Same Platform, 100x the Price – Sell Outcomes Instead of Software

01 /The Concept

Timelaps is a continuous brand tracking service. Unlike the traditional approach of commissioning research studies periodically, it runs on an always-on cadence – delivering fresh data every month.

The target customers: brand managers at companies, branding agencies, and startups.

The methodology follows established principles – surveying the brand's target audience on an ongoing basis. Each brand's annual sample runs to at least 4,000 respondents.

Interestingly, Timelaps doesn't recruit those respondents itself. It accesses them through partnerships with global audience research firms – essentially borrowing panels in exchange, at least partly, for the aggregate data those firms can incorporate into their own reporting. That said, individual respondents still get compensated for their participation.

Clients receive results not as raw data tables but on a clean dashboard where every metric is organized and visualized. The dashboard covers:

Occasion ownership – what situations people associate with a brand. A coffee brand might own "quiet place to work alone" or "great spot for catching up with friends."

Brand attributes – three-level scales rating service quality, product quality, value for money, and similar dimensions.

Advertising recall – what share of people mention the brand unprompted when asked to name brands in the category.

Conversion funnel – how awareness translates to consideration and purchase, broken out by channel (online vs. in-store, for example).

Audience profile – standard demographic breakdown of actual buyers.

An AI module also surfaces cross-cutting insights: how the brand compares to competitors, where it leads, where it lags, and where the clearest improvement opportunities lie.

Timelaps operates on annual contracts priced at $30,000 per year – a number that sounds large in isolation. Context makes it look different: traditional agencies offering comparable research typically charge $150,000 or more per year. Against that baseline, Timelaps looks quite reasonable.

Timelaps announced its public launch this week, first spotted via a Product Hunt announcement.

02 /Why It Matters

A similar product was covered in a prior review of Cafeteria ([related review](/review/kto-pervym-vstal-togo-i-tapki)) – which has raised $6 million total, including $3 million last summer. Cafeteria takes an even narrower positioning, focusing exclusively on brands targeting Gen Z, and that specificity runs through every part of its messaging.

Pricing at Cafeteria is similarly substantial: $48,000 per year for the base plan, $60,000 for unlimited AI-powered insight queries, and starting at $8,000 per month for new concept testing.

Neither Timelaps nor Cafeteria employs a team of human researchers to run surveys. The work is done by AI agents – reaching respondents by email, messaging apps, or phone. Other AI agents collect the responses, structure them, analyze them, and present them on polished dashboards.

That makes both companies textbook examples of a trend that keeps surfacing: AI-native agencies that deliver familiar services using AI infrastructure. Because the output is a finished result rather than a software tool, the pricing reflects the value of that result – and it's dramatically higher than what a software-only platform would charge.

For comparison, consider Perspective ([related review](/review/kak-poluchit-insajty-pro-svoj-produkt)), which raised $4 million earlier this year selling AI agents for "running large-scale user research." Its plans start at $99 per month – roughly 50 to 100 times less than Timelaps or Cafeteria.

This gap isn't an anomaly. Y Combinator's latest batch request explicitly called for "AI-native agencies" – startups that build AI platforms but use them to deliver services rather than sell access to the tool. The stated expectation: these agencies can charge clients 100x more than a subscription to the same underlying platform. We're now watching it play out in practice.

03 /Opportunities

It's become a recurring theme in these reviews, not by design but because the evidence keeps accumulating: the AI-native agency model is real and it works.

The strategic implication worth drawing from today's examples: don't optimize for volume, optimize for price. Choose services that command high fees rather than chasing scale at low margins.

High prices absorb customer acquisition costs, support investment in quality, and can produce strong absolute margins even without massive customer counts – as long as the value delivered justifies what you're charging.

The entry point that works here isn't "build a platform and sell subscriptions" – it's "identify an expensive service, build the AI infrastructure to deliver it, and charge for the outcome." Which expensive service fits that model in a domain you actually know?