Launching today
ZooData
The data layer for AI agents
656 followers
The data layer for AI agents
656 followers
ZooData turns any URL into agent-ready JSON, so AI agents can work with structured data instead of raw HTML or bloated markdown. Use ~75% fewer LLM tokens, pay only for the fields you use, and skip extra extraction credits.Beyond extraction, ZooData gives agents pre-analyzed e-commerce intelligence — competitor, market, traffic, and consumer insights — live for Amazon and TikTok. API, CLI, and MCP server included. Start with 1,000 free credits, no card required.
the auto-escalate-to-full-model-extraction when core fields fail validation is a smart fallback. if a site is mid A/B rollout and flips between old/new layout per request, does that thrash the escalation path back and forth before the new template locks in, or is there a cooldown so you're not eating full-model cost on every call during the rollout window?
One thing about dropping empty fields to save tokens: it makes the JSON shape shift from call to call. Our agent code ended up defaulting every field access because a missing `price` could mean the page had none or the extractor whiffed, and there's no way to tell which downstream. A stable schema with explicit nulls, or echoing back the resolved schema per page type, would fix that. Are you leaning stable-schema or minimal-payload long term?
NexaSDK for Mobile
Been testing this for a few weeks with my research agent.
The token savings are real: product pages that used to eat half the context window now come back as compact JSON. My LLM bill noticed before I did. Congrats on the launch! 🎉
ZooData
@power_valsha Weeks of real usage before a comment — that's the feedback that actually means something. Thank you. The context-window savings compounding into real bill savings is exactly the problem we set out to kill. Would love to hear what you're building with it.
Mom Clock
Hey Product Hunt! 👋
I am very happy to hunt ZooData today.
If you’ve spent any time building AI agents, you already know the massive pain point they are solving: raw HTML and heavy markdown burn through LLM tokens like crazy, and maintaining custom scrapers is a constant headache.
Personally, what impressed me is how effortlessly it turns any URL into clean, structured JSON—saving up to 75% on tokens. It’s built specifically for the agent era (with an MCP server right out of the box), meaning you only pay for the data fields you actually use instead of bloated prose.
Huge congrats to Ning and the team on the launch! Please give them your support if you find the product useful, test out their 1,000 free credits, and drop your feedback in the comments below!
ZooData
@justin2025 Thanks for the amazing hunt! 🙌
You nailed it — agents don't need prose, they need fields. Any URL in, clean typed JSON out, plus an MCP server out of the box so Claude/Cursor can pull live Amazon & TikTok Shop data as native tools.
Grab the 1,000 free credits and tell us what breaks — I'll be in the comments all day!
The 'any URL to structured JSON, pay only for the fields you use' angle is what makes this actually fit an agent loop instead of burning tokens filtering markdown. When I specify the fields, is that a per-request schema I define, so the same URL can return different shapes for different agents, or a fixed schema inferred per domain? And on JS-heavy pages that lazy-load content, does extraction run against the fully rendered DOM or the initial HTML?
the field-level trust discussion above is the real meat of this thread. one I didn't see covered: what happens on a listing/search-results page that needs pagination or infinite scroll to surface everything - does a single call return just what's in the initial DOM, or does ZooData drive the scroll/pagination itself to assemble the full result set before handing back JSON?
@Kyle Dong that's the right answer honestly, "we don't disguise a block as a success" is the thing that actually matters, most tools I've tried just eat the failure silently and you don't find out until your data looks wrong three steps downstream. does the response include which failure mode it hit, like captcha vs rate limit vs structure change, or is it just a generic fail code?