Visor AI

Visor.ai is an agentic AI platform for enterprise customer service. Companies use it to build AI agents that learn from their business, take actions across internal systems and handle customer conversations by voice and chat. The company says its agents can reach up to an 85 percent automation rate, and it has introduced Nexa, which it describes as agentic AI behind every action.

Solutions

The Voice AI Agent handles phone calls with human-like, multi-language responses to complex requests, and Visor.ai says it resolves up to 70 percent of requests on first contact. The homepage demo shows an insurance agent taking a broken car window claim: it asks for the caller's name and licence plate, checks whether a tow truck is needed and helps find a nearby garage, with a summary, transcript, notes and flow view of the call. The Chat AI Agent provides similar automation in messaging channels. The Quality AI Agent reviews interactions to monitor service quality.

Enterprise focus

Agents are fully customisable and designed to handle complex workflows across customer interactions, integrating with the systems a business already uses so they can look up data and complete tasks rather than only answering questions. A customer quote on the site from a support director credits the solution with doubling efficiency in their commercial team.

Pricing

Visor.ai does not publish prices. As an enterprise platform, pricing is quoted after a conversation with sales; use the Get Started option to request details.

Who it suits

Visor.ai suits mid-sized and large organisations with high contact volumes, such as insurance, telecoms, utilities, retail and banking, that want to automate routine calls and chats.

Things to consider

Automation rates quoted by vendors depend heavily on the type of requests, so run a pilot on your own traffic before committing. Always offer customers an easy route to a human. Check compliance requirements for recording calls and handling personal data in your country. Plan time for designing flows and testing integrations, which is where most of the work in a deployment usually lies.

Start with a narrow set of common requests, measure resolution and customer satisfaction, and expand the agents' scope gradually as results are confirmed.