Azimut
The product manager decides with feedback scattered across three places: support tickets, customer calls and surveys. Pulling it together takes days, writing the spec happens at night, and every question about adoption ends up in the data team's queue. Azimut brings all of that into one layer where the decision gets made on what was actually said, not on what someone remembers.
What Product gains
Deciding with everything in front of you
A customer request comes in through support, gets repeated in a sales call and shows up in a survey, and nobody sees the whole picture because each source lives in its own tool. When an agent groups tickets, surveys and conversations into prioritized themes, the decision gets made on the full set. Prioritizing is still the product team's job; what disappears is the week of gathering beforehand.
Documenting without stealing time
The spec has almost always already been said out loud: in the discovery meeting, on the customer call, in the discussion with engineering. The agent starts from the transcripts and notes and returns a first structured draft. The product manager edits and completes it instead of starting from a blank page at the last minute.
Answering itself
"How many customers used the new feature this week?" shouldn't be a ticket for the data team. With the data warehouse connected, the product team asks in plain language and gets the answer instantly. Decisions get made with data behind them because getting the data stops costing anything.
The agents you turn on
| Agent | What it does | What gets measured |
|---|---|---|
| Feedback synthesis | Groups tickets, surveys and conversations into prioritized themes. | Days from feedback arriving to a decision. |
| Spec drafting | Writes the spec from meeting transcripts and notes. | Hours per document. |
| Usage analysis | Answers questions about adoption and funnel in plain language. | Decisions backed by data. |
You start with feedback synthesis. It's the agent that changes the product manager's week the most, because it targets the time that today goes into gathering before you can even decide. It's also the one that connects the sources the specs draw on afterward: by the time the second agent arrives, transcripts and tickets are already on the platform and that connection work doesn't get repeated.
Which systems it works with
Product agents read from the wiki and internal documentation, your support tool, meeting and call recordings, and your data warehouse for usage questions. On the action side, the task manager: what comes out of a synthesis or a spec can turn into a task without copy-pasting. Azimut connects to the systems the team already uses and each person sees only what they could already see in them. The specific integrations are confirmed with your team during rollout, and only the ones tested end to end get activated.
Which model fits
Feedback synthesis and spec drafting call for the top-capability model: there's a lot of heterogeneous material to read, a pattern to find, and a document to write that will hold up under engineering review. Adoption and funnel queries work well with a mid-tier model, faster and cheaper per question. In Azimut, every user picks the model per task from OpenAI, Anthropic, Google and Mistral, and the team's agents don't depend on the model they were built with.
Where to start: the baseline
Before turning on any agent, you measure how the team decides today. In Product it's one number: the days between a customer request arriving and a decision being made on it, whether yes, no, or later. It's calculated on actual recent requests, not an estimate. You also note who's already using some AI tool on their own, with what and at what cost, and whether any ungoverned instruction or shared agent without company context is circulating on the team.
Week 0. You collect the days from request to decision on recent customer requests, the time spent writing each spec, and current AI use on the team: how many people, with what tool, and at what cost.
Month 3. You compare those same numbers against Azimut's usage panel by user, team, model and tool, to see how much the time to decision has changed and how many of those decisions carry data behind them.
Frequently asked questions
Does it replace my product team?
No. Prioritizing, deciding and defending the decision to leadership are still work for people. The agent gathers, groups and writes the first draft. What changes is that the feedback and the reasoning behind each decision stay on the platform, available to whoever comes next.
Do I need an integration project?
No. Azimut connects to the wiki, the support tool, recordings and the data warehouse with integrations from its catalog, which are tested before being activated. What it needs is single sign-on and permissions inherited from the source systems, configured in the first two weeks.
Who builds the agents?
At first, Azimut together with your team: the first agent is configured during rollout and shared with the whole company. From the second month on, power users on the team build their own, for example a synthesis per customer segment, and publish them for the rest.
Measure how long it takes you to decide
Request a demo and we'll come away with your product team's baseline numbers.
Request a demo