Groq

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Groq

Groq, Inc. is a company specializing in artificial intelligence (AI) hardware and software solutions. Founded in 2016 by ex-Google engineers, Groq focuses on delivering AI acceleration technologies to power next-generation applications across industries. With its deterministic approach to computation, Groq redefines performance and efficiency in AI inference tasks, providing scalable and developer-friendly solutions.

Headquartered in Mountain View, California, Groq operates globally, with additional offices in Canada, Europe, and other parts of the United States. Their solutions are tailored to meet the demands of enterprises seeking high-speed AI capabilities with a focus on reliability and cost efficiency.

General Information

Attribute	Details
Author	Jonathan Ross, CEO and Founder of Groq
Company	Groq
Founded	2016
Documentation	Groq Libraries
Github repository	https://github.com/groq
Discord	https://discord.com/invite/groq
technology Type	AI Accelerator Hardware and Software Solutions

Products and Services:

Language Processing Unit (LPU): Groq's LPU is architected from the ground up with a software-first design to meet the unique characteristics and needs of AI. It offers a deterministic execution model, eliminating traditional hardware bottlenecks and providing seamless scalability.
GroqCloud: A developer-centric platform that provides API access to Groq’s high-performance AI models. GroqCloud supports a range of applications, from vision and language processing to real-time inference tasks, delivering low-cost and high-quality results.
AI Models and API Access: Groq offers a range of AI models accessible via APIs, including vision models like Llama 3.2 11B Vision 8k and Llama 3.2 90B Vision 8k. These models are designed to provide high-quality, real-time inference for various applications.

How to Start Building with Groq

To begin leveraging Groq’s high-performance AI solutions, start by exploring their development environment. Developers can tap into GroqCloud for seamless API access to pre-trained models or optimize their own AI workloads using Groq's Language Processing Unit (LPU).

Visit Groq's GitHub repository to download SDKs, tools, and libraries required for your development.

Start building with Groq 👉 https://console.groq.com/login

Edit on GitHub

Groq AI technology page Hackathon projects

Discover innovative solutions crafted with Groq AI technology page, developed by our community members during our engaging hackathons.

PulseIntel — Enterprise Web Intelligence Platform

PulseIntel is a dual-track enterprise web intelligence platform built for modern security and revenue teams. Every company has two critical blind spots. First, competitors are quietly hiring machine learning engineers and fraud analysts — revealing their next product move months before any announcement. Nobody has time to read thousands of job postings and connect the dots manually. Second, phishing pages, fake domains, and credential dumps appear on the open web daily. Security teams find out only after customers start complaining because internal tools cannot monitor what lives outside the firewall. PulseIntel solves both problems with one unified platform. Track 1 — GTM Intelligence: Enter any company name and PulseIntel uses Bright Data MCP Server to scrape job postings across LinkedIn, Greenhouse, Lever, and company career pages in real time. Groq AI running LLaMA 3.3 70B analyzes the hiring patterns and generates a structured competitive strategy brief — what the company is building, which departments are growing, which competitors should be concerned, and an expected timeline. Track 3 — Security and Compliance: Enter any brand name and PulseIntel scans paste sites, social media, and the open web for brand mentions using Bright Data MCP Server. Groq AI scores each finding by risk level from 1 to 10 and categorizes threats as phishing, credential leak, lookalike domain, or impersonation. Each alert includes a recommended action for the security team. The entire data pipeline runs on Bright Data MCP Server which bypasses bot detection, handles JavaScript rendering, and returns clean markdown directly consumable by the AI layer. The dashboard is built on Streamlit with a dark enterprise aesthetic, real-time metrics, and risk-level filtering. PulseIntel was built in 4 days by WeCoders for the Web Data UNLOCKED Hackathon 2026.

Vigil: Autonomous Supply Chain Risk Intelligence

Vigil addresses a critical vulnerability in enterprise security: third-party supply chain risk. Vendor breaches (such as Snowflake and Okta) exhibit early indicators like credential leaks, exposed API keys, and sudden security personnel hiring spikes days before official public confirmation, yet modern security operation centers lack the tools to monitor these external vectors. Vigil solves this by deploying a collaborative swarm of six specialized autonomous agents: 1. Sentinel Orchestrator: Coordinates execution lifecycles and streams telemetry via Server-Sent Events (SSE). 2. Scout Agent: Discovers candidate breach indicators by querying search engine indexes and code repositories. 3. Extractor Agent: Pulls raw text segments from static dumps and pasteboards using Web Unlocker residential proxies. 4. Browser Agent: Connects to remote Scraping Browsers via Playwright CDP to render dynamic JavaScript security advisories. 5. Analyst Agent: Classifies scraped indicators using high-speed parallel LLM validation (Llama 3.3 / Gemini). 6. Compliance Agent: Computes diminishing-return risk scores and maps incidents to DORA Article 28, SOC 2, and ISO 27001 frameworks. Vigil combines asynchronous Python, high-fidelity web scraping, and real-time SSE streaming to detect and mitigate third-party supply chain exposures before they become public disclosures.

ZeravaneAI — Enterprise Web Intelligence Platform

ZeravaneAI is an enterprise-grade AI platform that combines web scraping, RAG (Retrieval-Augmented Generation), and advanced LLM capabilities to provide intelligent analysis of live web data. Key Features: • Real-time web scraping with BeautifulSoup for dynamic data extraction • Gemini 1.5 Flash LLM integration for intelligent response generation • Full RAG pipeline for context-aware, accurate answers • Multi-turn conversation with history tracking • Streamlit Cloud deployment for enterprise reliability • Handles complex queries about live web content Technical Stack: Gemini 1.5 Flash, BeautifulSoup, Streamlit, FastAPI, Python 3.x, ChromaDB Use Cases: - Market intelligence and competitor monitoring - News aggregation and trend analysis - Business research and data extraction - Enterprise web data analysis Live Demo: https://zeravaneai160506.streamlit.app/ MIT Licensed, open-source, ready for integration.

JACOBI: Adversarial Pricing Topology Probe

JACOBI is a modern intelligence engine designed to expose and bypass algorithmic price discrimination on e-commerce, travel, and booking platforms. Online retailers deploy sophisticated pricing systems that adjust costs dynamically based on location, hardware choices, search history, and referrer links. To reveal these hidden markups, JACOBI deploys 24 parallel probe agents across distinct geographic areas, device types, and cookie states. By routing traffic through BrightData's proxy infrastructure and Web Unlocker, the system bypasses bot detection layers and obtains a clear view of global pricing patterns. Once prices are collected, the analysis pipeline operates via a cascade of partner technologies: AI/ML API: Serves as the core reasoning system, orchestrating model selection (such as GPT-4o) to evaluate price spreads and generate optimization strategies. Groq: Provides sub-second inference speeds, enabling real-time conversion of complex pricing data into actionable recommendations. Cognee: Implements a semantic graph memory layer, allowing JACOBI to remember historical pricing profiles and track discrimination patterns over time. TriggerWare.ai: Manages event-driven post-probe workflows, triggering automated alerts, schedules, and saving strategies when price spreads exceed target thresholds. Ethereum Sepolia Ledger: Commits verified audit sessions to a gas-optimized Solidity smart contract (JacobiPricingLedger.sol) on the Sepolia testnet to prevent tampering and ensure trust. By combining multi-fingerprint testing with unified AI analysis, JACOBI ensures that consumers and businesses can locate the lowest achievable price for any online listing.

SalesPilot AI —The Autonomous B2B Sales Researcher

SalesPilot AI: Autonomous B2B Sales Research The Problem B2B Account Executives and SDRs spend up to 30 percent of their day manually researching prospects across LinkedIn, company websites, and news just to see if an account is worth pursuing. This manual process is slow, error-prone, and doesn't scale. The Solution SalesPilot AI automates the pre-sales research workflow. Built with a sophisticated multi-agent architecture, it operates like a team of researchers. When a user inputs a target company, AI agents dynamically scrape the web, extract data, and synthesize unstructured information into a cohesive intelligence report. Key Features: Deep Company Profiling: Extracts leadership details, business models, and industry positioning automatically. Buying Intent Scoring: Analyzes hiring velocity, recent funding rounds, and pain points to calculate a Buying Intent Score, helping reps prioritize accounts actively looking for solutions. Tech Stack & Competitor Intel: Identifies the software a company uses and pinpoints competitor displacement opportunities. Company Comparisons: Run head-to-head comparisons between two companies to identify the better prospect based on specific dimensional breakdowns. Automated Outreach Generation: Drafts hyper-personalized outreach emails tailored specifically to the company's pain points and recent news. CRM Integration: Syncs the enriched data directly to HubSpot with a single click. How it was built The backend is a high-performance Python application powered by FastAPI, LangGraph, and a massive LLM waterfall system routing between NVIDIA NIM, Google Gemini, Groq, Cerebras, and OpenRouter. The intelligence agents utilize Bright Data's Web Unlocker and SERP APIs to bypass anti-bot protections and gather real-time data. The frontend is a beautifully designed Next.js application, featuring real-time Server-Sent Events that stream the agents' live progress to the user so they can watch the AI research in real time.

ComputeScout

ComputeScout is infrastructure intelligence for AI and ML teams. Instead of jumping between RunPod, Vast.ai, Nebius, Lambda, and dozens of other GPU providers, you describe your workload once — fine-tuning, training, inference — along with your budget, deadline, and region, and get a ranked recommendation backed by live market data. The system uses Bright Data to scrape real-time GPU pricing across the global compute market, runs a deterministic scoring engine on price, availability, reliability, and region fit, and uses Groq only to parse natural language input and explain results. The AI never chooses the provider — the engine does. The Global Compute Radar gives you a Bloomberg-style view of the entire GPU market: live prices, provider health, and availability in one table. Green means scraped live. Gray means estimated. No guesswork, no chatbot UI — just serious infrastructure software built for teams who need to move fast and spend wisely. Built for the Bright Data AI Agents Hackathon.