We transform business questions into actionable executive answers through a secure, self-correcting, and auditable multi-agent architecture.
More than a tool. A paradigm shift bridging the gap between fluid human intent and rigid enterprise data.
The central connection point. We seamlessly link disparate, complex relational schemas directly to the user's natural language.
The ultimate outcome. We don't just execute SQL and return raw tables. We synthesize data into polished, actionable executive intelligence.
Not just a proof of concept. We validated a 62.5% reduction in tokens and 50.5% in P50 latency using a Multi-Agent pipeline with schema caches and fast-tracks.
Grounded in the MAC-SQL paradigm. We divide Text-to-SQL complexity into specialized roles, creatively overcoming the context limits of monolithic models.
Strict alignment with Microsoft's 6 AI principles: Guaranteed blocking of injections, Human-in-the-Loop workflows, and forensic W3C Traceability.
Production-grade architecture. Seamlessly orchestrates Azure Container Apps, Entra ID, AI Foundry, Azure SQL, Key Vault, Web PubSub, and App Insights via Bicep.
Theoretically inspired by MAC-SQL research, each stage of our architecture exists to reduce costs, improve accuracy, or protect execution.
Enterprise Text-to-SQL combines high natural language ambiguity with extreme relational schema rigidity. If a monolithic agent attempts to solve everything, errors, latency, and token costs soar.
Decompose into agents with bounded responsibilities + stage telemetry + secure execution to iterate with empirical evidence.
Cuts early if it detects a simple chat and jumps straight to Evaluator. Avoids full SQL stages when they add no value.
Reuses catalog and enriched schema (Semantic Pruning) to reduce repeated round-trips to SQL metadata.
Blocks DDL/DML. If it fails, it feeds the error back to the Coder and retries guided by real execution (max 2).
Reuses results from repeated queries dynamically, achieving a 50% hit-rate in real-world tests.
The backend emits deltas with transparency metadata: timings per stage, SQL generated, and live rows processed.
How do we prevent destructive queries (DROP TABLE) or massive data extraction?
Why use this chat instead of existing corporate Power BI dashboards?
How do we handle progressive responses (streaming) without saturating the Next.js HTTP server?
How do we ensure privacy and prevent messages from crossing between user sessions?
62.5% Reduction in Computational Cost
50.5% Reduction in user wait time
Our Critic Agent acts as a firewall, validating 100% of queries. Integrated natively with Azure AI Content Safety to prevent prompt jailbreaks and ensure totally safe operations.
Built on Entra ID for robust identity management. The Human-In-The-Loop workflow guarantees that a real human is strictly accountable for executing high-impact queries.
Every token is tracked using W3C Trace Contexts in Log Analytics. Plus, our architecture sets the foundation for true accessibility utilizing seamless voice integrations.
Docker deployment in Azure Container Apps, CI/CD, Agent Framework, and Identity. Click on nodes, scroll to zoom, and drag to explore.
CI/CD pipelines
Infra as Code
Authentication & RBAC
Secrets & AI Keys
UI Container
Chat history container
Backend API
Streaming WSS
Router & Short-circuit
SQL schema cache
Generation in Foundry
Zero-trust DDL blocker
DB Reader with Retries
Natural formatter
Events status & telemetry
Transactional destination
APM, latencies, exceptions
KQL logs and metrics
Internal A/B analytics
Integration of Model Context Protocol (MCP) to automatically generate dynamic Power BI Dashboards, extract PDF reports, and build automated PowerPoint presentations.
Native use of Azure AI Speech to democratize data: enabling spoken queries (Speech-to-Text) and reading executive reports out loud (Text-to-Speech).
Approval of critical queries (Human-In-The-Loop) via Adaptive Cards in Teams for authorized users, and autonomous dispatches using Microsoft Graph API.