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An agent that reads what customers say in public and prioritizes its impact.

The Pain Point Scanner reads public discussion, scores what it finds against stated hypotheses, and publishes a validation report to a private admin area. Nobody reads a raw post.

Runs on Scheduled agents Apify Hypothesis scoring Admin reporting Automated publishing
Live TryLinguals trylinguals.com
Runs per month 4 Pipeline stages 4 Hypotheses tracked 5 Human decisions per run 1
Artifacts Read the actual output Three real reports the agent wrote. Unedited apart from removing names and source links.
21 Mar Weekly validation The week the evidence said no 13 posts, 4 signal, zero tier one families Open the report 11 Mar Weekly validation Highest engagement run to date 17 signal posts, 12 tier one families Open the report 06 Mar Investor report A 30 day roll-up, different format 8 posts, 7 signal, competitor profile Open the report

The challenge

Every business is guessing about what its customers want. The evidence already sits in public, in the forums where people describe their problems in their own words. Reading it consistently is a job nobody has time for.

An agency turns that into a retainer and a slide deck a quarter later. Doing it yourself means hours of scrolling, no structure, and conclusions that follow whatever you already believed.

01 Evidence had to come from unprompted conversation, not a survey that leads the witness.
02 Findings had to be able to contradict us, not just confirm what we hoped.
03 The output had to arrive as a decision, on a schedule, without anyone running it.

The system

01

Ingest

A scheduled agent pulls new posts from the communities where the audience actually gathers. No manual collection, no browser tabs left open.

02

Classify

Every post is scored against named hypotheses: is this the problem we think it is, how urgent is it, is this person reachable.

03

Distil

Signal is separated from volume. The agent writes an executive summary, classifies the gap type, and names one recommended action.

04

Publish

The finished report is posted to a private admin area, timestamped and kept, so any week can be compared against the last.

How it works under the hood

Classification is prompt based rather than a trained model, so hypotheses can be rewritten in an afternoon instead of a retraining cycle. Agents run on a schedule rather than waiting for someone to press a button. Every output is written somewhere a human can open it and argue with it.

The report can say no.

Hypotheses are scored, not confirmed. A week where the evidence does not support the idea produces a report that says so, which is the only reason to trust the weeks that say yes.

What this means for your business

Replace the research retainer

The same pipeline points at whatever communities your buyers use. A weekly file instead of a quarterly deck, at the cost of running a scheduled job.

Score your own assumptions

Name the things you believe about your market, then have every incoming signal scored against them. Being wrong becomes cheap and early.

Any recurring read and report

Support tickets, reviews, competitor moves, inbound applications. If someone currently reads a pile and writes a summary, this is that job.

Point this at your market.

We build and run these systems ourselves, so you are hiring people who operate the thing rather than specify it. Tell us where your buyers talk and what you need to know.

Book a call

Thirty minutes on what runs manually today. We follow up by email and send a proposal with scope and a fixed price before any work starts.

Or hire us through Upwork ↗