55%
of rounds are reported by exactly one publication
The single number that decides whether a build succeeds. Watching a handful of well-known outlets is achievable in a day and misses most of the market permanently.
Build vs. buy
The first version takes a weekend. The maintenance never stops.
Parsing funding announcements is genuinely not hard. What is hard is that six outlets report one raise with three different amounts, the company name matches four companies, and every source changes its markup on a schedule nobody tells you about.
In short
You can build this. Most teams that try get a working prototype quickly and then discover the real work: deciding that several articles describe one event, attaching it to the right company out of several with similar names, reconciling amounts and stages where sources disagree, and knowing when to publish nothing rather than something wrong. None of that is finished once. Sources change and the pipeline degrades quietly, which is the failure mode that costs the most, because nobody notices until a customer does.
Measured, not asserted.
Recomputed weekly from the live corpus. Every figure below is reproducible from your own API responses.
72%
of rounds are queryable within an hour of publication
The bar a scheduled crawl has to clear. Measured from the article's publish timestamp, recomputed weekly.
60+
sources monitored continuously
Each with its own format, quirks and failure modes, all of which change without notice.
~820
new rounds a month to keep correct
Deduplicated, resolved to a company, and reconciled where outlets disagree.
As of September 7, 2026
Side by side.
Datahyena
Building it yourself
Time to first clean signal
One API call
Weeks to a working version
Coverage of the long tail
60% of rounds come from a single outlet
Scales with sources you maintain
Deduplication
One raise is one event
You design and maintain it
Entity resolution
Companies, investors and people resolved
The hardest part, and never finished
Knowing when to publish nothing
Confidence floor; low-confidence events are withheld
You build the judgement
Source maintenance
Ours
Yours, forever
Latency
Median 36 minutes from publication
Your schedule plus your uptime
Cost shape
Usage-based, scales down as well as up
Engineering salary, permanently
Which one fits you.
Choose Datahyena when
- You want the problem solved rather than owned. One call returns a resolved event with the company and investors attached.
- Your engineers have something more valuable to do than repair parsers.
- You need coverage beyond the obvious outlets, which is where most of the market actually is.
- You want someone else on the hook when a source changes shape on a Sunday.
When building it yourself makes sense
- Signal collection is your product, not an input to it. Then this is your core IP and you should own it.
- You have proprietary or private sources no API can replicate.
- You have a data team that will still own this in two years, budgeted and staffed.
- Your requirements are narrow enough to stay narrow: one country, one sector, a handful of sources you can watch by hand.
What makes Datahyena different.
The hard part is not fetching
Every build underestimates the same three things: that one raise appears across many outlets and has to collapse into one event, that the company name in an article often matches several real companies, and that outlets disagree about the amount and the stage. Getting an article is a solved problem. Deciding what it means is the product.
Silent degradation is the real cost
Pipelines rarely fail loudly. A source changes markup and one feed quietly returns nothing; a parser starts reading the wrong element and produces plausible garbage. Weeks later someone notices the numbers are wrong. Detecting that continuously is its own system, and it is the part nobody scopes.
Abstention has to be designed in
The instinct when evidence is ambiguous is to pick the most likely answer. For this data that is the wrong instinct: a wrong company on a real round routes work to the wrong account and corrupts everything downstream. Deciding to return nothing is a feature, and it is harder to build than the happy path.
Where Building it yourself wins.
A comparison with no losses is an advert. These are the cases where we would tell you to buy the other thing.
If you only need a few sources, build it
Genuinely. If your scope is one country, one sector, and five publications you can name, a scheduled fetch and a language model will get you most of the way, and paying for a corpus you will not use is waste. The economics turn when you need the long tail or the maintenance stops being free.
You give up control of the schema
Our fields are our fields. If you need a bespoke shape, an unusual event type or your own confidence rules, an API is a constraint where a build is not.
Our funding record starts in 2020
Dense from 2020 forward. If you need a decade of history, that is a gap a build with archive access could close and we currently cannot.
Common questions
Datahyena and Building it yourself, answered.
How long does building this actually take?
A prototype covering a few known outlets: days. Something you would put in front of a customer — deduplicated across sources, resolved to companies, sensible when outlets disagree, and monitored so you find out when it breaks — is a materially larger project, and unlike the prototype it does not end.
Could I just use an LLM on RSS feeds?
For extraction, yes, and it works well. That gets you structured events per article. It does not tell you that this article and five others describe the same raise, or which of four similarly named companies it belongs to, or which figure to trust when two outlets disagree. That is the part that takes the time.
What if we build and keep you as a fallback?
That is a reasonable architecture and some teams run it. Use your own pipeline for the sources you care most about, and the API for coverage breadth and corroboration. The single-source figure is the argument for the second half.
How do we compare our build against you?
Take a week you already know about, run both, and compare on three things: how many rounds each caught, how long after publication, and how many were attached to the wrong company. The third one is usually the surprise.
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