Real-world Problems Solved with Real-time Inference on Groq

What is the Groq platform about?

Groq is a generative AI solutions company and creator of the LPU™ Inference Engine, providing the speed developers need and the scalability industry requires. Founded in 2016 by Jonathan Ross, inventor of the Google Tensor Processing Unit (TPU), Groq pioneered the Language Processing Unit as the first chip purpose-built for AI inference. The company delivers fast AI inference through GroqCloud, its full-stack cloud platform, and GroqRack compute clusters for on-premises deployments. Groq technology powers real-time AI applications across industries, enabling developers to turn ideas into production-ready solutions at unprecedented speed. Learn more at groq.com.

Goal:

Build real-world AI agent systems that can perceive, reason, and act powered by Groq’s ultra-fast inference and optionally integrated with Model Context Protocol (MCP) for seamless tool/data connectivity.

The Challenge

Develop multi-agent systems that genuinely solve real problems (not just demos), showcasing:

At least 2 specialized agents working together
Real-time performance using Groq API
(Optional) MCP integration with external tools or APIs
Multi-modal intelligence – combine at least two modalities (text, voice, vision, etc.)
Meaningful impact – solve a problem you care about

Example Use Case: Intelligent Document Processing

Agents:

Intake Agent (Whisper) – Handles voice and document uploads
Vision Agent (Llama-4-Scout) – Extracts text/tables from documents
Classification Agent (Gemma2) – Categorizes documents
Integration Agent (MCP) – Connects to APIs/databases
Orchestrator Agent (Llama 3.3 70B) – Coordinates workflow

Flow Example:

Voice input: “Process these invoices and update accounting”
Vision agent extracts key data
Validation via MCP-connected vendor DB
MCP updates accounting system
Voice feedback confirms success

Submission Requirements

Submit a GitHub repo with:

Max 3-min video demo + live link or setup guide
Repository must include:

Executive Summary

Project Title
Problem Statement
Solution Overview
Key Results / Benefits

System Architecture

Agent roles and coordination diagram
Model choices (Groq models)
MCP integration points

Technical Implementation

Agent code (documented)
MCP connectors
Orchestration logic + error handling

Live Demo

End-to-end user journey
Multi-modal inputs
Real-time performance showcase
MCP integrations in action

Performance & Impact

Speed benchmarks
User & business impact
Scalability

Setup & Deployment

En