DB Intel
Ask your database a question in plain English — get a real answer, not a SQL tutorial.
DB Intel lets anyone on the team ask a business question in plain language — "which customers had the highest orders last quarter?" — and get a real, verified answer pulled live from the database, with the underlying query shown for anyone who wants to check it.
Without it, that question sits in a queue for whoever knows SQL, or someone spends an afternoon hand-writing joins across tables they don't fully remember the shape of.
This isn’t a proof of concept.
What you’re about to see is a fully containerized, production/enterprise-grade solution — the same build that deploys to Kubernetes on any of the three major cloud providers: AWS, Azure, or Google Cloud. There is no gap between this demo and what ships to production.
Built in, not bolted on
Agent Governance
Every agent call runs inside a governor with its own time and token budget and a kill-switch — a runaway or misbehaving step is stopped automatically, not left to run up cost or produce a bad answer, and every call leaves an audit-trail row.
PII / PHI Safe
User input is screened for personally identifiable and health information before it ever reaches a model, so sensitive data doesn't leak into a prompt, a log, or a third-party LLM call by accident.
Prompt-Injection Safe
A dedicated classifier checks every input for attempts to hijack the agent's instructions before it's acted on — the kind of "ignore your previous instructions" attack that a plain chatbot has no defense against.
SQL-Injection Safe
Where a solution talks to a database, every generated query is checked against a strict allow-list before it runs — no destructive statement (drop, delete, update, alter) can reach the database, however it's phrased.
Observability — SSE, Phoenix & Live Logging
Every step an agent takes streams live to the screen as it happens (no blank-screen wait for a final answer), and mirrors into a self-hosted Phoenix tracing dashboard — full request timelines, per-agent spans, and real token/cost usage, visible in real time, not reconstructed after the fact from a log file.
QoS — LLM-as-Judge
Before an answer ever reaches the user, a second, independent model call checks it for safety and can block it outright; a separate quality pass then scores completeness, correctness and relevance in the background — real automated review, not a cosmetic "checking quality..." status line.
Self-Improving (RSI via SKILLS.md)
Agents record what they learn from real runs into version-controlled SKILL.md files, which future runs read back — the system gets measurably better at its job over time instead of staying frozen at its original prompt.
Bring Your Own Model (BYOM)
Switch the underlying model with one setting — a fully local, on-prem model, GPT, Gemini, Claude, Amazon Bedrock, or Microsoft Foundry — with no code change and no vendor lock-in. Every option is a genuinely working, tested path, not a stub.
Durable Memory Across Sessions
Give the agent a user ID and it remembers your last few questions and answers — a follow-up like "which of those spent the most?" resolves correctly days later, without re-explaining context every time you come back.
Tenant Info Isolation
That remembered history is scoped strictly per user ID at the database level — one person's session data is never visible to, or blendable with, another's, even on the same solution.
What it’s built with
Python backend, served with Uvicorn
A FastAPI service running on Uvicorn handles every request — the same production Asynchronous Server Gateway Interface (ASGI) stack used across the whole platform, not a notebook or a prototype script.
FastMCP tool server — MCP v2 support
Database access, document tools and integrations are exposed through the Model Context Protocol via a dedicated FastMCP server running on MCP v2, so agents call real, typed tools instead of hand-rolled function stubs.
Large MCP Payload Support
We go beyond the default MCP payload of 5MB. We support MCP payloads of 5MB+.
Next.js frontend
A React/Next.js interface talks to the backend over Server-Sent Events for live streaming — no page reloads, no polling.
Choice of multi-agent framework
The same solution can run on Strands, LangGraph, Google ADK, or Microsoft Agent Framework — switchable per deployment, not hard-wired to one vendor's orchestration engine.
Agent2Agent (A2A) protocol support
Every agent pipeline also exposes a standards-based Agent-to-Agent (A2A) endpoint with a real, discoverable agent card — so another agent system can call it directly, not just this UI, including mid-conversation clarification round-trips.