Azimut AI
Illustrative scenario. The company on this page doesn't exist: it's a typical profile for the sector, built to explain how Azimut would be used. It doesn't describe any customer or claim measured results.
The company in this scenario
- 200 people: 80 in engineering, 20 in product and design, 40 in support and customer success, and the rest in sales and support functions.
- Code on GitHub, documentation in a wiki, tickets in a support tool and conversation in Slack.
- Many technical teams already use some AI tool, each its own.
- Customers in Spain and abroad, with support in several languages.
The problem
In a software company the context exists, but in pieces: the why behind a decision is in a Slack thread, the how is in the repository, and what the customer is asking for is in the tickets. Support escalates to engineering questions that are already documented; product prioritizes without seeing all the tickets; a new engineer takes weeks to learn who to ask. And the company pays for several different AI subscriptions, person by person, without knowing what gets used or what it costs.
How it would work with Azimut
Azimut would connect to the repository, the wiki, the support tool and messaging with each system's permissions, and replace the scattered subscriptions with one shared layer where spend is visible. The company would publish four agents.
| Agent | What it does | Who uses it | What gets measured |
|---|---|---|---|
| Level-1 support | Answers repeat tickets with the documentation and already-resolved tickets, and escalates new issues with the context already gathered. | Support | Tickets resolved without escalation. |
| Technical context | Answers "why was it done this way?" and "where is this?" with the repository, the wiki and team threads. | Engineering and support | Questions that stop interrupting an engineer. |
| Feedback synthesis | Groups tickets and customer conversations into prioritized themes for product. | Product | Days from feedback to decision. |
| Release notes | Writes release notes and customer documentation from the changes in the repository. | Product and engineering | Hours per release. |
Here model choice matters more than in any other sector: technical teams have strong preferences and switch when a better model comes out. In Azimut each person picks per task from OpenAI, Anthropic, Google and Mistral, and the team's agents don't depend on the model they were built with.
Where you'd start
You'd start with level-1 support. It has the clearest metric in the scenario (tickets that never reach engineering), and to work it requires connecting the documentation and the support tool, which are the foundation for the other three agents.
What's out of scope
Azimut isn't a coding assistant inside the editor: it doesn't compete with the tools engineers use to write code; it gathers the company's context around the code. It doesn't deploy or modify the repository. Specific integrations are confirmed during rollout and only the ones tested end to end get activated.
How it would be measured
Before turning on any agent, you set the baseline, with the company's own numbers rather than industry averages.
Week 0. Share of tickets support escalates to engineering, interruptions to engineering per week, onboarding days for the latest engineer, and an inventory of the AI subscriptions the company pays for today: how many, from which provider and at what cost.
Month 3. The same numbers, next to Azimut's usage panel by user, team, model and tool, against the week-0 spend on scattered subscriptions.
Frequently asked questions
Does it replace our coding assistant?
No. That one lives in the editor and writes code; Azimut gathers the whole company's context around the code. They can coexist.
Can each team keep using the model it prefers?
Yes. Each person picks the model per task, and if a better one comes out tomorrow it's a configuration change, without rebuilding the agents.
What about private code?
Agents inherit GitHub permissions: whoever can't see a repository gets no answers from it. Model providers don't train on the data sent to them.