To maximize product growth using AI agents, you have to build and manage all the context for them, so they produce something connected to your product strategy and business.
Continuum Tracker builds and manages that context for every product you run, automatically. It learns your market first, then sharpens it with your own data as you connect it. You get back ranked, evidence-backed value propositions — each carrying its own context, ready to hand straight to your AI agents.
This terminal replays an illustrative example for a digital product. Nova Stays and its features are a hypothetical product, and the real output will differ depending on your own product, data and setup.
One product has many opportunities, and only a few of them have exponential growth potential. Growth hacking is the work of finding those few. You place as many bets as you can afford, because most lose and the one that wins pays for all of them.
Test 1–7 ideas per week.
The understanding is yours to do. You have to know the problem well enough to explain it to an agent, and judge for yourself which opportunity looks the most promising. One person can only work through a few growth opportunities a day, so this system is hard to scale.
Test 7–50 ideas per week.
Because we assemble the ranked, evidence-backed value propositions, each one arrives with its priority already set and its context already attached — ready to hand to an agent. You create many product prototypes at once, then test them with your product team or with synthetic personas.
Test 50+ ideas per week, per product line.
We can learn any product market ourselves, so even a product line you have no data on still gets full context. It is assembled from the market itself rather than from your customers, and handed to an agent exactly like the rest.
So you can test value propositions using prototypes in markets you have never sold into, and run every product line in parallel.
A sales or customer call goes in. Out come only the sentences that carry a pain point, and why each matters in your product market. The same problem said five ways merges into one user need, ranked by traction. That is where the growth opportunities come from.
We keep the synthesis running, so it is never your job. The judgement is yours — which methodology to apply and what the results mean.
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We read years of product development in your product market. Every feature shipped, every product launched. That is how the context gets assembled. Doing it yourself is brutal, even with AI agents.
We keep that context current, so you inherit what already shipped and where real money was spent. Proof of the bets that came before. That depth is what lets an agent understand the customer problem rather than guess at it, and it arrives attached to every one of your ranked, evidence-backed value propositions.
Sign Up for BetaWalk out of a client meeting and just talk to the app. It captures the problems your customer actually described, in the words they used. Nothing gets paraphrased into someone else’s summary, which is why this produces the best context you can give an agent.
Nothing to connect and nothing to configure. We transcribe it, keep the sentences that carry a pain point, and merge them into your ranked list like any other input.
Sign Up for Beta
Ask a model what to test and it will answer. It always answers. What comes back is a well-argued hypothesis about a user who does not exist, and five agents asked the same way will produce five polished variants of that same invention — fast, plausible, and anchored to nothing.
We anchor it to market understanding, sharpened by the data you supply.
English
Čeština.gitignore the moment it is created, and it never touches your product's source. The point is to learn whether the change is worth building before anybody builds it properly.