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Ant AI
LANCERProject Description
An emerging AI research startup from the US, focused on transforming competitive analysis with multi-agent LLM systems, approached Oodles to build an MVP for their AI-powered research agent. Their goal was to automate competitor tracking, generate contextual insights, and visualize relationships in real time. The project demanded a fast-track development cycle with a one-month delivery target for investor demos.
Scope Of Work
The client aimed to build a chat-first AI platform capable of ingesting diverse file types, understanding research prompts, retrieving relevant insights, and presenting them through dynamic visualizations. Key areas of work included multi-agent orchestration, data preprocessing, semantic search integration, automated workflows, and graph-based visual reporting.
Our Solution
To meet the client’s timeline and vision, Oodles developed a lightweight, scalable MVP that combined LLM-driven intelligence, retrieval-augmented generation (RAG), and workflow automation.
Key features implemented include:
- Chat-first Interface with prompt-based research inputs and multi-file uploads
- GPT-4 + LangChain integration for structured, context-aware generative output
- Multi-agent Framework to break down complex queries and assign tasks
- RAG System for knowledge grounding using Chroma vector database
- Neo4j Graph Engine for dynamic relationship visualization between companies
- React-based Frontend for real-time chat experience and modular research display
- Node.js Backend for API orchestration.