Top Builders

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

ElevenLabs

ElevenLabs is a voice technology research company, developing the most compelling AI speech software for publishers and creators. The goal is to instantly convert spoken audio between languages. ElevenLabs was founded in 2022 by best friends: Piotr, an ex-Google machine learning engineer, and Mati, an ex-Palantir deployment strategist. It's backed by Credo Ventures, Concept Ventures and other angel investors, founders, strategic operators and former executives from the industry.

General
Release date2022
AuthorElevenLabs
TypeVoice technology research

Products

Speech Synthesis

Speech Synthesis tool lets you convert any writing to professional audio. Powered by a deep learning model, Speech Synthesis lets you voice anything from a single sentence to a whole book in top quality, at a fraction of the time and resources traditionally involved in recording.

VoiceLab

Design entirely new synthetic voices or clone your own voice. The generative AI model lets you create completely new voices from scratch, while the voice cloning model learns any speech profile from just a minute of audio.

Resources

Useful resources on how to build with ElevenLabs

ElevenLabs - Helpful Resources

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Edit on GitHub

ElevenLabs AI technology page Hackathon projects

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

Foreshock — Continuous ICT Vendor Risk Monitoring

Foreshock — Continuous ICT Vendor Risk Monitoring

Under DORA Article 28, fintechs are legally accountable for continuous oversight of every critical ICT vendor. The leading indicators of a vendor going bad show up in public data weeks before they reach a security score or a questionnaire cycle. Leadership exits. Lawsuits. Hiring freezes. Sentiment collapse. GRC platforms watch paperwork. Security raters watch the attack surface. Neither one watches business health, and that is the gap Foreshock fills. Foreshock runs a daily unattended agent. It pulls signals across five query classes per vendor through Bright Data MCP, and for public companies it pulls SEC EDGAR 8-K filings straight from the source. Every signal gets appended to a Type-2 timestamped history and never overwritten, so the trend is always preserved. A Claude validator throws out the false positives (about 80% of candidates), and a CDC diff scores six weighted dimensions: leadership, legal, headcount, sentiment, news volume, and open roles. When several signals deteriorate at once, a convergence alert fires. AI then writes the risk summary the way a GRC analyst would, and every factual claim carries a citation that resolves to its source signal. A built-in citation audit confirms it, with zero unresolved across all vendors. One click exports a DORA Article 28 ICT Register PDF: cover page, fleet audit, per-vendor sections, the AI narrative with numbered sources, and a methodology appendix. No competitor ships that today. The same engine (watch, detect, score, alert, summarize, source) points at any entity where stale data defeats the purpose. Fintech vendors today. Competitors, suppliers, and acquisition targets next.

Verity - Hiring Intelligence Agent

Verity - Hiring Intelligence Agent

Verity is the first hiring intelligence agent designed to solve the growing problem of "ghost jobs". With up to 33% of online postings lacking genuine intent to hire, job seekers waste countless hours applying in the dark, while B2B sales teams rely on corrupted growth signals. Unlike existing tools that simply classify pasted text, Verity is a fully autonomous agent. Users input a plain-English query (like "software engineer"). Verity then uses the Bright Data SERP API to discover live jobs and runs parallel enrichment agents via the Bright Data Web Unlocker to fetch intelligence from sources like Indeed, LinkedIn, and Glassdoor. Verity evaluates 5 critical signals per job: posting age, repost count, headcount trajectory, recent company news, and employee sentiment. Every job receives a deterministic "Hiring Reality Score" (0–100) and a verdict: Real, Suspicious, or Ghost. Crucially, Verity is engineered to prevent AI hallucinations. Using strict closed-context prompting, the LLM (Groq) only synthesizes explanations from explicitly source-tagged signals retrieved by Bright Data tools. Built in just 5 days, Verity transforms unstructured web data into actionable truth for both job seekers and GTM teams

CrossBorder Revenue Radar

CrossBorder Revenue Radar

##Project Overview The CrossBorder Revenue Radar is an autonomous market-intelligence platform engineered to exploit cross-border spot-price discrepancies by treating physical commodities as tradable arbitrage assets. Powered by Bright Data's high-performance scraping infrastructure, the platform targets the vast, fragmented trade corridors between United States and Kenya. By continuously tracking live web retail data, wholesale liquidation channels and global freight metrics, it strips away manual complexity of international product sourcing, transforming raw web data into immediate, actionable revenue signals for both agile startups and enterprise exporters. ##Mathematical Framework At the core of the platform is a deterministic valuation and pricing engine that normalizes unstructured web data to calculate risk-adjusted margins. The system handles dual vector market scenarios through symmetric mathematical modelling. For the startup tier executing import strategies (US to KE), the engine identifies high-velocity consumer goods by calculating local profitability using the following equation: Profit_Import = Price_Kenya - (Price_US + freight_cost + tarrifs). Conversely, for enterprise-tier agricultural and artisanal cooperatives looking to optimize their go-to-market execution export channels (KE to US), the engine computes the valuation spread as: Profit = Price_US - (Price_Kenya + freight_cost + tarrifs) ##Interactive Workflow User send a prompt requesting top market opportunities as of that date -> Bright Data pulls web prices -> Qwen & Llama parse data -> Django's hardcoded math evaluates profit margins and exact market state as well as offering recommendations and predictions using RL logic and basic Gradient boosting algorithms -> The data is sent to the user via the react webpage having ElevenLabs voice brief the user on the top arbitrage corridor.

DUAL-BROKER SOTA ENGINE

DUAL-BROKER SOTA ENGINE

Dual-Broker SOTA Engine is an automated trading system capturing real-time arbitrage between TradFi and Web3 prediction markets (Polymarket). The project proves that combining robust web scraping with low-latency LLM intelligence creates a secure, enterprise-grade engine. **Bright Data: Bypassing the Web's Toughest Blocks** Arbitrage demands real-time data from highly protected platforms like Yahoo Finance and Polymarket, where stale data leads to losses. The engine implements a resilient 3-tier extraction fallback powered by Bright Data: - **Bright Data Scraping Browser (CDP):** Renders JS-heavy, dynamic order books and scrapes depth snapshots via Puppeteer. - **Web Unlocker:** Bypasses advanced browser fingerprinting and CAPTCHAs on news feeds to guarantee a 99.9% extraction success rate. - **Residential Proxies:** Rotates IPs across a massive pool, ensuring high-frequency scraping runs continuously without rate-limiting or bans. Standardized via a Bright Data MCP Server, this stack transforms the open web into a structured enterprise data feed. **AI/ML API: High-Concurrency, Cost-Effective Swarm Intelligence** Running financial forecasts in real-time requires a consensus mechanism that is fast and affordable. The engine deploys a 50-persona Bayesian Swarm Consensus powered by the AI/ML API: - **Ultra-Low Latency:** AI/ML API orchestrates up to 50 parallel LLM persona requests simultaneously, converging the decision matrix in under 5 seconds. - **Economic Viability:** Leveraging top-tier models (DeepSeek-V4-Pro) via the gateway keeps token costs at a fraction of a cent. - **Real-Time P&L Safeguards:** The dashboard integrates with AI/ML API's billing API to track consumption and prove positive net profitability. With Apache Flink streaming and a Saga-based transaction sandbox for atomic execution, the engine proves that web data unlocked by Bright Data and reasoned by AI/ML API is ready for enterprise production.

Synapse Corp AI

Synapse Corp AI

Synapse AI is an enterprise-grade multi-agent workflow automation platform designed to simulate how real organizations operate using autonomous AI agents. The platform includes specialized agents such as HR, CTO, CFO, CEO, and Risk Management agents that collaborate intelligently to perform tasks like AI-driven interviews, candidate evaluation, operational analysis, workflow automation, and executive decision-making. Unlike traditional AI assistants or single-agent chatbots, Synapse AI focuses on collaborative intelligence where multiple AI agents communicate, reason, and coordinate together to solve complex organizational workflows in real time. The system supports multimodal interactions including text, documents, reports, and speech inputs, allowing users to simulate real enterprise environments and automate time-consuming operational processes. For example, users can conduct AI-powered HR interviews, upload business reports for executive analysis, or generate strategic recommendations through coordinated AI agent discussions. Technically, the platform is built using Next.js, FastAPI, Gemini AI, Speechmatics, Supabase, Docker, and Vultr cloud infrastructure. The architecture uses scalable distributed services, asynchronous processing, and modular AI orchestration to ensure reliability, low latency, and production-style deployment readiness. Synapse AI demonstrates how autonomous AI systems can function like real organizational teams, helping businesses improve operational efficiency, reduce repetitive manual work, accelerate decision-making, and create scalable intelligent enterprise workflows for the future of AI-driven organizations.

Verdict

Verdict

Every hackathon is a courtroom. Verdict is the agent that's read every case file before you walk in. Verdict is an autonomous Gemini-powered agent system with two modes: SCOUT — Point it at any hackathon URL, or feed it submissions directly. The agent enriches each project with AI-written descriptions and extracted links (GitHub, demo, video), clusters them by theme, and produces a competitive intelligence report: who's building what, what gaps no one is filling, and which dark-horse project is positioned to win. JUDGMENT — Bring your own judging rubric in plain English ("novelty 40%, technical depth 30%, real-world impact 30%") and a batch of submissions from any source: scrape a URL, upload a file (CSV, JSON, Markdown, PDF, TXT), or paste them manually. Verdict scores every submission against your rubric and delivers a ranked leaderboard with per-criterion scoring and judicial verdict lines. Both modes stream the agent's reasoning live via Server-Sent Events to a courtroom-themed interface — you watch the agent think, fetch, deliberate, and rule in real time. The core is a function-calling agent loop on Gemini. The reasoning model (Pro tier) handles planning, clustering, and scoring. The fast model (Flash tier) handles high-volume tool steps: link extraction, description writing, file parsing, and rule parsing. Users can swap either model from the UI. Every ruling gets a permanent shareable URL — pass verdicts around like screenshots. Tech stack: Python · Flask · Gemini (function calling, dual-model) · Server-Sent Events · BeautifulSoup + Playwright · pypdf · pandas · gunicorn + gevent. Designed to deploy on Vultr Ubuntu 24.04. Built solo for the AI Agent Olympics at Milan AI Week 2026.