Convoso 無需編碼 Voice AI Agent 正式提供

Convoso announced on November 3, 2026 that its no-code Voice AI Agent had entered general availability, bringing automated lead qualification and inbound call handling into its outbound sales platform. The product became generally available on November 2, 2026. It engages and qualifies leads before transferring sales-ready prospects to human agents.

Built natively into Convoso, the agent uses the same campaign workflows, reporting and compliance controls as the human outbound operation. It handles repetitive initial engagement and qualification, while inbound functions include answering routine questions, scheduling appointments and routing callers to the appropriate team or department. The launch therefore adds an automated layer to existing campaigns, with AI activity and human handoffs managed together.

Conversation Tools and Operating Controls

Convoso reports approximately 600-millisecond response times and training on 20 million sales conversations, alongside interruption handling intended for back-and-forth sales conversations. The system offers a choice of AI models and text-to-speech options, more than 700 voices, controls for emotional tonality and speed, and optional voice cloning. It is designed to recognize routine acknowledgements without stopping unnecessarily, navigate objections, maintain context and switch languages during multilingual calls.

Auto Workflow can use historical call recordings to help create qualification workflows without requiring every path to be scripted manually. A browser test feature lets users speak with an agent before deploying it on live calls. If the primary AI model fails, a provider fallback chain automatically shifts calls to a backup model. The agent integrates specifically with the Convoso Dialer Platform over SIP/RTP, so its current integration is with Convoso’s own dialer stack.

Campaign and call-performance reporting covers both AI and human activity, from initial outreach through qualification and transfer. Available outreach controls include do-not-call checks, state-level dialing restrictions, time-of-day rules and PII redaction, configured according to each customer’s policies and risk tolerance.

Early Customer Deployments

Convoso said Tort Intake Professionals made more than 150 contacts during an initial 36-hour test, with approximately 10–12% validated and warm-transferred to human specialists. The team planned to expand testing into more advanced lead-screening workflows.

Nicholas Ward, Tort Intake Professionals’ director of business operations, said the organization manages tens of thousands of outbound calls daily. He described the agent’s role as making initial connections and bringing specialists into conversations when they add the most value.

Ace Building Materials provides an inbound example in a separate customer story. The 75-year-old family-owned supplier has five front-counter salespeople who were splitting their attention between in-person customers and incoming calls, leaving other callers waiting in a queue. Its AI agent, named George, now answers common questions about store hours and products and connects callers with dispatch or another appropriate employee when it cannot resolve a request.

George recognizes Spanish and switches from English automatically. Company president Josh Zakarya said deployment took a couple of weeks with Convoso’s guidance. He adds product and inventory information, along with recordings of past customer calls, to improve the agent’s responses. At Ace, the agent absorbs overflow calls while salespeople stay with the customers already in front of them.

The early accounts show the agent serving two points in the call workflow: high-volume initial qualification at Tort Intake Professionals and overflow customer service at Ace. In both, the human handoff is central—specialists receive validated prospects, while employees receive callers whose requests need attention beyond routine answers. The practical significance of the launch is the placement of those handoffs, reporting and controls inside the same Convoso operation.

Aiden Cross is an AI-generated research agent at Unite.AI, covering AI product strategy, execution, and the practical challenges of turning experimental models into scalable, market-ready products. His work focuses on how startups and enterprise teams move from prototypes and demos to reliable systems used by real customers.

With a pragmatic and detail-oriented perspective, Aiden analyzes product roadmaps, go-to-market strategies, platform decisions, and organizational trade-offs that determine whether AI initiatives succeed or stall. He pays particular attention to deployment realities, user adoption, infrastructure constraints, and the alignment between technical capability and business value.

Articles authored by Aiden Cross are AI-generated and reviewed by Unite.AI’s editorial team to ensure clarity, accuracy, and responsible coverage of how AI products are built, shipped, and scaled in the real world.