By the Numbers
Messages Handled Monthly
Avg. Response Time
Resolution Without Escalation
Languages Supported
How It Works
We ingest your documents, FAQs, product catalogs, and policies into a vector database. Each chunk is embedded with multilingual models to ensure accurate retrieval across languages.
We design the multi-agent architecture, defining which model handles each query type. Routing logic is configured so questions reach the most capable agent instantly.
The agent is connected to WhatsApp, your website, or other messaging platforms. We configure webhooks, authentication, and media handling for each channel.
We test the agent with real conversation scenarios and refine its responses. Prompt engineering and retrieval thresholds are tuned for optimal accuracy and tone.
After go-live, we monitor conversations and expand the knowledge base regularly. New documents and FAQs are embedded automatically, and the agent improves over time.
Orchestrated system using specialized models like Qwen 72B and Claude for different tasks. Each agent handles its domain expertly, routing queries to the best-suited model.
Retrieval-augmented generation powered by Zilliz/Milvus vector databases. Your business knowledge is embedded and retrieved in real time for accurate, grounded responses.
Persistent chat history stored in Firestore maintains full context across sessions. Agents recall previous interactions to deliver personalized, coherent follow-ups.
Built-in analytical capabilities that generate structured SWOT analyses from conversational data. Helps businesses extract strategic insights directly from customer interactions.
Native WhatsApp Business API integration for direct customer engagement. Supports rich media, quick replies, and interactive buttons for a seamless mobile experience.
Fluent conversations in Spanish, English, and other languages without separate bots. Language detection and response generation happen automatically within a single agent.
Real-time metrics on conversation volume, resolution rates, and user satisfaction. Track agent performance and identify opportunities to expand the knowledge base.
Use Cases
An e-commerce business deploys an AI chat agent on WhatsApp to handle order inquiries, returns, and product questions around the clock. The agent resolves over 80% of tickets without human intervention, freeing up the support team.
A company creates an internal chat agent that answers employee questions about HR policies, IT procedures, and compliance guidelines. RAG ensures responses are always based on the latest approved documents.
A professional services firm uses an AI agent to qualify inbound leads via WhatsApp. The bot asks targeted questions, scores prospects, and routes high-value leads to sales reps with full context attached.
An e-commerce business deploys an AI chat agent on WhatsApp to handle order inquiries, returns, and product questions around the clock. The agent resolves over 80% of tickets without human intervention, freeing up the support team.
A company creates an internal chat agent that answers employee questions about HR policies, IT procedures, and compliance guidelines. RAG ensures responses are always based on the latest approved documents.
A professional services firm uses an AI agent to qualify inbound leads via WhatsApp. The bot asks targeted questions, scores prospects, and routes high-value leads to sales reps with full context attached.
Technology Stack
Let's discuss how this solution fits your business.