Another good thing about Mercury is that in case you’re stuck/not being treated fairly, you can just email/publicly mention Immad (CEO) and he’ll reply within minutes and will look into this
It really makes sense, and the best part — customers love it. It’s the simple form of pricing, and it’s simple to understand.
In many cases though, you don’t know whether the outcome is correct or not but we just have evals for that.
Our product is a SOTA recall-first web search for complex queries. For example, let’s say your agent needs to find all instances of product launches in the past week.
“Classic” web search would return top results while ours return a full dataset where each row is a unique product (with citations to web pages)
We charge a flat fee per record. So, if we found 100 records, you pay us for 100. Of its 0 then it’s free.
We started doing quarterly RFC at Newscatcher, and it was a big game-changer. We're entirely remote.
I got this idea from Netflix's founder's book "No Rules Rules" (highly recommend it)
Overall, I think the main idea is: context is what matters, and RFC helps you get your (mine, I'm the founder) vision into people's heads a bit more. Therefore, people can be more autonomous and move on faster.
no no, they want to use it on external data, we do not do any internal data.
I'll give a few examples of how they use the tool.
Example 1 -- real estate PE that invests in multi-family residential buildings.
Let's say they operate in Texas and want to get notifications about many different events. For example, they need to know about any new public transport infrastructure that will make specific area more accessible -> prices wil go up.
There are hundreds of valid records each month. However, to derive those records, we usually have to sift through tens of thousands of hyper-local news articles.
Example 2 -- Logistics & Supply Chain at F100
Tracking of all the 3rd party providers, any kind of instability in the main regions, disruptions at air and marine ports, political discussions around the regulation that might affect them, etc. There are like 20-50 events, and all of them are multi-lingual at global scale.
thousands of valid records each week, millions of web pages to derive those from.
We’re optimising for large enterprises and government customers that we serve, not consumers.
Even the most motivated people, such as OSINT or KYC analysts, can only skim through tens, maybe hundreds of web pages. Our tool goes through 10,000+ pages per minute.
An LLM that has to open each web page to process the context isn’t much better than a human.
A perfect web search experience for LLM would be to get just the answer, aka the valid tokens that can be fully loaded into context with citations.
Many enterprises should leverage AI workflows, not AI agents.
Nice to have // must have. Existing AI implementations are failing because it’s hard to rely on results; therefore, they’re used for nice-to-haves.
Most business departments know precisely what real-world events can impact their operations. Therefore, search is unnecessary; businesses would love to get notifications.
The best search is no search at all. We’re building monitors – a solution that transforms your catchALL query into a real-time updating feed.
It's probably the best research agent that uses live search. Are you using Firecrawl, I assume?
We're soon launching a similar tool (CatchALL by NewsCatcher) that does the same thing but on a much larger scale because we already index and pre-process millions of pages daily (news, corporate, government files). We're seeing so much better results compared to parallel.ai for queries like "find all new funding announcements for any kind of public transit in California State, US that took place in the past two weeks"
However, our tool will not perform live searches, so I think we're complementary.
Can someone explain to me what I would need to do in terms of resources (GPU, I assume) if I want to run 20 concurrent processes, assuming I need 1k tokens/second throughput (on each, so 20 x 1k)
Also, is this model better/comparable for information extraction compared to gpt-4.1-nano, and would it be cheaper to host myself 20b?
Will, Jeff, I am a BIG Exa fan. Congrats on finally doing your HN Launch.
I think NewsCatcher (my YC startup) and Exa aren’t direct competitors but we definitely share the same insight — SERP is not the right way to let LLM interact with web. Because it’s literally optimized for humans who can open 10 pages at most.
What we found is that LLMs can sift through 10k+ web pages if you pre-extract all the signals out of it.
But we took a bit of a different angle. Even though we have over 1.5 billion of news stories only in our index we don’t have a solution to sift through as your Websets do (saw your impressive GPU cluster :))
So what we do instead is we do bespoke pipelines for our customers (who are mostly large enterprise/F1000). So we fine-tune LLMs on specific information extraction with very high accuracy.
Our insight: for many enterprises the solution should be either a perfect fit or nothing. And that’s where they’re ok to pay 10-100x for the last mile effort.
P.S. Will, loved your comment on a podcast where you said Exa can be used to find a dating partner.
YC S22
artem [at] newscatcherapi.com