Azimut
The support team answers the same level-1 questions every day while the tickets that genuinely need a person wait in the queue. When a query comes in a different language, someone goes looking for whoever speaks it. And whatever gets resolved through effort stays in the closed ticket instead of turning into documentation for next time. Azimut puts the knowledge base in front of every answer and makes every resolved ticket teach the team something.
What Customer Support gains
Deflecting level-1 volume
A large share of tickets ask something that's already written in the knowledge base. When an agent answers those frequent queries with the knowledge base behind it, the team is left with the cases that need judgment, context or a decision. The queue drops without quality dropping, and satisfaction gets measured on every answer instead of assumed.
Answering in any language
A customer writing in French or German shouldn't have to wait for the one person who speaks it to be available. The agent drafts the reply in the customer's language and someone on the team reviews and sends it. The first response goes out sooner and in the company's tone, and the reviewer doesn't need to be fluent to know the answer is correct.
Learning from every ticket
Every hard ticket that gets resolved is knowledge that today gets lost the moment it closes. When the agent turns what got resolved into a frequently asked question and adds it to the knowledge base, the next similar query gets answered at level 1. What the person who solved it knew becomes the whole team's, and the company's.
The agents you turn on
| Agent | What it does | What gets measured |
|---|---|---|
| Level-1 deflection | Answers frequent queries from the knowledge base. | Deflection rate and customer satisfaction. |
| Response drafts | Drafts replies in several languages for someone on the team to review and send. | First-response time. |
| Triage and routing | Tags, prioritizes and assigns every ticket, and turns resolved ones into FAQs. | Triage time. |
You start with level-1 deflection. It's the agent whose effect shows up fastest and is easiest to count: tickets that used to reach a person now get resolved with the knowledge base behind them. It also forces you to review and organize that knowledge base, which is the work the other two agents depend on: response drafts and turning tickets into FAQs both draw on the same source.
Which systems it works with
Customer Support agents read from your ticketing tool, your CRM and the knowledge base. That gives them the customer's history, the ticket's status and the documented answer. Azimut connects to the systems the team already uses, with nothing migrated, and each person sees only what they could already see in the source system. The specific integrations are confirmed with your team during rollout, and only the ones tested end to end get activated.
Which model fits
Level-1 deflection and triage work well with a lightweight model: these are high-volume tasks with short answers grounded in existing documentation, where speed and cost per ticket matter most. Replies that go out to the customer, especially in other languages, call for a mid-tier model, which writes with more care and holds the tone. 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 support works today. In Customer Support that's three numbers: level-1 tickets coming in per month, average resolution time, and customer satisfaction. All three are usually already in the ticketing tool, so they're taken as they are, without estimating. You also note who's already using some AI tool on their own, with what and at what cost, and whether any ungoverned shared instruction or template is circulating on the team.
Week 0. You collect level-1 tickets from the last month, average resolution time, customer satisfaction, and current AI use on the team: how many people, with what tool, and at what cost.
Month 3. You compare those same three numbers against Azimut's usage panel by user, team, model and tool, to see how much volume got deflected, how resolution time changed, and what happened to satisfaction.
Frequently asked questions
Does it replace my support team?
No. The agent answers what's already documented and prepares drafts; people on the team review, decide and handle the cases that need someone. What changes is that every resolved ticket stays in the knowledge base and the whole team answers with what the person who solved it knew.
Do I need an integration project?
No. Azimut connects to your ticketing tool, your CRM and the knowledge base 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 of rollout.
Who builds the agents?
At first, Azimut together with your team, on your knowledge base. From the second month on, power users on the team build their own, for example one per product or language, and publish them for the rest.
Measure how much level-1 volume comes in today
Request a demo and we'll come away with your support team's baseline numbers.
Request a demo