The Problem
Across the businesses we work with, the same pattern kept showing up: support teams answering the same handful of questions dozens of times a day, sales reps manually qualifying every inbound lead before it was worth a real conversation, and order-status follow-ups eating hours that should have gone to actual selling. None of it needed a human - it needed a system that never got tired of repeating itself.
[SCREENSHOT: AI chatbot conversation flow on WhatsApp]The Solution
Darwinbark builds AI-powered automation that plugs into the tools a business already runs on, rather than asking them to switch platforms. That means WhatsApp and web chatbots with AI-generated responses, lead qualification that runs before a human ever sees the conversation, automated order-status follow-ups, and a direct line into the CRM the sales team already lives in.
Technology Stack
- Backend: Laravel (PHP) - orchestrates conversation state, webhook intake, and routing between channels.
- Messaging: WhatsApp Cloud API (Meta-verified) for chat, plus a web widget for on-site chat.
- AI layer: LLM-based response generation for free-text queries, with structured intent detection for things like "where is my order" that don't need a full AI round-trip.
- CRM integration: Webhook + REST sync into HubSpot and Zoho, so leads captured by the bot land directly in the sales pipeline with no manual re-entry.
- Queues: Laravel queues handle outbound messages, drip sequences, and CRM sync so the chatbot itself replies instantly while the slower integrations happen in the background.
Architecture
Every inbound message - WhatsApp or web widget - hits a single webhook endpoint that normalises it into one internal message format, regardless of channel. From there, an intent layer decides whether the message needs a scripted flow (order status, business hours, common FAQs) or a full AI response. Qualified leads and flagged support tickets get pushed into the CRM through a queued job, so a CRM outage or rate limit never blocks the conversation itself from continuing.
Sentiment analysis runs as a secondary pass on support conversations, flagging anything that reads as frustrated or urgent so it gets escalated to a human agent instead of staying in the automated flow.
Features
- WhatsApp & web chatbot with AI-generated responses
- Lead qualification automation before human hand-off
- Order status & follow-up automation
- CRM integration (HubSpot, Zoho)
- Email/SMS drip campaigns triggered by chatbot events
- AI sentiment analysis for support ticket escalation
Challenges Solved
Avoiding the "obviously a bot" feel. Scripted flows handle the predictable, high-volume questions (hours, order status, pricing) so they're instant and accurate, while the AI layer only takes over for genuinely open-ended questions - keeping response quality high without needing the AI to be right 100% of the time.
Not losing leads to integration lag. Queued CRM sync means a slow third-party API never delays the customer-facing reply - the conversation always feels real-time even when the backend integrations are working through a queue.
Escalating the right conversations. Sentiment analysis on support tickets means a frustrated customer gets routed to a human before they've had to say "let me speak to a person" - the system catches the tone shift rather than waiting for an explicit request.
Result
Clients reduced customer support workload by 70% and improved lead response time from hours to seconds using AI automation, freeing support and sales teams to spend their time on the conversations that actually needed a human.
[SCREENSHOT: CRM lead sync dashboard]
