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Real-Time AI Voice Agent & WhatsApp Sales Agent

enhanced

Dataforest

Real-Time AI Voice Agent & WhatsApp Sales Agent

MNDA

Enterprise/Corporation
Company Type
-, Singapore
Location
Project work
Engagement Model
6 - 10 people
Team Size
9 - 12 Months
Duration
$100K - $150K
Budget

About

Real-time AI sales agent on voice calls and WhatsApp: qualifies leads in conversation, answers from approved product knowledge, hands qualified prospects to the sales team with full context in the CRM, matches top sales reps and cuts outreach costs by 60%.

Challenge, approach, and impact

Real-Time AI Voice Agent for Cold Calling

Developing a real-time voice-to-voice AI agent for cold calling, delivering human-like conversations, handling noisy environments, and integrating with the client’s CRM and ATS.

Lead Qualification & Sales Handoff

The agent qualifies prospects in real time against client-defined criteria ([budget, intent, timeline]), tags lead status and hands qualified prospects to human sales reps, with full context synced to the CRM.

Grounded Answers from Approved Knowledge (RAG + Guardrails)

Responses are generated only from an approved, curated knowledge base (product information, pricing, objection handling), with guardrails that prevent off-script claims and hallucinations.

Conversation History & Analytics

Every call is transcribed, summarized and logged against the lead record in the CRM, giving the sales team full conversation history, outcomes and performance analytics.

Channel-Agnostic Conversation Engine

The dialogue, qualification and RAG layers are decoupled from the voice channel and can run over text channels such as WhatsApp or web chat.

WhatsApp Business API Channel

Deployed the same AI agent on WhatsApp via the WhatsApp Business API, using approved message templates for outreach and follow-ups, and handling incoming messages within the 24-hour customer service window. The voice and WhatsApp channels share one qualification logic, knowledge base and lead record, so prospects can move between a call and a chat without losing context.

CRM Integration

Integrated the agent with HubSpot and the client’s custom ATS: new leads, qualification status, call and chat transcripts, summaries and handoff events are synced automatically, so sales reps see the full conversation history before taking over.

How we built

Conversational AI
AI & ML Solutions
Voice Assistants
API
Architecture
VoIP
Integrations
Databases
Data Analytics and Visualization

Testimonials

Anonymous

Verified Testimonial

“Working on the AI Voice Agent project was a strong example of how applied Generative AI can deliver measurable business impact when deeply integrated into real workflows. From the very first intro call, it was clear that the client wasn’t looking for an experimental AI demo. Their goal was to replace and scale a critical revenue-driving process — cold calling — without losing sales quality. That set a high bar for latency, speech realism, data accuracy, and CRM/ATS integration. One of the most technically demanding aspects was achieving true real-time voice-to-voice interaction. We had to optimize the entire pipeline — from voice activity detection and speech recognition to LLM reasoning and speech synthesis — to stay below human reaction time. Reaching <450 ms latency consistently required architectural decisions at every layer, not just model selection. Another key challenge was sales intelligence. This was not a generic chatbot. We trained the agent on hundreds of hours of real sales calls, objection-handling patterns, and product-specific nuances using a RAG-based architecture. As a result, the AI behaves like a trained sales rep, not a scripted bot. What I personally value most in this project is that the results were not theoretical. The AI agent matched and in some cases exceeded human sales performance, achieving a 1:1–1.5 sales quality ratio, while operating at a fraction of the cost and with perfect consistency. This project shows how AI can move from “assistive tooling” to a core operational asset. For the client, it unlocked scalable outreach, reduced dependency on human operators, and created a sustainable competitive advantage. For us as a team, it reinforced the importance of combining strong engineering, applied AI, and deep business understanding into one solution.“

Team structure

Client team

Mochamad Purwanto's avatar

Mochamad Purwanto

CEO

Project stakeholder

The client stakeholders were working closely with the team at Dataforest

Agency team

1 x Business Analytst's avatar

1 x Business Analytst

Production

3 x Machine Learning Engineer's avatar

3 x Machine Learning Engineer

Production

2 x Business Analytics's avatar

2 x Business Analytics

Production

2 x Data Engineer's avatar

2 x Data Engineer

Production

2 x Backend Developer's avatar

2 x Backend Developer

Production

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