[ X: 1045, Y: 890, Z: 200 ]
Enterprise AI & RAG Infrastructure

Jarvis:
Enterprise Knowledge Graph RAG Platform

A production-grade AI platform delivering explainable, domain-specific decision support using a knowledge graph-enhanced retrieval-augmented generation approach.

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[ 01 ]

The
Challenge

Standard RAG systems retrieve documents and hope the context is enough — but for domain-specific decision support, that isn't good enough. Jarvis needed responses that were not just accurate but explainable, with the ability to route different types of queries to different retrieval strategies, and a way to safely ingest live web content into conversation-scoped context without breaking grounding or introducing safety risks.

The platform also needed comprehensive observability and safety validation built in, given that Jarvis was meant to support decisions in economically and clinically sensitive workflows where an ungrounded or overconfident answer carries real consequences.

Finally, maintaining long-running, stateful conversational contexts while running asynchronous ingestion pipelines in the background introduced significant architectural complexity. The infrastructure required an environment-agnostic setup capable of handling concurrent real-time data streaming without compromising system stability or data integrity.

What stood in the way

  • Shallow Retrieval

    Standard RAG retrieves and hopes the context is enough

  • Explainability Required

    Every answer traceable back to its source

  • Sensitive Decisions

    Clinical and economic workflows leave no room for error

  • Stateful Complexity

    Long-running context alongside async ingestion

Unified platform
  • Milvus (VectorDB)
  • LangGraph
  • Azure OpenAI
  • Streamlit
  • Docker
  • Python 3.13
[ 02 ]

The
Solution

Toadster architected a multi-agent orchestration workflow using LangGraph to handle intelligent query routing, hybrid retrieval, response generation, and comprehensive safety validation as distinct, coordinated steps rather than a single opaque generation call. A high-performance FastAPI backend featured Server-Sent Events for real-time streaming chat, stateful conversation management, and asynchronous document ingestion pipelines.

We implemented a hybrid retrieval system leveraging Milvus for vector search alongside graph-aware context assembly, significantly improving both the accuracy and explainability of AI responses. A custom Clipper ingestion workflow was engineered to dynamically process and convert browser-clipped HTML pages into highly relevant, conversation-scoped context.

Measurable Impact

The Project
Overview

SYSTEM_DIAGNOSTICS: OPTIMAL
MODULES: ONLINE
97%

Response Grounding Accuracy

<3s

Average Query Response Time

1M+

Documents Indexed in Knowledge Graph

SEQ.01_IMPACT

Delivered Impact

Jarvis now Delivers grounded, explainable answers for domain-specific decision support, with multi-agent routing ensuring each query gets the right retrieval strategy and safety validation layers catching risky or overconfident responses before they reach the user.

  • Reports
  • Answers
  • Insights
AI Engine
  1. 01

    Connect

    Bring your data sources together

  2. 02

    Analyze

    AI finds patterns and answers

  3. 03

    Deliver

    Grounded insights that drive action

CLIENT SIGNAL
“What sets Jarvis apart from a standard chatbot is that every answer can be traced back to why it was generated. That explainability was non-negotiable for us, and Toadster built it into the architecture, not bolted on after.”
— Director of AI Strategy, Jarvis
  • ReliableConsistent data you can depend on
  • AuditableFull traceability and transparency
  • ActionableInsights that drive real outcomes
Architecture

Enterprise
Architecture

Built with modern, scalable technologies designed for production reliability.

Core & Application

Python 3.13FastAPILangGraphDSPyAzure OpenAIMilvus (VectorDB)

Infrastructure & Delivery

DockerUvicornPydanticStreamlitLangfuse

Frequently asked Questions

Common questions about the Knowledge Graph RAG Platform

It's a retrieval-augmented generation system that combines traditional vector search with a knowledge graph, using entity and relationship data to assemble more accurate and explainable context for AI-generated responses.

By routing different types of queries to specialized agents — for retrieval, reasoning, or safety validation — rather than relying on a single generation step, multi-agent orchestration can catch errors and select better retrieval strategies for each specific query type.

Explainability comes from being able to trace a generated answer back to specific retrieved sources and reasoning steps, often supported by graph-aware context assembly and confidence scoring, rather than treating the model as a black box.

Hybrid retrieval combines vector similarity search with structured, graph-aware context assembly, giving the system both semantic matching and relationship-aware context that pure vector search alone would miss.

Through dedicated validation layers in the response pipeline that apply confidence scoring, domain-specific checks, and automated disclaimers, ensuring risky or low-confidence answers are flagged rather than presented as certain.

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