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AI learns how your market evolved, what users are trying to achieve, and where products historically fail. It creates an initial backlog of likely user problems, then uses customer data to validate, refine, and prioritize it.
Scroll to begin↓Step 1 — learning your market
Step 2 — reading the call
Now applying the market knowledge learned in Step 1 to the customer’s own words — sentence by sentence.
[Owner] So I signed up, got through the setup wizard without much trouble, connected my Google Calendar, and at first it looked really promising. But honestly, the calendar sync kept lagging, and twice I had two clients booked into the exact same slot — and in a physio practice that's a disaster, you can't put two people on one table. I'd move an appointment in Google and the app wouldn't pick it up for ten or fifteen minutes, so the availability everyone saw online was just plain wrong. The bigger day-to-day pain, though — we get a lot of no-shows, and the automatic SMS reminders just weren't going out reliably. I set them up, or I thought I did, but loads of clients later told me they never got a text. Someone forgets, doesn't turn up, and that's an empty hour I can't bill for. If the reminders actually fired, that alone would pay for the whole thing. Getting started was rough as well. I had around four hundred existing clients sitting in a spreadsheet, and there was no way to import them in bulk — I ended up typing them in one by one across two evenings. No CSV upload, no contacts sync, I just couldn't get my own data into the system, and I nearly gave up right there. And the last thing — I wanted to charge a small deposit at booking to cut the no-shows, but I couldn't work out how to connect payments at all. It mentioned Stripe somewhere, but the setup was confusing and I never got it running, so for now everyone still pays in person. If I could just take a card deposit up front, that would genuinely change the business for me.
The most common pain across 200 comparable products — users stall in setup before first value.
An appointment moved in Google Calendar didn’t sync for ~15 minutes, so two clients took the same slot.
SMS reminders never sent reliably, so clients forgot, didn’t show, and left empty appointment slots.
With ~400 clients in a spreadsheet and no bulk import, the owner typed them in one by one.
The owner couldn’t connect Stripe for a booking deposit, so clients still pay in person.
Across 200+ logged problems, the backlog re-prioritizes itself as evidence accumulates — and Continuum Tracker reads each one more deeply than general-purpose models.
Volume and granularity matter: the more distinct, concrete problems surfaced, the more precisely a team can prioritize specific next steps rather than vague themes.
Number of customer pain points identified
Each frontier model read the same 12 months of anonymized customer conversations and logged distinct, non-duplicate user pain points. Counts are cumulative and de-duplicated, then verified by a human reviewer.
Depth captures unspoken meaning: the better a model predicts the unspoken needs behind what users say — using real-world context — the more likely it reflects their real needs.
Average depth-of-understanding score
For every identified problem, models were scored 0–100 on the relevant business context they surfaced — severity, frequency, affected segment, and supporting evidence. Continuum Tracker additionally grounds its reading in market-learning context from 200+ comparable products.
We learn from products across the market and combine that insight with your feedback. The automatically structured backlog merges customer needs with product market intelligence, so you can focus on product strategy—not on data collection.
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