How one founder uses Reddit data to find evidence-based SaaS ideas

A Reddit user built a workflow to mine niche subreddits for verifiable pain points, rejecting AI-generated ideas. Their first test analyzed 3,151 posts in construction forums.

A Reddit user, posting under the handle Findep18, reports analyzing 3,151 posts and comments from construction-focused subreddits to find verifiable micro-SaaS ideas. The effort was a direct response to the founder's frustration with AI-driven research, which they claim invents pain points without evidence.

The week-long project focused on r/Construction, r/ConstructionManagers, and r/estimators. It produced a ranked list of five specific problems, with the top issue being the difficulty construction estimators face in tracing numbers back to source documents like drawings and PDFs. The entire process was designed to generate hypotheses backed by a trail of specific comments, upvote scores, and permalinks.

The critique of AI idea generation

The founder's process began with a rejection of using large language models for market research. They argue that AI tools produce flawed micro-SaaS ideas for three primary reasons. First, they miss or cannot access key source material, such as real-time Reddit discussions. Second, they invent patterns that do not exist in the data. Third, and most critically, they cannot provide a verifiable evidence trail for why a stated pain point is real.

A workflow for evidence-backed hypotheses

To counter these weaknesses, Findep18 established a workflow with strict evidentiary requirements. The goal was to move beyond vague notions of user pain. The workflow requires that every hypothesis be supported by exact comments, scores, permalinks, and a CSV evidence trail. This manual-first approach ensures that every potential idea can be traced back to a specific user statement and its reception by the community, measured through upvotes.

Case study: Mining construction subreddits

The founder tested this workflow on the construction vertical, chosen specifically because it was an unfamiliar industry with established professional workflows. Data was collected from the top posts across three relevant subreddits between June 20th and June 27th, totaling a claimed 3,151 rows of comments and posts.

The analysis yielded five distinct pain hypotheses. The top-ranked problem, supported by 137 comments across 14 threads, was that construction estimators struggle to trace cost calculations back to their sources in drawings, PDFs, and spreadsheets. Another high-confidence signal, with a comment reaching 86 upvotes, was a deep skepticism among construction professionals toward AI tools whose outputs could not be linked back to source documents. The process also surfaced lower-confidence pains related to subcontractor price tracking and the paperwork involved in public bid submissions.

What we'd change

The methodology is a disciplined filter but has clear limitations. The "Confidence" score assigned to each pain point is presented without a definition. It appears to be a subjective label rather than a calculated metric, which undermines the data-driven premise. A transparent rubric, combining factors like comment volume, average score, and thread count, would make the rankings more rigorous.

The analysis is also a point-in-time snapshot. A single week of data cannot account for seasonality, industry events, or recency bias. A more robust system would involve continuous monitoring or sampling over several months to distinguish persistent, chronic pains from fleeting complaints. This would require some level of automation, which the founder does not detail.

Finally, the workflow identifies problems but does not validate demand. A high volume of complaints about paperwork does not automatically translate to a willingness to pay for a software solution. The founder's process is a strong starting point for idea generation. The critical next steps of customer discovery, interviews, and landing page tests are needed to determine if these pains represent a viable market.

Landing

This playbook is not about finding a billion-dollar idea. It is a systematic process for finding a thousand-dollar problem with a built-in audience. By prioritizing verifiable evidence over algorithmic abstraction, the founder created a repeatable method for generating niche SaaS ideas with a higher signal-to-noise ratio. For founders drowning in generic advice, this manual, evidence-first approach offers a clear path to identifying problems worth solving.

The investor read

This playbook exemplifies a non-scalable, founder-led discovery process ideal for bootstrapped or micro-SaaS ventures targeting niche B2B verticals. It prioritizes evidence over total addressable market, seeking overlooked problems where a focused product can win. While not investable at this stage, the method signals founder discipline. An investor would want to see this process scaled and paired with rigorous customer discovery before considering it a source of venture-backable ideas. The construction vertical is a massive, underserved market, but this approach targets the long tail of specific workflow pains, not large platform plays.

Pull quote: “The workflow requires that every hypothesis be supported by exact comments, scores, permalinks, and a CSV evidence trail.”