AI WhatsApp Assistant MVP Build

@koolvishruth

Hello, I am ML engineer from Karnataka.I can help you in building this .I have 8 years of experience in same field. Let’s connect

@ashinnd84

Hello there, we are a team of Full Stack Developers and interested in your WhatsApp based AI assistant. Please, send me the project complete details to start the work. Thanks Ashish Kumar.

@abdf2010

Hey, your project, AI WhatsApp Assistant MVP Build looks like a great fit for my skills. I've worked on similar Python projects and can deliver solid results. Let me know if you'd like to chat about the approach.

@mahads31

Having reviewed your project's specific needs, I'm thrilled to offer my services as a Web & Software Developer with extensive experience in API development, Node.js, and Python - skills directly aligned with your project requirements. Over the years, I've developed numerous scalable digital solutions across various industries, which gives me a unique edge in turning your AI WhatsApp Assistant MVP into a reality. One of my key strengths is my ability to transform complex requirements into clean, reliable, and high-impact systems - an asset that would be extremely valuable in building your AI assistant and its underlying ML pipeline. Additionally, my deep understanding of server logic and authentication will ensure your backend is not only robust but also secur. Furthermore, my focus on efficiency without compromising quality makes me ideal for fast-paced projects like yours. I believe in an agile and reliable approach that ensures timely delivery without sacrificing the product's integrity. If you choose to collaborate with me, I'll guarantee a documented codebase that's modular and can be efficiently deployed on any standard cloud stack - ensuring future evolution is smooth and seamless.

@Hrolgar

ConXbyte works best when each conversation has exactly one active state: inquiry, schedule, reschedule, cancel, or handoff. That constraint keeps the bot from going off-script when a buyer asks three things at once. I built a similar conversation engine for a property portal last year, replacing a triage form with a WhatsApp flow. Response rates from cold leads went up noticeably once buyers could reply in their own time rather than fill out a form. For the backend I'd use FastAPI with per-conversation state machines stored in Redis, so concurrent chats don't bleed into each other. WhatsApp Cloud API handles transport, webhooks land on the service, and intent plus slot extraction runs through a lightweight NLP layer that's easy to retrain as you see real traffic. Buyer-intent scoring runs as a background job after the conversation closes, not inline, so response times stay under 2 seconds regardless of scoring complexity. M1: WhatsApp webhook + per-session state machine, INR 5000, 4d. M2: Intent/slot extraction + inquiry capture flow, INR 5000, 4d. M3: Visit scheduling module with calendar integration, INR 5000, 4d. M4: Buyer-intent scoring job + REST/GraphQL API layer, INR 5000, 5d. M5: Admin dashboard, end-to-end testing, deployment, INR 5000, 4d. One thing worth confirming before I start: which calendar system do your agents use? Google Calendar, Outlook, or something custom?

@basant807

Hello, My approach: Node.js (Express) backend with modular service layers — a WhatsApp webhook handler, an ML pipeline (intent classification + property recommendation using lightweight transformer models), and a scheduling engine that syncs visits to Google Calendar. React dashboard for owners with lead summaries, intent scores, and trend snapshots. Auth via JWT + refresh tokens. PostgreSQL for structured data, Redis for session/queue management. Deliverables across 5–6 weeks: → WhatsApp flow: inquiry capture → NLP intent scoring → property match → auto-reply → Scheduling: slot detection, confirmation messages, calendar sync → ML module: buyer intent classifier + trend flag engine (trainable on your data) → Owner dashboard: lead volume, intent heatmap, market highlights → REST APIs: internal + third-party-ready with Swagger docs → Deployed on AWS/GCP with CI/CD I keep code modular, documented, and handoff-ready. What's your current data situation — do you have existing inquiry logs I can use to bootstrap the ML models, or should I start with a pre-trained base? That'll help lock the Week 1–2 sprint scope precisely. Regards, Basant

@PyMLExpert1

I'm Zainab, your go-to freelancer for all things Python and Machine Learning. With years of experience in architecting and deploying intricate AI solutions, I understand the importance of your MVP phase. Your project aligns perfectly with my skillset as it combines the power of machine learning and chatbots, something I'm passionate about. I've previously built chatbot-focused projects that achieved excellent conversational flow, data storage capabilities, scheduling functionalities, and intuitive dashboards. This experience will directly contribute to delivering a reliable end-to-end system for you. Moreover, I value clean code and comprehensive documentation which ensures long-term maintainability. Collaborating with you during the MVP phase is not only a job to me but an opportunity to showcase my abilities and establish a potential long-term partnership. I am ready to bring my technical skills and strategic thinking to yield meaningful insights for your real estate business on the WhatsApp platform, providing you with a competitive edge in the market. Let's get started!

@indreshpnk

Hello, I understand you need an **AI WhatsApp-based real-estate assistant MVP** that captures incoming property inquiries, schedules site visits automatically, and provides AI-driven business insights for owners directly inside WhatsApp. The goal is to deliver a **reliable, scalable, and intelligent automation system** that removes the need for complex dashboards while enabling real-time decision-making. Here’s what I can provide: • End-to-end WhatsApp Business API integration with structured lead capture and storage • ML-powered intent analysis to detect buyer interest, recommend properties, and generate insights • Automated site-visit scheduling synced with calendar + real-time updates to users and internal system I bring over 4+ years of experience in Python, Node.js, and full-stack development with strong expertise in building scalable backend systems, APIs, and AI-driven applications. I’ve worked on SaaS platforms and automation systems focusing on performance, clean architecture, and production-ready deployment. Just to clarify a few things: • Do you already have WhatsApp Business API access set up or need assistance with integration? • Which calendar system should be integrated for scheduling (Google Calendar or custom solution)? Please come to the chat box to discuss more about your project. Best regards Indresh Kushwaha

@fabianofff

ConXbyte MVP sounds exciting. I build WhatsApp AI assistants weekly using the official Meta WhatsApp Business API, GPT-4o, and Python/Node.js backends. For your WhatsApp assistant MVP I will deliver: 1. Meta WhatsApp Business API or Z-API setup with production-ready webhooks 2. AI assistant trained on your business context, product catalog, and policies 3. Conversation state management across multi-turn chats 4. Rich messaging — buttons, lists, media, location sharing 5. Admin panel for reviewing conversations and tuning responses 6. Integration with your database/CRM + analytics dashboard Stack: Python FastAPI or Node.js, GPT-4o with function calling, PostgreSQL, Redis for session state. Production-grade with logs, retries, rate limits. Ready to start immediately. Can you share what ConXbyte does and the main use cases the assistant should handle?

@letswork92

Hello, I understand that ConXbyte is looking to efficiently capture property inquiries and provide actionable insights through an AI-powered WhatsApp assistant, a crucial tool for real estate teams. The challenge lies in seamlessly integrating the WhatsApp Business API with a robust machine learning pipeline while ensuring user-friendly interactions. With over 12 years of experience, I specialize in building scalable applications using Node.js for backend logic and React.js for dynamic dashboards. My expertise extends to developing clean RESTful APIs that facilitate smooth data flow and third-party integrations. Additionally, I have worked extensively with machine learning models to analyze user intent and generate real-time business insights. To ensure successful MVP delivery, I will focus on creating a modular codebase that meets your acceptance criteria while implementing solid authentication protocols. Could you share more details about the specific ML models you're considering or any existing data sources you'd like to leverage for this project? Looking forward to your response!

@harshbhandari556

Hi, let’s connect we have strong expertise in building WhatsApp-based AI systems and can deliver this faster with a clean, scalable approach. Would be happy to discuss and get started quickly

@HarisXdev

I've built this exact product before — just on Telegram instead of WhatsApp. My AI Receptionist Bot handled real-time NLP intent classification across 5 categories at 85% accuracy, automated Google Calendar scheduling end-to-end, ran on a MongoDB backend, and was fully containerized with Docker. ConXbyte's MVP is the same architecture shifted to WhatsApp Business API — and that's a migration I can handle cleanly. Here is how I'd build your MVP: Backend (Python + FastAPI): Webhook listener for incoming WhatsApp messages, NLP pipeline for buyer intent scoring, property recommendation logic, and a clean REST layer for your dashboard to consume. Scheduling: Direct Google Calendar integration — site visits confirmed automatically to both buyer and agent via WhatsApp reply. Insights module: Lead volume, intent scores, and trend summaries surfaced via a lightweight dashboard endpoint. Simple, fast, no complex UI needed for MVP. Database: MongoDB for conversation logs and lead data — schemaless and fast to iterate on during MVP phase. Deployment: Dockerized and cloud-ready from day one. You can replicate on any standard VPS or cloud instance. I work modularly and document as I go — your engineering team will be able to pick it up without hand-holding. Timeline: 3 weeks for a working MVP covering all four acceptance criteria. Ready to start immediately. Want to jump on a quick chat to align on the WhatsApp Business API credentials and data model?

@ayrens

hi there, I’ve built seamless AI-powered chat assistants before, so creating a WhatsApp-based AI assistant that captures inquiries and schedules visits feels right in my wheelhouse. Making complex data feel simple and user-friendly is something I’ve done, ensuring business insights just click for owners without any dashboard frustration. You’re after a clean, integrated backend—Python or Node.js fits perfectly—that powers smooth conversation flows and automated scheduling. I appreciate the focus on real-time ML analysis for buyer intent and trend spotting, plus the lightweight React dashboard for clear, concise insights. Keeping authentication solid and APIs future-proof is key, and I’ve got that covered. I like working closely, sharing progress often, and focusing on delivering exactly what the project needs, so timelines and quality both stay on track without stress. I’m confident I can deliver a reliable, polished MVP quickly with transparent communication every step of the way. I am available for a quick chat! Regards, Ayren Stephens