Voice AI Agent
AI Agents That Answer Customer Calls
Building a voice agent is the easy part. The hard part is trusting it on a live line at peak hour, and that's the part this platform is built around.
What goes wrong
Where voice agents fail
A bad call costs more than a bad chat. Your customer can't scroll back or click a button, and they hear every pause.
Silence Before Every Answer
The agent pauses long enough for your customer to start talking over it, and then it loses the thread.
A Transfer That Starts Over
The call reaches a person, who asks again for the policy number your customer already gave.
A Wrong Answer, Said Confidently
Nobody listens to every call, so you hear about the wrong answer when the complaint arrives.
Quiet Drift After Launch
A service the agent depends on changes after go-live. The agent gets worse without raising a single error.
What the platform does
Most platforms stop after deployment
Building is one of six areas on the platform. The other five exist so you can trust the agent once it's talking to your customers.
Tested Before It Takes Calls
You write the scenarios your customers really bring, like the caller who changes their mind halfway or reads an account number out wrong. Each one runs against the agent repeatedly, with success criteria you set. Before any customer hears it, you have a consistency score per scenario and a ranked list of what fails most often.
Watched Like a Real-Time System
A voice agent depends on four or five services at once: the language model, speech recognition, speech synthesis and your own APIs. The platform measures each of them separately, so when a call drags you know which service caused it and at what latency percentile. Empty responses from your own systems are counted too.
A Person Can Take the Call
Watchtower shows every call in progress. A supervisor can listen in, and when a call needs a human, press Take. The transcript goes with it, so whoever picks up carries on from where Voice Agent stopped.
Told When It Goes Wrong
A deployed bot usually fails in silence. Nexa reads real conversations on a schedule and spots patterns no error log records, such as a run of calls ending at the same step. It writes down what it saw and proposes a fix, and nothing changes until someone on your team accepts it.
EU AI Act Checks, Every Agent
Each agent runs twelve automatic checks against the EU AI Act and the GDPR. Every check cites its article and quotes the evidence from the agent's own flow. When procurement asks how the agent complies, you answer from the product rather than from a document someone wrote once.
Not Tied to One Model
Each agent is a flow you can read on a canvas. The model behind each step and the speech services are settings on its nodes, so when a better model arrives the agent moves to it without a rebuild. A provider's price or policy change stays their problem rather than becoming yours.
The platform is compatible with models from Google, Anthropic, OpenAI and Microsoft. It connects to your telephony and to your contact-centre or CCaaS platform, so a call reaches the agent the way it reaches your people today.
Our clients
Companies that work with Visor
How we deploy
A dedicated Visor.ai team
From kickoff to go-live, you work with named people who know your project, whether you come to us directly or through a partner.
Clear scope, no surprises
We agree on what's in and what's out before we start, so everyone knows what success looks like.
Built to be measured
Performance metrics are part of the build, so you see results from your first day in production.
Tested with your team
We test end to end, then your team validates it in real conditions before launch.
Your team, in control
We train your people to run the platform confidently, and we stay by your side for as long as you need us.
An honest word on reliability
The models generate each answer in the moment and can occasionally fail. Visor's work is to contextualise them, apply controls and tune continuously. The solution runs on specialist providers who, like any provider, do not guarantee total availability. Judge the AI Agent by the standard you would apply to an experienced colleague: consistent and improving performance, not infallibility. The go-live is the start of the improvement, not the end of the project.
Questions buyers often ask
- How do I evaluate an AI voice agent before buying it, beyond how natural the voice sounds?
- Test it on the scenarios your customers really bring, like the caller who changes their mind halfway, with success criteria you set. Each scenario runs against the agent repeatedly, so before any customer hears it you have a consistency score per scenario and a ranked list of what fails most often. Then look at latency service by service: a voice agent depends on the language model, speech recognition, speech synthesis and your own APIs, and the platform measures each of them separately, down to the latency percentile.
- Can an AI voice agent transfer a call to a person without losing the conversation, inside the contact-centre platform we already use?
- Yes. Watchtower shows every call in progress, and a supervisor can listen in and press Take when a call needs a human. The transcript goes with the call, so whoever picks up carries on from where Voice AI Agent stopped. The platform connects to your telephony and to your contact-centre or CCaaS platform, so a call reaches the agent the way it reaches your people today.