Tabstack

You don't need to build an agent to get clean data off the web. You need one API call: define a schema, pass a URL, get back JSON that matches. Tabstack's Structured Extraction is live on Product Hunt! 🚀 https://lnkd.in/duSRB6zS

Use autonomous research to keep tabs on your competitors. Automatically.

The most common AI agent failure: confident wrong answers. The fix isn't a better LLM. It's citations. Tabstack's Autonomous Research with citations is live on Product Hunt! 🚀 https://lnkd.in/g9rcp5EX

Tabstack launched on Product Hunt today 🚀 If you’ve ever tried to build an AI agent that needs to actually read the web, you know the pain: ❌ Broken scrapers every time a site changes. ❌ Messy HTML that confuses your LLM. ❌ Complex infrastructure just to handle a few clicks. That’s why we built Tabstack. Backed by Mozilla, Tabstack is the "Web Execution Layer" for AI. Instead of raw content, you get reliable, structured data in one API call. No maintenance and no infrastructure. Just the data your product needs. What can it do?  • 𝗘𝘅𝘁𝗿𝗮𝗰𝘁: Turn any URL into clean Markdown or JSON via a schema.  • 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵: Multi-source web research with citations in seconds.  • 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲: Managed browser agents that handle clicks, forms, and flows. We’d love your support and feedback today! Check us out on Product Hunt: https://lnkd.in/gwZPh7U6 #ProductHunt #AI #WebAutomation #Mozilla #Developers #Tabstack

We're live on Product Hunt today! We'd love your support with an upvote, sarcastic or witty comment, and 5 ⭐ review. 🚀 https://lnkd.in/g4tEBu2j

🚨 New Feature Alert: 𝗠𝗲𝗲𝘁 𝘁𝗵𝗲 𝗘𝗳𝗳𝗼𝗿𝘁 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿! We’ve rearchitected how Tabstack fetches pages to be faster by default. But we also wanted to give you the steering wheel. With three new levels of fetching effort—𝗠𝗶𝗻, 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱, 𝗮𝗻𝗱 𝗠𝗮𝘅—you can now fine-tune your extraction strategy based on the site you're targeting. 🎯 ✅ Reduce latency to under 1 second for static sites. ✅ Ensure 100% data capture for JS-heavy dashboards. ✅ Build smarter, cost-effective AI agents. See how it works: https://bit.ly/47Qwc1r #ProductUpdate #AIInfrastructure #Scraping #Tabstack

Web automation has always been a battle against entropy. Selectors change, networks flake, and rigid scripts break. When you add LLMs to the mix, the infrastructure complexity explodes. 💥 At Tabstack, we needed a way to reliably automate the web for our /automate endpoint. Nothing off the shelf was robust enough. So we built our own solution. Introducing Pilo: The Open Source Engine for Agentic Web Automation. ⚡️ Pilo isn't just a scraper; it's a decision-making engine. It operates in a continuous loop: 👁️ Observe: Captures the browser's accessibility tree for a stable, semantic view of the page (no messy HTML soup). 🧠 Decide: Uses an LLM to plan a strategy and select the right tools for the job. 👉 Act: Executes clicks, fills forms, and navigates with complex retry logic. We designed Pilo to solve the hardest parts of building AI agents: 🔹 Smart Compression: We pipe accessibility trees through a compression engine, slashing token usage by 60-80% while keeping every interactive element accessible. 🔹 Layered Defense: From timeout escalation to full browser restarts, Pilo handles the flakiness of the web so you don't have to. 🔹 Quality Control: Before finishing a task, Pilo runs a separate validation step to ensure the data matches your success criteria. We are incredibly excited to open source this today. You can run Pilo locally, integrate it into your own infrastructure, or even install our browser extension to watch the agent think and act in real-time. For those who want the power of Pilo without managing the browser infrastructure, it remains the core of the Tabstack platform. But starting today, the engine is yours to explore. Read the full technical breakdown here: https://bit.ly/3OvScIl

Does your AI agent need a single data point, or a verified investigation? 🕵️♂️ Extracting a price from a URL is a solved infrastructure problem. But answering a complex question like "Compare Slack vs. Teams enterprise retention policies" is a state management problem. To get it right, an agent has to: ✅ Spawn a fleet of parallel searches ✅ Navigate dozens of unvetted URLs ✅ Filter out 90% marketing noise ✅ Reconcile conflicting data across domains We call this The Synthesis Gap. Until now, developers had to bridge this gap by stitching together brittle scrapers and search logic. That works for a prototype, but it’s expensive and fragile at scale. Today we are launching Tabstack Research. 🚀 We’ve moved the autonomous reasoning loop into the infrastructure layer. You send a goal, and Tabstack: 1️⃣ Plans the investigation across targeted data silos. 2️⃣ Navigates the web using the right level of automation. 3️⃣ Detects information gaps and pivots recursively to find missing details. 4️⃣ Returns a synthesized answer with inline citations. 📎 The result isn’t a list of links or a black-box summary. It’s a verified, application-ready answer you can inspect, trust, and build on. 🏗️ As part of the Mozilla ecosystem, we built this on a foundation of privacy: no training on your data, and all research is ephemeral by design. Ready to bridge the synthesis gap? Read the full technical breakdown on our blog: https://lnkd.in/gdPRXMSU #AI #LLMs #Engineering #WebScraping #Tabstack #AgenticWorkflows