Pinecone

Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

Pinecone: Next-Gen Vector Similarity Search

Pinecone is a cutting-edge technology provider specializing in vector similarity search. Founded in 2020, Pinecone offers a scalable and efficient solution for searching through high-dimensional data.

General
Author	Pinecone
Repository	https://github.com/pinecone-io
Type	Vector database for ML apps

Key Features

Swiftly finds similar items in vast datasets, providing precise results for recommendations and searches
Offers near-instant responses, ideal for applications needing quick feedback
Integrates into existing applications with minimal setup
Handles large datasets and ensures consistent performance as data grows

Start building with Pinecone's products

Pinecone offers a suite of products designed to streamline vector similarity search and accelerate innovation in various fields. Dive into Pinecone's offerings and unleash the potential of your data-driven applications. Don't forget to explore the apps created with Pinecone technology showcased during lablab.ai hackathons!

List of Pinecone's products

Pinecone SDK

The Pinecone SDK empowers developers to integrate vector similarity search capabilities into their applications seamlessly. With easy-to-use APIs and robust documentation, developers can leverage the power of Pinecone's technology to enhance search experiences and unlock new insights.

Pinecone Console

The Pinecone Console provides a user-friendly interface for managing and querying vector indexes. With intuitive controls and real-time monitoring features, users can efficiently navigate through vast datasets and optimize search performance.

Pinecone Hub

Pinecone Hub is a centralized repository of pre-trained embeddings and models, offering a treasure trove of resources for accelerating development cycles. From image recognition to natural language processing, Pinecone Hub provides access to a diverse range of embeddings for various use cases.

System Requirements

Pinecone runs on Linux, macOS, and Windows systems, needing a minimum of 4 GB RAM and sufficient storage for datasets. A multicore processor is recommended for optimal performance, with stable internet for cloud access. Modern browsers with JavaScript support are necessary, while GPU acceleration is optional for enhanced performance.

Edit on GitHub

Pinecone AI technology page Hackathon projects

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

PakAI Capital | AI Investment Intelligence for PSX

PakAI Capital is an enterprise-grade autonomous AI Investment Intelligence Platform that replaces traditional chartered financial analysts for Pakistan Stock Exchange (PSX) investors. Built with Google Gemini 2.0 Flash, the system deploys a coordinated team of six specialized agents: Research Analyst Agent — Performs fundamental analysis using RAG over company filings Macro Economist Agent — Evaluates SBP policy, PKR, inflation, and global signals Sentiment Analyst Agent — Processes real-time news from Dawn, Business Recorder, and more Risk Manager Agent — Calculates volatility, position sizing, and risk scores Portfolio Manager Agent — Synthesizes all signals into clear BUY/HOLD/SELL recommendations Governance & Compliance Agent — Audits every recommendation for hallucinations, bias, and missing disclosures Key Highlights: Real-time PSX data ingestion + news scraping ChromaDB / Pinecone RAG pipeline for grounded analysis LangGraph orchestration for reliable multi-agent workflow Beautiful Next.js dashboard with live agent reasoning feed Strong focus on explainability and AI governance — directly aligned with DeepMind’s enterprise AI priorities This submission targets Track 2: AI Agents with Google AI Studio and Track 1: Agent Security & AI Governance.

Meridian

Video is one of the most information-dense formats that exists, but almost none of that information is actually accessible after the recording ends. You can scrub through a timeline, you can search a transcript if one exists, but if the answer to your question was written on a whiteboard, shown on a slide, or explained through a gesture without being spoken aloud, you simply cannot find it. Meridian was built to fix that. When you upload a video, Meridian processes it through three completely independent channels simultaneously. It transcribes everything that was spoken with precise word-level timestamps. It reads every frame for on-screen text, picking up slides, diagrams, formulas, and annotations. And it generates a full natural language description of the visual scene in every frame, capturing what the speaker is doing, pointing at, or drawing. When you ask a question, Meridian searches across all three of those knowledge stores at once and uses Gemini 2.5 Pro as an AI reasoning agent to identify the single moment in the video that best answers what you asked. The video seeks to that timestamp automatically. You also see exactly which source fired the strongest signal for that answer, whether it was the spoken word, the on-screen text, or the visual scene, so you can trust the result. The target audience is anyone who works with recorded video as a knowledge source: engineering teams reviewing architecture discussions, legal and compliance teams indexing regulatory training libraries, researchers cataloguing interview recordings, or enterprise teams making internal video knowledge bases actually searchable. Meridian makes the full content of any video as accessible as a well-indexed document.

AI-Call Center - monitored by Humans

A full stack call center solution that answers inbound / outbound calls for insurance / health care verticals. Liev monitoring by humans for extra trued and safety. 10x productivity of a human call center agent, as they can live monitor autonomouse agents taking voice calls, self-healing agents preserve enterprise knowledge and learnings. Autonomous but under human supervision provides guardrails for anyn hallucinations / dificult conversations / escalations Both Inbound Outbound capabilities, tightly integrated with your CRM and Telephony Providers (Twilio) Lead screening, follow ups, Renewals

RepoGuardian AI

RepoGuardian AI is an autonomous repository intelligence and impact analysis platform designed to help developers understand, analyze, and safely modify large codebases. The system performs deep parsing to understand repository structure, builds dependency graphs using Neo4j, and generates semantic embeddings for intelligent code understanding. When developers introduce code changes, RepoGuardian AI detects downstream dependencies, predicts architectural impact, identifies breaking changes, and provides AI-powered reasoning about potential failures. Using graph-based analysis, semantic retrieval, and LLM-driven contextual reasoning, the platform can trace ripple effects across files, functions, services, and APIs in real time. RepoGuardian AI also includes a self-healing engine capable of generating backward-compatible fixes, validating changes, and automatically creating pull requests with detailed risk analysis. The platform combines FastAPI, React, Neo4j, Pinecone, Groq Llama 3.3, and Google Gemini embeddings to create a modern developer intelligence workflow focused on proactive debugging, repository understanding, and autonomous code maintenance.

Sprint-to-Code Pipeline

Modern software teams waste hours translating tickets into actionable engineering work - manually hunting through codebases, guessing which files need changing, and writing boilerplate scaffold. This tool eliminates that entirely. Given a ticket title and description, the system generates vector embeddings of the input, semantically searches the target GitHub repository's codebase (pre-indexed via chunk-level embeddings), and retrieves only the files and code blocks with the highest contextual relevance. No full-repo dumps. No irrelevant noise. Just the exact context the model needs. That enriched context -ticket + relevant code - is fed to an LLM which outputs three things: a structured subtask breakdown that maps work to logical engineering units, a file modification plan that names exact files, functions, and the nature of each change, and working code scaffolds pre-wired to the existing codebase conventions, imports, and patterns. The output isn't a chat response. It's a draft GitHub Pull Request - automatically created against the target repo with a structured description, the subtask checklist, and scaffold code committed to a feature branch. Engineers receive a PR that's already 40–60% complete and contextually accurate, not a blank branch and a vague ticket. The system is designed for real codebases: it handles chunking strategies for large files, respects token budget constraints when assembling context, and uses reranking to prioritize the most semantically dense matches before injection. The result is faster sprint execution, fewer "where do I even start" moments, and a tighter loop between product requirements and shipped code.