I built GripeRadar to stop guessing which SaaS ideas are worth researching
I kept finding lists of SaaS ideas, but they rarely explained why an idea might be worth pursuing.
To investigate one properly, I had to jump between customer discussions, search trends, open-source projects, recent launches, creator coverage, and revenue reports. Comparing all those signals manually became the problem I wanted to solve.
So I built GripeRadar.
GripeRadar collects public market signals, groups related evidence, and turns it into ranked SaaS research opportunities. Each opportunity includes the underlying problem, target user, supporting signals, and separate opportunity and confidence scores.
One important decision was not to treat every signal as an equal vote. A complaint, a search trend, an open-source project, and verified revenue evidence can each mean something different. GripeRadar preserves that context instead of presenting an idea as automatically “validated.”
The product currently includes a public Top 5, deeper opportunity research, source coverage, and daily reports:
I’m especially interested in feedback on two things:
Does the supporting evidence make an opportunity easier to evaluate?
What information would you need before deciding whether to investigate an idea further?
I’m still improving the filtering, scoring, and handling of weak or misleading signals, so critical feedback is welcome.
The scoring risk I would watch is correlated evidence. One launch can produce a Reddit discussion, search spike, GitHub project, creator video, and revenue mention, which look like five signals but share one cause. An evidence graph with source lineage and cluster-level weighting would prevent accidental double counting. I would also surface negative evidence such as failed products, high acquisition cost, regulation, and incumbent response, then backtest historical opportunities to see whether high confidence actually predicted durable demand.
I think keeping opportunity and confidence separate is the right call. That's the difference between "people are talking about this" and "there's actually a business here."
One thing I'd want is a timeline for the signals. Knowing whether they all appeared in the last month or are spread over three years changes how I'd judge the opportunity.
On your second question: I'd weight self-reported evidence higher than inferred behavior. A search trend or forum complaint is still your interpretation of someone's "why" — the strongest signal is when a user states the reason themselves, unprompted. That data almost never becomes public though, since it usually lives inside a private cancellation flow. Built CancelKit around exactly that gap — capturing the stated reason at the actual decision point instead of inferring it after the fact. Might be worth a separate confidence tier for opportunities with zero self-reported evidence vs. some.
Separating opportunity score from confidence score is a strong choice. I’d add a “decision cost” signal: how quickly can a founder test the idea with one real workflow instead of doing another week of research?
With Speechara, conversations describing a concrete workaround — losing meeting context, retyping notes, or switching between audio sources — have been much stronger signals than generic interest in “an AI meeting tool.” The complaint plus the existing workaround tells you what to test first.
That seems like an interesting product concept.
Did you develop Griperadar alone, or did you develop it with a team?
I like the way you're separating evidence from conclusions instead of trying to reduce everything to a single score. That's a much more realistic approach, because different signals carry different weight depending on the market.
One thing I'd probably want before spending time on an idea is some sense of why now. Is the opportunity growing, stable, or already getting crowded? That kind of context would help me decide whether it's worth digging deeper.
Interesting project - I can definitely see myself exploring something like this when looking for ideas.
The distinction between an opportunity score and a confidence score caught my attention.
Especially because you’re explicitly avoiding the claim that aggregated signals automatically equal validation.
The strongest part of this idea is that you're not trying to create another "100 SaaS ideas" list — you're solving the harder problem of deciding which ideas deserve attention.
One conversion question I'd be curious about: when someone lands on GripeRadar, do they immediately understand what happens after they find an opportunity?
Because the visitor's real fear usually isn't "I can't find ideas." It's: "I've spent weeks researching something that looked promising but had no real demand."
The strongest positioning may be less about discovering ideas and more about reducing wasted founder time by showing the evidence behind a decision.
The evidence layer is what makes this interesting — I'd make sure that trust and confidence-building are impossible to miss in the first impression.
I like that you don't flatten every signal into the same score. Keeping a complaint separate from actual revenue evidence makes the research feel a lot more believable.
This also feels like one of those products that could naturally be getting a lot more organic traffic than it probably is today. At this stage that kind of momentum can shape what people end up trusting inside the product.
Made me curious. Have you noticed that too?
How do you currently handle your analytics and would you mind reading my indiehacker pots on how crawlers and bots are ruining analytics data?
https://www.indiehackers.com/post/i-just-discovered-my-analytics-numbers-are-mostly-fake-here-is-why-8197e3ff9d