Turning WhatsApp Bursts into Briefs: A Local AI Tool for Freelancers & Small Businesses

A developer built Client Buddy, a privacy-first, local-first client intelligence dashboard that captures live WhatsApp message streams via an authenticated headless browser session and aggregates them with a custom 2-minute sliding-window batching engine. The tool runs open-weight models (llama3.2:latest or gemma:2b) locally through Ollama to generate structured executive briefs with deliverables, deadlines and suggested reply drafts, and escalates critical incidents via an ElevenLabs-synthesized voice alert delivered through Twilio. Raw messages are purged by a MongoDB 7-day TTL index, and the system enforces a strict zero auto-reply policy requiring human discretion.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle https://dev.to/challenges/hacktoberfest-week2-2026-10-12 Client communication on WhatsApp is notoriously chaotic. Freelancers and agency owners rarely receive structured project briefs; instead, clients send rapid-fire bursts of fragmented messages that bury essential scope shifts, budgets, and deadlines. I built Client Buddy formerly ClientPulse AI , a privacy-first, local-first client intelligence dashboard. It solves the "fragmented burst" problem by securely capturing live WhatsApp message streams via an authenticated headless web session and aggregating them using a custom 2-minute sliding-window batching engine. Once a client finishes typing, Client Buddy processes the grouped messages entirely locally on your hardware to generate structured executive briefs containing explicit deliverables, hard deadlines, and tone-matched suggested reply drafts. To respect professional boundaries, it enforces a strict "Zero Auto-Reply" policy—the application never messages clients automatically, ensuring all outbound communication requires human discretion via 1-click copy-paste drafts. For severe incidents like a server crash or payment failure , the system flags the batch as CRITICAL and triggers an immediate outbound phone call alert to your mobile device using a synthesized AI voice. Video Demo https://youtu.be/T6 h7ZFzigc https://youtu.be/T6 h7ZFzigc Code Base https://github.com/Mritunjaii/Client-Buddy.git https://github.com/Mritunjaii/Client-Buddy.git Client Buddy is structured as a modern monorepo separating the dashboard from the heavy-lifting background workers. The Ingestion Engine: I used whatsapp-web.js with Puppeteer to run a headless browser session that securely captures live incoming messages. The Sliding-Window Batcher: To prevent disjointed summaries, incoming messages from the same chat reset a 120-second inactivity countdown flushAt . I used MongoDB to store these pending batches, implementing atomic lease locks findOneAndUpdate to guarantee thread safety and exactly-once processing across background workers. Local-First AI: The background worker feeds the aggregated batch into Ollama, running open-weight models llama3.2:latest or gemma:2b locally. The prompt forces strict JSON outputs to populate the Next.js dashboard with requirements, open questions, and drafted replies. Critical Alerts: If the local LLM classifies a burst as a CRITICAL emergency, the backend calls the ElevenLabs API eleven multilingual v2 to synthesize a realistic voice briefing, and uses Twilio to literally ring my mobile phone with the alert. Zero-Footprint Privacy: Raw source messages are protected by a native MongoDB 7-day TTL index expireAfterSeconds: 0 , automatically purging raw chat records to maintain GDPR compliance. When you are managing client relationships, privacy is non-negotiable. Processing client NDAs, proprietary code snippets, and financial budgets through a closed-source cloud API is often a massive compliance violation. Open innovation made Client Buddy possible. By leveraging local inference through Ollama and open-weight models, sensitive conversation histories never leave the user's machine for analysis. It provides enterprise-grade intelligence with zero cloud LLM exposure and zero per-token costs. Open models give freelancers the power of advanced AI without forcing them to compromise their clients' trust. I am submitting Client Buddy for the following prize categories: - Best Use of Gemma: Utilized locally via Ollama as the core reasoning engine to extract structured deadlines, requirements, and reply drafts from messy chat data. - Best Use of MongoDB Atlas: Used for complex state management, atomic worker lease locks findOneAndUpdate for the batching engine, and TTL indexes for privacy-first raw data purging. - Best Use of ElevenLabs: Integrated the eleven multilingual v2 model to synthesize crystal-clear, natural speech for outbound emergency PSTN phone calls.