Automated scanning and cryptographic tokenization of sensitive personal data (passports, phones, cards, emails) before LLM ingestion. Zero raw PII in prompts (152-FZ / GDPR / HIPAA).
Enterprise SSO integration (Active Directory, Keycloak, SAML 2.0) with AST-enforced policy injection. Automatically isolates organizational data slices by user role.
Cryptographic Merkle-tree query signing with immutable event logs and streaming JSON telemetry into enterprise SIEM systems (Splunk, ELK, QRadar, KUMA).
Embedding query indexing with Redis/Qdrant cosine similarity matching. Reuses verified AST execution plans for semantically equivalent questions, cutting LLM inference costs by up to 70%.
LLM Token & API Cost Calculator
Calculate your annual savings on LLM API tokens, GPU compute, and DWH scanning by eliminating raw DDL schema flooding.
Transparent Enterprise Plans
All licenses include the full deductive analytics engine, AST sandbox, MCP server, and protected binary distribution.
For testing hypotheses and validating architecture on 1-2 data warehouses.
For production deployments inside closed enterprise security perimeters.
Embedding the deductive engine into your own B2B platforms and applications.
We provide a fully functional binary distribution and test On-Premise key within 15 minutes.