I built free stock analysis for 8,000 stocks. The hard part was not the valuation
Hey everyone! I’m the founder of Intrinsiqq, a free stock analysis tool for US stocks.
The idea sounded simple when I started:
- Type in a ticker and get a DCF estimate, quality score, dividend analysis, and 10+ years of financials.
I wanted it to work so that literally anyone could use it. Initially, I also thought the hard part would be the valuation model.
It was not. (By a big margin)
The hard part was turning SEC filings into data that was clean enough to trust.
A few things I underestimated:
- XBRL tags are messy
Two companies can report the same basic thing under different tags. Some change tags over time. Some filings are clean. Others make you question your life choices.
- A DCF is easy for one company, harder for 8,000
For one ticker, you can open the 10-K and adjust the model manually.
At scale, you need fallbacks, sanity checks, and the discipline to say “this model is not useful here” instead of forcing a fake fair value estimate.
- Bad precision is worse than no estimate
Some companies should not get a DCF.
If free cash flow is negative, inconsistent, or not meaningful for the business model, pretending the output is precise creates false confidence.
That was one of the harder product decisions: sometimes the honest answer is “not applicable.”
- Trust is part of the product
I used to think the output was the product.
Now I think the assumptions are just as important. If someone cannot see how the score or valuation was built, they probably should not trust it.
I'm very proud to say after months of hard work our data quality has now been 100% optimized and it's been out for others to use this month
I’d love blunt feedback from other founders:
Does the landing page explain the value quickly enough?
Would you trust a quality score to show a company's fundamentals as a good indicator?
What would make a finance product like this feel more credible: more methodology, more examples, a founder story, or something else? I feel like I have focused on methodology a lot so far
Not looking for praise. Tear it apart if something feels off.
The product is live here: https://intrinsiqq.com
the decision to output 'not applicable' instead of forcing a fake fair value estimate is a massive green flag for long-term user retention. forcing numbers just to look complete completely kills trust the second an active trader audits the sheet.
regarding the landing page explaining the value quickly: the current hook is fine, but you should explicitly brag about your data cleaning discipline. don't just say 'free stock analysis tool'—make it something like '8,000 US stocks with raw xbrl data normalized by hand, not black-box hallucinations.'
explaining how your sanity checks and fallbacks eliminate the messy noise of sec filings is exactly what makes a tool like intrinsiqq feel credible. showing the unglamorous math is your best growth loop.
really cool. im building in this category too. your link seems broken btw. woulda loved to analyze.
This is a strong finance product angle because you are not just showing stock data. You are trying to make the messy parts of public-company analysis understandable enough that someone can actually trust the output.
The point about “bad precision is worse than no estimate” is probably the most important part of the positioning. Most stock tools try to look confident even when the underlying model is weak. Saying “not applicable” when the data does not support a useful DCF is a trust signal, not a limitation.
For the landing page, I would lead harder with that: clean financial data, visible assumptions, and honest valuation boundaries across 8,000 stocks. That makes the product feel more credible than another free DCF calculator.
One thing I’d pressure-test early is the brand frame. Intrinsiqq is distinctive, but the spelling may add a little friction in a category where credibility, recall, and trust matter a lot. Finance users are already skeptical of free tools, so the name should reduce doubt instead of making people pause.
Beryxa .com would fit this direction better as a serious financial intelligence and decision-analysis brand. It keeps the product on your side: same data quality work, same transparency, same free access, but with a cleaner brand shell if this grows beyond stock screens into portfolio research, alerts, scoring, or broader investor decision tools.
For a finance product, naming is not just cosmetic. It affects whether people trust the analysis before they fully understand the methodology.