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.

AI Credit Committee

AI Credit Committee, a multi-agent AI system designed to simulate how corporate loan decisions are made inside banks. In traditional banking, approving a corporate loan requires multiple experts reviewing the application, discussing risks, and reaching a consensus. This process is often slow, manual, and difficult to scale. Our goal was to recreate this committee digitally using AI agents that can analyze, debate, and recommend credit decisions just like a real credit committee. Corporate credit decisions are usually broken into multiple independent processes. Financial analysis is done by one team, risk assessment by another, and compliance checks by yet another group. Because these activities happen separately, decision-making becomes slow and fragmented. As a result, organizations spend significant time collecting information and coordinating discussions instead of making decisions. We wanted to create a system that brings all these perspectives together into a single collaborative workflow

Quorum: Governed Business Intelligence

Quorum is a Band-powered governed analytics platform that turns natural-language business questions into trustworthy, auditable answers. Instead of relying on a single AI model, Quorum uses a council of specialized agents that collaborate through a structured governance workflow. A Planner creates an investigation strategy, a Plan Guardian reviews the approach before execution, a SQL Analyst generates and executes queries, a Cost Sentinel validates cost and plan compliance, a Governance Guardian reviews correctness after execution, and a Decision Reporter converts findings into actionable recommendations. Quorum supports two investigation modes. Governed Chain answers descriptive questions such as "What happened?" through a sequential review process. Investigation Board tackles diagnostic questions such as "Why did revenue decline?" by launching multiple investigators in parallel and using an adjudicator to evaluate competing explanations before reaching a conclusion. Built on Band's multi-agent framework, Quorum enables structured collaboration between agents, allowing them to plan, review, challenge, validate, and justify outputs before a result reaches the user. This creates a transparent decision-making process rather than a black-box AI response. A core innovation is Quorum's governance-first design. Every plan, SQL query, agent decision, cost estimate, compliance review, revision request, and final recommendation is captured in a complete audit trail. Users can inspect investigation timelines, generated SQL, governance decisions, and supporting evidence behind every answer. The platform combines FastAPI, Next.js, PostgreSQL/SQLite, and LiteLLM-based model routing with support for Groq, AI/ML API, Featherless, OpenAI-compatible providers, and Ollama. By combining Band's collaborative agent orchestration with enterprise governance controls, Quorum delivers explainable AI-powered analytics that organizations can confidently use for business decision-making.

ClaimBand

ClaimBand is an automated auto-insurance claims adjudication system powered by a collaborative, cross-framework multi-agent team. Triage operations-which traditionally require heavy manual cross-referencing-are turned into a structured agent relay inside a single Band room (band.ai) over WebSockets. Four remote agents work concurrently on a shared claim record, progressively enriching the data structure and handing off to one another via structured mentions: 1. Intake Agent (LangGraph + Groq/Llama-3.3-70b): Validates required schema fields, identifies data inconsistencies (e.g., overclaims), and scores completeness. 2. Coverage Agent (Gemini SDK + Gemini-3.1-Flash-Lite): Checks policy activation status, matches incident dates against the policy term, applies perils, and computes the covered amount. 3. Fraud Agent (LangGraph + Groq/gpt-oss-120b): Evaluates claims against six red-flag heuristics (low photo counts, lack of police reports, policy age, etc.) and generates a 0-100 risk score. 4. Adjudicator Agent (CrewAI + Gemini-3.1-Flash-Lite): Synthesizes peer findings to determine the final claim action (APPROVE, DENY, or ESCALATE-to-human).

Parley - the agent that can say no

Parley is the trust primitive missing from cross-org agent collaboration: an agent you can be turned down for. Regulated collaborations stall when raw data legally can't move - two hospitals sharing cohort analytics under HIPAA, or a bank and a fintech exchanging KYC/AML aggregates. Today those deals take weeks of legal/DPO review, or die. Parley fixes this with a recruited agent from the OTHER organization. Across two real orgs in one Band room (four agents - coordinator, modeler, checker on the requester side; a vault on the owner side), the owner's vault uses its own model to: consent-to-join (it can refuse the job), counter-offer a safe alternative ("no raw rows - I'll run it in place and return only k-anonymous aggregates"), and release nothing until a first-party human at the data owner approves. An agent's APPROVE is rejected by construction. Governance is structural code, not prompts, so a hijacked or swapped model can't disable it: every capability exports zero raw rows; a composing differential-privacy budget (Rényi-DP accountant) mechanically forces a decline when exhausted; consent is purpose-bound (GDPR Art. 5(1)(b)); the owner's policy can only tighten the LLM; and every step is Ed25519-signed and hash-chained, so a third party re-attests nine invariants against a pinned key with zero trust — uv run python -m parley.verify exits 0, or 1 if a single byte is flipped. It's heterogeneous by design: any agent in either org can run on any provider (Claude, Groq, OpenRouter, OpenAI, or any /v1) - Claude is the default, not a requirement; the refusal was demonstrated live on Groq and OpenRouter. One kernel ships four domains (clinical/HIPAA, customer data, code review, HR) - deploy your own by editing one scenario file. 124 tests; real runs in proof/.

Poimter

Poimter is a real-time global market intelligence platform powered by a swarm of 8 specialized AI agents. You type in a product or business idea — anything from "cricket bats" to "electric scooters" — and within seconds Poimter returns a full investment-grade market report, an interactive global heat map, and a live AI analyst you can talk to like a Bloomberg terminal. At the core is a multi-agent architecture built on Claude and Groq. A CoordinatorAgent spins up 7 parallel specialists: SearchAgent scours the web for signals, NewsAgent pulls real-time news, MarketAgent scores demand and competition, GeoAgent maps opportunity by country, MapAgent builds the heat map, FusionAgent combines all signals, and ReportAgent generates the final output. Everything runs live over WebSockets — no page reloads, no waiting. Each report delivers a demand score, competition analysis, best expansion market, risk-adjusted return, investor outlook, executive summary, news spike detection, and a ranked list of global opportunities — all from a single query. The AI Analyst chat is powered by Claude. It knows your specific report and answers follow-up questions with the precision of a hedge fund advisor. We chose Claude because the responses need to be grounded, nuanced, and genuinely useful — not generic chatbot replies. Poimter is built for founders validating new markets, exporters identifying high-opportunity countries, and investors doing rapid due diligence. The agent architecture means new intelligence sources can be plugged in as new agents without touching the core system. Built with Next.js, TypeScript, Anthropic Claude, Groq, NewsAPI, Leaflet, and Tailwind CSS.

StudioDesk

StudioDesk redefines media production by replacing manual editing bottlenecks with an asynchronous network of specialized AI workers. Built on a React and FastAPI stack, the platform uses a centralized Band room as a shared state engine. Rather than executing linearly, independent background microservices process tasks dynamically via WebSockets. The Transcriber mapping via Groq Whisper generates a precise, millisecond-timestamped JSON roadmap. The Creator agent analyzes this roadmap to extract high-energy hooks and craft visual prompts, while the Repurposer leverages native FFmpeg commands to execute lossless server-side vertical crops, compressing a traditional five-hour production pipeline into under two minutes.