Top AI Product

If you’ve ever stared at a web page full of valuable data and wished it would leap neatly into a spreadsheet, Chat4Data is trying to make that happen—without code, and in roughly three clicks. “We are a small team based in the US,” the founding team told me in our interview. “Most of us are developers aiming to create something that can change the industry of web scraping. While LLMs are powerful, they cannot get the data for us. Learning to code seems to cost people too much time and effort. That’s why we built Chat4data.”

The team pushed further, framing their thesis less as a reaction to legacy tools and more as a recognition of a technological breakpoint. “Chat4Data is not really a reaction to traditional data extraction or web scraping tools and their shortcomings. No. Imagine writing an essay justifying why you were moving on from your candle business at the dawn of electric light,” the team said. “Electric intelligence is here—and it would be naive of us to pretend it doesn’t fundamentally change the kind of product we need to build to meet the moment.”

Product Overview

Chat4Data is a Chrome extension that turns natural-language prompts into structured extractions. The product leans into a minimal workflow—“3 Clicks Is All It Takes”—by pairing presets with AI-driven detection. In practice, that looks like this: install the extension from the Chrome Web Store, sign up for a free account, and start chatting on any target site. The assistant auto-detects and extracts common fields and “the most valuable data,” including images, links, emails, phone numbers, and even hidden page elements that standard copy-paste misses. When results look right, you push them into a spreadsheet—the company’s “prompt it to your spreadsheet” framing—without wading through XPath, CSS selectors, or brittle scripts.

For breadth, the tool crawls across paginated lists so you “leave no page unturned,” assembling complete datasets instead of a single screen’s worth. The team positions this for non-technical operators—analysts, marketers, sales ops, founders—who need web data quickly but don’t want to build scrapers. Pricing today is pay-as-you-go: users top up credits to run tasks, with the company defining “1 credit equals 1M tokens.” There’s no subscription yet, and no chat history feature—“every scraping activity will not be saved,” the team notes, signaling a privacy-conscious posture as they scale.

Deep-Dive Dialogue

Under the hood, Chat4Data’s approach is pragmatic rather than mystical. “We train the LLM models to read the HTML of the web page and parse it to get the data users want,” the founding team explained. That matters because HTML is messy, idiosyncratic, and often inconsistent across sites. The team is tackling that variability incrementally: “However, numerous site structures are out there; we are still training the models to recognize the most popular sites.”

They’re equally blunt about where they think the category is headed. “Traditional web scraping tools, as we know them, will die,” the team said. “Much in the same way that search engines and IDEs are being reimagined. That doesn’t mean we’ll stop searching or coding. It just means the environments we do it in will look very different, in a way that makes traditional web scraping tools feel like candles—however thoughtfully crafted. We at Lumoris Technologies Inc. are getting out of the candle business when it comes to web scraping. You should too.”

In that spirit, the interface choices are intentional. Natural language over GUIs and code: “Complex coding and clunky GUIs are barriers. Chat interfaces already behave like browsers and SaaS tools: they search, read, generate, respond, and interact with APIs, LLMs, and databases,” the team told me, arguing that conversation is becoming the default interaction model. Browsers remain the canvas: “Webpages won’t be replaced—they’ll remain essential. Our tabs aren’t expendable; they’re our core context for web scraping.” That’s why the product lives as a browser extension and not a separate desktop app. New interfaces start familiar: even as they push toward fully open-ended conversation, the team keeps “Quick reply” buttons for now. “These buttons will eventually be removed in future updates so Chat4Data can respond in a more open, natural way. No rigid decision trees, just fluid conversation that feels more human.”

On day-one experience, the path is intentionally thin: install, log in, chat. “Once users install the extension, open it, and it will guide users in extracting the current web page,” they said. On commercialization and privacy, the team is keeping options open while staying conservative with user data. “We plan to launch a subscription plan, but the time has not been decided. We now adopt a pay-as-you-go pricing strategy where users can top up their credits to perform scraping tasks. 1 credit equals 1M tokens. We don’t have a history chat feature, so every scraping activity will not be saved. We are still working on scaling.”

There’s also a meta-story in how the product is being built. “We want to leverage AI not just in our product, but in how we build it,” the team said. By integrating tools like Cursor for AI-assisted coding, Web Studio for rapid website iteration, and Claude Artifact for generating social content, they claim to have shortened iteration cycles and kept the core team lean: “These AI-powered workflows allow us to prototype, iterate, and ship faster than ever before, freeing our team to focus on creativity and strategic thinking rather than repetitive tasks.”

Market Significance

Conversational scraping lands in a familiar tension: traditional scrapers are powerful but high-friction, while LLM-based tools are approachable but can be brittle. Chat4Data is betting that a chat-first interface paired with HTML-aware models is enough to cover common jobs—collecting marketplace listings, pulling contact info, compiling press mentions—without pushing users into code or visual selector tooling. If the presets and auto-detection are truly good out of the box, that’s an immediate productivity win for non-engineers who otherwise depend on either dev time or piecemeal browser extensions.

The bigger provocation is the team’s “public web database” ambition. In their view, users shouldn’t need to know where data lives; they should simply ask, and a copilot retrieves authoritative, up-to-date information from across the public web. It’s a sweeping vision—akin to what AI-assisted search promises—but they’re candid about current gaps: real-time freshness, batch scale, and reliable metadata remain hard problems. Their advantage may hinge on two factors. First, reliability across pagination and varied layouts—what the team calls “recognize the most popular sites”—because scraping success is defined by edge cases: infinite scroll, lazy-loaded elements, and obfuscated markup. Second, keeping the “prompt-to-spreadsheet” loop fast and correct so users spend time analyzing data, not fixing it.

There are challenges, of course. Site owners deploy anti-bot protections and rate limits, and responsible scraping requires honoring robots directives and terms of service—areas where vendors must provide clear guidance and guardrails. The team’s “no history saved” stance is a plus for privacy-sensitive users, but it also removes a killer feature: reusable playbooks and versioned extractors. Over time, offering optional, user-controlled histories—encrypted and exportable—could unlock compounding value without compromising trust.

Roadmap Ahead

The near-term product roadmap is focused on depth, not just breadth. Today’s sweet spot is list pages; the next release aims to penetrate detail pages reliably. “We will launch a new version in the next quarter, focusing on scraping detailed pages instead of just listing pages on the website,” the team said. “This would help more users perform their daily tasks.” Longer term, they see Chat4Data as the copilot interface for web data work inside the browser—contextual, conversational, and capable of scaling up from a single page to complete site coverage.

They’re also realistic about execution risk. “Chat4Data is still in its early stages and we might fail. Or we might partially succeed but not win. We still assume we don’t know,” the team told me. But the conviction is clear: “Five years from now, the most-used web scraping tools will look nothing like today’s. The next-gen web scraper is being built right now—whether it’s Chat4Data or not.”

Closing

Chat4Data’s promise is refreshingly clear: less wrestling with selectors, more getting the data you need by describing it in natural language. The team confirmed in our interview that they’re keeping the UX intentionally light, the pricing flexible, and the near-term roadmap aimed at the everyday tasks most users hit first—while staking out a bigger claim that conversational copilots will eclipse traditional scrapers. If they can nail reliability on detail pages and keep exports clean, that claim may feel less like rhetoric and more like a roadmap.