How the pipeline actually works
No black box. Here is exactly how a plain-English claim becomes a ranked, sourced report — the steps, the scoring, the cost, and how we test that it holds up.
From a claim to a report, in six steps.
State the hypothesis
You write your claim in plain English — e.g. “indie founders struggle to get their first 100 customers.”
Clarify the scope
A short set of AI clarifying questions sharpens who you mean and what counts, so the search does not drift.
Suggest & tier subreddits
Candidates are validated against Reddit itself — dead, private, or sub-500-member communities are dropped, and the rest tiered Bullseye / Decent / Off-topic.
Mine real search queries
Queries are built from the actual title phrases people post in your subs, with frequency counts — so you search the way your audience writes.
Set the pipeline knobs
Choose how many threads to classify and how many run in parallel; a live cost estimate updates as you adjust, so there are no surprises.
Review & launch
Watch live logs, a running cost meter, and a per-thread status grid. Stop and resume any time — runs are resumable.
Every thread is scored into a fixed schema.
A fixed schema is what lets the fields aggregate across hundreds of posts — instead of becoming unique, un-comparable prose per thread.
What a run costs, stage by stage.
Cost scales with how many comments you pull per thread — more comments mean richer quotes and a slightly higher run cost. Classification is the only paid step, and it stays inexpensive; each thread takes only seconds to process.
The foundation that outlasts API changes.
Public data only
Every thread comes from Reddit’s public data — nothing to authenticate, no quota approval to wait on.
Resumable & rate-limit tolerant
A 429 or dropped connection mid-run is caught and retried rather than losing progress. A top-N cap bounds exactly how many threads get classified — the main cost lever.
Not exposed to API pricing
That public-data foundation is why the pipeline isn’t exposed to the pricing changes that shut down other Reddit tools.
We measure the parts that could quietly fail.
Not assertions — reproducible scripts anyone can re-run, with caveats published next to the numbers.
AI-suggested subreddits are real
We probed 100 suggestions across ten domains: 90 were live, public communities and only one was a hallucinated name.
Read the test →Exact-phrase search wins
A quoted-vs-tokenized A/B found roughly three in four multi-word phrases return enough exact-match results to use directly.
Read the test →The caveats stay in view. Small samples, and a search ranker that shifts over time — we publish those next to the numbers rather than burying them.
Go deeper.
Validate your startup idea with evidence, not optimism
Test a SaaS or startup idea against real demand before you build.
GuideHow we score a thread for pain and willingness to pay
The exact schema that turns a messy Reddit thread into a rankable pain and willingness-to-pay score.
GuideDoes AI hallucinate subreddit names? We tested 100
We probed 100 AI-suggested subreddits against Reddit’s own API. 90 were real. Here is the 1% that wasn’t.
GuideQuoted vs tokenized Reddit search: an A/B test
Loose keyword search buries you in off-topic posts. We A/B-tested exact-phrase search — 3 in 4 multi-word queries were better off quoted.