This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
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.
Demo
Video Demo[https://youtu.be/T6_h7ZFzigc]
Code
Code Base[https://github.com/Mritunjaii/Client-Buddy.git]
How I Built It
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.
Why Does Open Innovation Matter?
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.
Prize Categories
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.
Top comments (2)
고객의 연속 WhatsApp 메시지를 120초 창으로 묶어 요구사항·기한을 정리하고, 답장은 자동 발송하지 않고 사람이 확인하게 한 경계가 좋습니다. 다만 CRITICAL 알림은 ElevenLabs·Twilio를 거치므로 통화에는 고객 원문이나 예산·연락처 대신 사건 ID와 긴급 여부만 담고, 자세한 내용은 로컬 대시보드에서 확인하도록 분리하면 개인정보 보호 설명과도 더 잘 맞겠습니다.
Excellent project for the challenge!