Good point. There are a few (Creditsafe, Experian) but they're expensive and focus on basic credit metrics, not comprehensive financial health analysis.
The real value is in the contextual interpretation - understanding what the numbers mean for investment/credit decisions. That requires domain knowledge baked into the analytics, not just clean data feeds.
Plus controlling the full pipeline lets us iterate quickly on scoring algorithms based on user feedback. Hard to do that with third-party normalised data.
If these are already expensive, there must be a lot of value for the data itself? Why not make that your core offering? If you're able to automate the process and provide more data on top, you'll definitely have an edge over them.
Fair point - I started with the Companies House API and traditional parsing too.
The issue isn't data access, it's consistency. UK companies file in wildly different formats - some use full GAAP, others micro-entity accounts, many have incomplete data. Rule-based systems work for ~60% of companies, then break on edge cases.
Financial health also requires context: Is this debt ratio concerning for their industry/size? Are these cash patterns seasonal or declining? How do you weight profitability vs growth stage?
I spent months on traditional approaches first. The AI handles the inconsistency and contextual interpretation that spreadsheet formulas can't.
“Help with” implies you do some of the work yourself. But most of the comments in this thread so far are about glaring copy mistakes. Have you not read any of the text Claude gave you? That doesn‘t inspire any confidence that your products is remotely competent and what you claim.
Wrong, my friend, that is only for small companies, big companies accounts are only available in PDF. Plus, in any case you have the raw data, here you have a full financial analysis.
