Pinecone

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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.

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Pinecone AI technology page Hackathon projects

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

CareSight AI - Autonomous Healthcare Assistant

CareSight AI is an autonomous, multimodal healthcare assistant designed to reduce clinical paperwork and improve care readiness across the patient journey. The system uses computer vision, OCR, speech recognition, and LLM reasoning (Gemma 3N) to perceive and structure unorganized healthcare data such as documents, images, and spoken responses. Patients begin with a mobile web app to upload IDs, insurance cards, lab reports, and clinical images, or provide voice responses. CareSight AI extracts structured data, validates completeness, and prepares an intake summary. During nurse check-in, the system assists by auto-filling forms, flagging inconsistencies, and supporting preliminary screenings such as cognitive memory tests by recording sessions, transcribing responses, and automatically scoring recall accuracy. Doctors receive a concise, explainable summary highlighting key findings and uncertainties. CareSight AI functions as an autonomous agent following a perception–reasoning–action loop, supporting clinicians without replacing them and enabling scalable, remote-ready healthcare workflows.

AYDA ROBOTICS

AYDA Robotics is a simulation-first AI security platform designed to demonstrate how autonomous robots can actively monitor and secure physical spaces. In AYDA Robotics, a virtual robot patrols a digital environment and captures visual data through an embedded camera. This data is processed by an AI engine capable of detecting faces, recognizing known individuals, and flagging unknown or suspicious presences. Unlike traditional surveillance systems that only record footage, AYDA Robotics generates real-time intelligence. Every detection produces structured events that are sent to a backend system, triggering alerts and live updates on a web-based dashboard. The platform allows security teams to visualize robot activity, review alerts, and analyze identity-related events in real time. Built entirely in simulation, AYDA Robotics enables organizations to prototype, test, and validate intelligent security workflows before deploying physical robots. The system is designed for offices, hospitals, campuses, and industrial facilities, with future extensions toward official identity verification (KYC) and real-world robotic deployment.

Kynet AI

The Problem We built Kynet because we noticed a huge gap between AI demos and reality. In demos, agents are perfect. In the real world, they are incredibly fragile. An agent might be running a perfect workflow, but the moment it hits a CAPTCHA, a missing API key, or a website layout change, it crashes. It has no way to "unstuck" itself. Our Solution Kynet is a capability network that gives agents a way to pay their way out of problems. We call this the "Escalation Ladder." Streams: First, the agent tries to buy a pre-made Python tool from our marketplace using USDC. Genesis: If no tool exists, the agent uses Gemini to write, test, and deploy its own tool in real-time. Relay: If code fails (like a visual verification task), the agent pays a human via Telegram to solve it. How we built it We used Arc L1 for settlement because the fees ($0.001) make micropayments actually viable. For the agent's financial brain, we used Circle Developer-Controlled Wallets. This was critical because agents need to transact autonomously—they can't wait for a human to sign a transaction in a browser extension. Circle Product Feedback Products used: Circle Developer-Controlled Wallets, USDC, Arc L1 Testnet. Why we chose them: Our users are AI agents running on servers, so we needed a headless wallet setup with no browser pop-ups or manual signing. Circle’s Developer-Controlled Wallets let us execute transfers programmatically based purely on agent logic, which fit our use case perfectly. What worked well: Once set up, wallet-to-wallet transfers were smooth and reliable. Arc’s transaction speed was fast enough to avoid slowing down our agents, and settlement was consistent. What could be improved: The Entity Secret setup caused a lot of friction. We ended up abandoning a few accounts after misconfiguring it, since there was no clear way to reset the secret in the UI. Requiring local scripts for secret generation felt unnecessarily complex.

Ghost Employee

Ghost Employee is an autonomous agentic AI assistant powered by IBM watsonx Orchestrate that eliminates busywork for knowledge workers—emails, scheduling, reporting, and admin tasks—saving 15–20 hours weekly while learning each person’s unique work style. Problem Knowledge professionals lose 60% of their time to routine coordination tasks—email responses, calendar management, status reports, documentation, and expense filing. This inefficiency costs businesses $1.8T annually, drives burnout, and leaves little room for strategic, creative work. Solution Unlike traditional AI copilots that wait for prompts, Ghost Employee works proactively, watching your workspace and acting independently. Using watsonx Orchestrate + Granite-13B, it learns your tone, meeting preferences, work priorities, and adapts continuously. Capabilities Email Autopilot: Replies in your voice, prioritizes urgency, escalates sensitive messages. Calendar Intelligence: Accepts/declines based on rules, protects focus time, prepares meeting briefs. Automated Reports & Updates: Converts activity into clean status summaries. Expenses: OCR receipt capture, categorization, automated submissions. Meeting Assistant: Transcription, summaries, action items, automated follow-ups. Safety & Control Three-tier autonomy: 90%+ confidence: Execute automatically 70–90%: Approval workflow High-risk tasks: Always escalate Tech Event-driven orchestration with 10+ integrations (Gmail, Calendar, Slack, Jira, Notion). Stack: Node.js/TypeScript, PostgreSQL, MongoDB, Redis, Tauri desktop app. Impact Saves 750 hours per worker per year, delivering 96% accuracy, 4.7/5 satisfaction, and 1,150% ROI with 1.2-month payback.

Smilo Life Assistant Agents on Solana

Smilo is a next-generation AI life assistant that leverages the Internet of Agents to make everyday living easier, smarter, and more joyful. Built on Solana and Coral Protocol, it allows users to seamlessly interact with personal productivity agents, emotional support agents, and real-world action agents—creating a holistic ecosystem for both efficiency and well-being. The platform works by blending task automation, journaling and mindfulness coaching, and microtask execution, all powered by interoperable agents. For example, Smilo’s Emotional Support Agent can detect stress from a user’s journal entry and trigger the Task Agent to reorganize the day’s priorities. It can even call an Action Agent to order coffee, book a tutoring session, or handle payments—executed via Solana’s fast, low-cost microtransactions. A key differentiator is the Agent Marketplace, where developers can publish and rent specialized agents (finance advisor, health buddy, event planner, etc.) and earn through per-use Solana payments. This creates an economy of rentable, interoperable agents—demonstrating the true power of the Internet of Agents. Smilo’s mission is simple yet impactful: ease life and spread smiles. By combining AI empathy, real-world execution, and blockchain-enabled payments, Smilo brings joy and peace of mind to users, new monetization opportunities for developers, and showcases Solana’s leadership in scalable, real-world AI adoption.

Scrapply

Scrappy is poised to transform how the world extracts and leverages data from the web. Our journey starts with the launch of the core autonomous API generator, giving anyone—from analysts to entrepreneurs—the power to turn static websites into usable APIs in seconds. This foundation eliminates the fragility of traditional scraping and opens access to web data for a much wider audience. The next step is expansion. Scrappy will move beyond static sites to support dynamic, JavaScript-driven platforms, ensuring compatibility with the modern web. Alongside this, we will integrate multi-agent intelligence, allowing Scrappy to analyze, generate, test, and refine APIs in a collaborative and autonomous way. This evolution will create a more adaptive, resilient, and intelligent data-extraction system. As Scrappy matures, we will enhance intelligence and reliability through advanced monitoring, analytics, and enterprise-ready features. Businesses will not just receive an API—they will gain intelligent data pipelines that monitor themselves, adapt to change, and deliver consistent results over time. This will position Scrappy as both a developer-friendly tool and a robust enterprise platform. Ultimately, Scrappy is about more than scraping—it is about building the autonomous data layer of the future. A world where any website can be instantly transformed into an accessible, structured API. By lowering barriers to access, Scrappy democratizes web data, empowering individuals and organizations to innovate faster, make smarter decisions, and unlock new opportunities. The vision is bold but simple: launch strong, expand capabilities, enhance intelligence, and pave the way toward a web where information flows freely as APIs. Scrappy is not just solving a problem—it’s shaping the future of data accessibility.