AI Voice Call Agent

The Challenge

For enterprise sales teams managing high lead volumes, the qualification bottleneck is almost always the same: too many leads, not enough bandwidth. SDRs spend the majority of their time on repetitive first-touch calls asking the same discovery questions, navigating objections, and manually logging notes into CRM after each call. The leads that actually deserve senior attention often wait days before anyone reaches them.

The client needed a way to dramatically increase lead engagement velocity without proportionally increasing headcount. They also needed call data that was consistent and structured not free-form notes that varied by rep.

What We Built

We designed and built a full AI voice agent pipeline capable of conducting real phone conversations inbound and outbound with natural pacing, contextual understanding, and graceful handling of interruptions and objections.

The agent operates across three modes:

• Outbound qualification: The agent proactively calls leads from a configured list, introduces the company, and walks through a qualification script. It adapts based on responses probing further when interest is high, wrapping up cleanly when it isn't.
• Inbound handling: When a prospect calls in, the agent answers immediately, gathers context, and either resolves the enquiry or routes to the right human with full call context attached.
• Warm handoff: When a conversation reaches a point that warrants human involvement a hot lead, a complex technical question, a decision-maker who wants to talk now the agent transfers the call in real time and passes a live summary to the receiving agent.

At the end of every call, the system automatically generates a structured summary: lead score, key responses, detected intent, and recommended next action. This is pushed directly to the client's CRM via webhook, with zero manual entry required.

System Architecture

The pipeline was built for low latency the gap between a human speaking and the AI responding needed to feel conversational, not robotic. We achieved this through a tightly integrated STT → LLM → TTS loop with streaming at each stage:

• Speech-to-text transcribes in real time as the caller speaks, rather than waiting for sentence completion.
• The language model processes partial transcripts and begins generating a response before the caller has finished.
• Text-to-speech streams audio output as tokens arrive, cutting perceived latency significantly.

Dialogue management sits on top of this loop handling turn-taking, interruption detection, silence handling, and script branching based on what the caller says.

Key Capabilities

• Real-time STT → LLM → TTS pipeline with streaming for low-latency conversation
• Configurable qualification scripts with dynamic branching and fallback paths
• Objection handling with contextual responses, not canned rebuttals
• Real-time warm transfer to human agents with live context summary
• Automatic post-call CRM logging: lead score, key signals, next action
• Call analytics dashboard: volume, duration, qualification rate, transfer rate
• Support for both inbound routing and outbound dialling campaigns

Tech Stack

Outcome

The client's sales team moved from manually dialling and qualifying leads one at a time to running parallel AI-driven conversations at scale. The volume of leads engaged per week increased by 10x with no additional headcount. Human SDRs now exclusively handle warm, AI-qualified conversations spending their time where it actually matters. CRM data quality improved significantly as a side effect, since every call produced a consistent, structured record.