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Ryan Akmal's avatar
Ryan Akmal
Banda Aceh, Indonesia
AI Automation Engineer. I build production-ready AI systems
New to Contra
AI Automation Engineer. I build production-ready AI systems
Cover image for Building Streak — An AI-Powered
Building Streak — An AI-Powered Habit Tracker Focused on Consistency & Accountability Overview Streak is an AI-powered habit tracking platform designed to help users build consistency through structured accountability, intelligent reminders, and real-time progress tracking. Unlike traditional habit trackers that simply record completed tasks, Streak was built to function more like a behavioral coaching system — combining habit management, AI-driven motivation, streak tracking, and conversational accountability into a single experience. The platform includes onboarding flows, AI coaching conversations, real-time habit tracking, reminder systems, progress reviews, and subscription-based feature access designed for long-term engagement and retention. I led the full development of the platform, including the onboarding experience, AI coaching system, real-time application architecture, subscription logic, and behavioral engagement workflows. Goal The primary goal was to create a habit tracking experience that encourages long-term consistency rather than short-term motivation. Most habit apps fail because users lose momentum after the first few days. Streak was designed to solve that problem by combining: - accountability systems - proactive reminders - AI-driven coaching - streak psychology - weekly progress reflection - frictionless check-ins The platform needed to feel fast, personal, and emotionally engaging while still maintaining a clean and modern user experience. What I Built I developed the platform end-to-end with a strong focus on performance, retention, and real-time interaction. My work included: - Building the onboarding-first habit setup experience - Implementing authentication and workspace management - Developing real-time habit tracking and check-in systems - Building AI coach messaging workflows powered by Open AI - Implementing AI intent parsing for contextual coaching responses - Creating proactive reminder pipelines and weekly review systems - Building free vs pro subscription enforcement systems - Designing responsive UI components and habit management flows - Developing real-time state synchronization across habits, chats, and streak activity - Optimizing application performance and interaction speed Workflow & System Design One of the key challenges was designing the platform around behavioral consistency instead of simple task completion. The AI layer was designed to: - understand user intent and emotional context - encourage habit completion naturally - provide accountability-oriented responses - trigger reminders and motivational nudges - support long-term user engagement patterns - The system architecture also prioritized: - real-time responsiveness - low-friction interactions - scalable habit state management - fast onboarding and retention optimization The overall experience was designed to feel lightweight, motivating, and habit-forming without overwhelming the user. Implementation The platform was built using a modern real-time full-stack architecture. Core technologies including Next.js, React, Convex realtime backend, Clerk authentication, Open AI integration, Web Push notifications The application leveraged real-time state synchronization to ensure habits, streaks, reminders, and AI interactions updated instantly across the user experience. Result Streak successfully delivered a fast and engaging habit tracking experience centered around accountability and consistency. The platform enabled users to: - track habits in real time - maintain streak motivation - receive proactive AI coaching - stay accountable through reminders and reviews - reduce friction in daily habit management The AI coaching system added a more personal and engaging layer to the product, helping the platform feel more interactive than traditional habit trackers. Outcome This project demonstrated how AI can be applied to productivity and behavioral systems in a more meaningful way, not simply as a chatbot feature, but as an engagement and accountability layer integrated directly into the product experience. The result was a modern habit platform designed to help users stay consistent, build momentum, and maintain long-term behavioral progress more effectively.
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Cover image for Building an AI-Powered Website Chatbot
Building an AI-Powered Website Chatbot Platform for Customer Support & Lead Conversion Overview Chattiphy is an AI-powered website chatbot platform designed to help businesses engage visitors, automate support, capture leads, and improve conversion directly from their website. The platform allows businesses to embed a fully customizable AI assistant into their landing pages and web applications without complex setup. Beyond basic chat automation, Chattiphy provides conversation analytics, AI training systems, widget customization, lead tracking, and workflow management tools through a centralized dashboard. I led the end-to-end development of the platform, including the chatbot infrastructure, widget embedding system, AI response workflows, analytics dashboard, customization engine, and conversation management tools. Goal The goal of the project was to transform traditional website chat widgets into intelligent AI-powered customer interaction systems that could operate 24/7 while maintaining a natural and branded user experience. The platform needed to help businesses: - answer visitor questions instantly - reduce repetitive support workload - capture and qualify leads automatically - improve customer engagement - increase conversion opportunities - provide scalable customer communication workflows Rather than building a simple chatbot popup, the focus was creating a business-ready AI engagement platform that integrates seamlessly into modern websites. What I Did I built the platform from the ground up with a strong focus on usability, scalability, and real-world business adoption. My work included: - Building the AI chatbot infrastructure and messaging workflows - Developing the embeddable website widget system - Creating the admin dashboard and analytics platform - Designing customizable widget themes and appearance settings - Implementing AI knowledge training from website content and FAQs - Building lead capture and visitor engagement workflows - Creating conversation tracking and performance analytics - Developing bot behavior and response configuration systems - Implementing multi-source AI context handling - Building integration-ready backend systems for future scalability Workflow & System Design One of the core challenges was designing the platform as an intelligent customer engagement system instead of a static support widget. The AI layer needed structured access to: - website content and documentation - FAQs and support resources - conversation history - lead capture workflows - widget behavior configuration - business tone and response rules This allowed the chatbot to provide more relevant, contextual, and human-like responses tailored to each business. The platform was designed around operational simplicity: easy embedding, fast setup, customizable branding, measurable engagement metrics, and scalable AI-driven communication. Implementation The platform was built using modern full-stack architecture and AI-driven conversational systems. Key implementation areas included: - React-based dashboard interface - embeddable website chatbot widget - AI-powered conversational workflows - widget appearance & theme customization - analytics and engagement tracking - AI knowledge base systems - lead capture workflows - real-time messaging infrastructure - role-based workspace management - scalable backend architecture Result The platform successfully enabled businesses to deploy AI-powered chat experiences directly on their websites with minimal setup. Businesses were able to automate visitor communication, engage users instantly, reduce manual support workload, capture more qualified leads, improve customer experience and monitor chatbot performance through analytics dashboards The system achieved fast AI response times while maintaining a clean, branded, and customizable customer experience across different website types. Outcome This project demonstrated how AI chat systems can evolve beyond traditional support widgets into scalable business communication infrastructure. Instead of acting like a generic chatbot, Chattiphy was designed to function as an intelligent website engagement layer that helps businesses support users, capture opportunities, and improve customer interaction workflows in a more efficient and scalable way.
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Cover image for Voxify | AI Text-to-Speech Platform Built
Voxify | AI Text-to-Speech Platform Built a production-ready AI voice platform that lets creators, businesses, and developers generate realistic speech audio at scale — fast, clean, and API-ready. What the platform does Multi-voice generation with customizable expression and speaking style, organization-based workspace collaboration, real-time audio synthesis with low latency playback, developer-friendly REST APIs, and usage tracking with credit and analytics systems. What I built Led end-to-end development across the full stack — dashboard experience in Next.js, Python FastAPI service on Modal (GPU A10G) for TTS inference, Prisma with PostgreSQL for data modeling, Cloudflare R2 for audio storage and delivery, and a typed API layer using tRPC and OpenAPI typegen. Auth and org management handled via Clerk. Stack Next.js + TypeScript — Python FastAPI — Prisma + PostgreSQL — Cloudflare R2 — tRPC — Clerk — Tailwind + shadcn/ui — TanStack Query — Zod The result A scalable voice infrastructure product, not just a demo. Teams can generate high-quality voiceovers instantly, manage multiple voice profiles, and plug audio generation directly into their products through a clean API. Built for creators, built for developers, built for scale.
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Cover image for Building an AI-Powered WhatsApp Sales
Building an AI-Powered WhatsApp Sales & Support Platform for Businesses Overview Wabrix is a WhatsApp automation platform designed to help businesses handle customer conversations at scale using AI. The platform enables brands to automate support, qualify leads, answer FAQs, recover abandoned customers, and manage conversations directly through WhatsApp with a fast, human-like AI assistant. I led the end-to-end development of the platform, including the AI chat infrastructure, WhatsApp integration system, onboarding flow, automation builder, analytics dashboard, and real-time conversation management tools. Goal The main goal was to turn WhatsApp from a manual support channel into an AI-powered operational workflow that could increase response speed, reduce support workload, and improve customer conversion. From a business perspective, the platform needed to do more than simply auto-reply messages. It had to function as an intelligent sales and support assistant that could: - qualify leads automatically - guide customers through buying decisions - handle repetitive support requests - recover potential lost sales - escalate conversations to human agents when needed The product was designed to help businesses respond instantly while maintaining a natural and branded customer experience. What I Did I built the platform from the ground up with a strong focus on scalability, automation, and real-world business usability. My work included: - Building the WhatsApp AI chat infrastructure and messaging workflows - Developing the admin dashboard and conversation management system - Creating automation flows for support, lead qualification, and customer follow-up - Integrating WhatsApp Cloud API for real-time messaging - Designing AI context handling for better response accuracy and brand consistency - Implementing multilingual support for broader business adoption - Building analytics and reporting systems for tracking engagement and response performance - Creating human handoff systems for support escalation - Developing onboarding flows to simplify setup and adoption for businesses Workflow / System Design One of the most important parts of the project was designing the platform as a business workflow system rather than a simple chatbot. The AI layer needed structured access to: - customer conversations and message history - business knowledge and FAQs - sales and support workflows - lead qualification logic - human escalation rules - response behavior and brand tone This allowed the AI to operate more like a real support and sales team member instead of a generic assistant. The platform was designed around operational efficiency: automated responses, structured customer flows, measurable outcomes, and smooth human collaboration when needed. Implementation The platform was built using modern full-stack architecture and real-time messaging systems: - React-based admin dashboard - WhatsApp Cloud API integration - AI-powered conversational workflows - real-time messaging infrastructure - automation and escalation systems - analytics and conversation tracking - multilingual AI response handling - role-based workspace management - performance monitoring and reporting tools Result The platform successfully helped businesses automate customer communication while improving operational efficiency and response speed. Businesses were able to: - reduce manual support workload - respond to customers instantly - increase lead engagement - improve conversion opportunities - manage conversations more efficiently through centralized workflows The system achieved extremely fast AI response times, averaging around 17 seconds for automated support interactions. Outcome This project demonstrated how AI can be applied to WhatsApp in a practical, business-focused way. not as a gimmick, but as a scalable communication workflow that supports sales, support, and customer experience simultaneously. The result was a platform that helped businesses operate faster, respond smarter, and create a more seamless customer journey directly inside WhatsApp.
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PostgreSQL(3)
Ridha Amaly's avatar
Ridha Amaly
Malang, Indonesia
AI Automation | CRM Development | n8n | GHL
17
Followers
AI Automation | CRM Development | n8n | GHL
Cover image for Hi, i'm new here. I want to share my recent project. AI Know...
Hi, i'm new here. I want to share my recent project. AI Knowledge Base Automation (RAG Pipeline). This workflow is a sophisticated backend solution for building custom AI assistants. It automates the creation of a Retrieval-Augmented Generation (RAG) knowledge base, transforming your static business documents into a dynamic, intelligent, and queryable AI memory. Core Integrations: . Google Docs: Serves as the primary source for knowledge (e.g., SOPs, FAQs, product information). . Google Gemini AI: Leveraged for its powerful embedding models to understand the semantic meaning of the text. Open to any collaboration or internship. Thank you.
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Cover image for Ringcentral + N8N Automated AI Call Quality Assurance & CRM ...
Ringcentral + N8N Automated AI Call Quality Assurance & CRM Sync I engineered an enterprise-grade automation pipeline using n8n, RingCentral, and Anthropic (Claude AI) to revolutionize call monitoring. Key Features: AI-Powered QA: Automated analysis of call transcripts with sentiment scoring, summary generation, and performance grading. Real-time Alerts: Instant Slack notifications for low scores or critical compliance flags. CRM Integration: Seamless data syncing to GoHighLevel (GHL) and Google Drive/Docs reporting. Smart Logic: Implemented advanced deduplication and automated missed call SMS auto-replies. This solution eliminated manual call reviews
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Cover image for Hi everyone, i want to share my recent project. AI Multi-Age...
Hi everyone, i want to share my recent project. AI Multi-Agent Sales Chatbot (RAG + Semantic Search) ✅ This is a sophisticated AI sales agent for a parts shop. It uses an AI "router" to understand customer intent and dispatch the query to the correct specialized agent. 🎯 Core Integrations: .WhatsApp (via Webhook) .Google Gemini (Multi-Agent AI) .Supabase (Vector Database) .Google Sheets (Live Product Catalog) 📚 Key Functions: . Intent Router: A Gemini agent classifies intent (SOP, Product, Greeting). . RAG Agent: Answers policy/SOP questions ("Can I return this?") by querying a Supabase knowledge base. . Semantic Search Agent: For product queries ("Got brake pads for a 2019 Brio?"), it extracts entities, searches the Google Sheet catalog, and provides precis stock/price info. Open to any collaboration. Let's discuss more.
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Cover image for This workflow is a smart multi-user expense tracking automat...
This workflow is a smart multi-user expense tracking automation built with n8n, designed to simplify daily purchase reporting for teams or businesses. It integrates WhatsApp, Airtable, and AI message parsing, allowing multiple users to send expense data directly through chat — no manual Airtable entry needed.
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PostgreSQL(1)
Naufal Rabbani's avatar
Naufal Rabbani
proBekasi, Indonesia
Full Stack Web Developer | UI Designer
Full Stack Web Developer | UI Designer
Cover image for Annur School
Annur School
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Cover image for Legenda Permata
Legenda Permata
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Cover image for Neuromage
Neuromage
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PostgreSQL(2)
Abyan Hisyam Al'ammar's avatar
Abyan Hisyam Al'ammar
Yogyakarta, Indonesia
Backend & AI Integration Engineer
New to Contra
Backend & AI Integration Engineer
Cover image for CDSS AI Keperawatan
CDSS AI Keperawatan
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Cover image for Hey, I'm Alammar👋 Backend &
Hey, I'm Alammar👋 Backend & AI Integration Engineer from Indonesia. I specialize in AI-powered systems, automation workflows, and production-grade backend services. Currently in active development on CDSS AI Keperawatan, a multi-agent clinical decision support system for Indonesian nurses: LangChain multi-agent orchestration (zero-shot, maker-checker, parallel critic) RAG pipeline over SDKI/SLKI/SIKI clinical databases Tamper-evident WORM audit ledger for mediko-legal compliance Real-time streaming on FastAPI + Next.js We're being deliberate about the rollout. Official clinical documents are still in process, and we're actively consulting with doctors and nurses to ensure the system stays within the right privacy, security, and ethical boundaries for Indonesian healthtech. Because AI in healthcare isn't just about what the model can do — it's about earning the trust to deploy it responsibly. If you're building something where that kind of care matters — let's connect. #HealthTech #AIIntegration #FastAPI #LangChain #BackendEngineer
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RentalIqra
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Cover image for Attesta
Attesta
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PostgreSQL(1)
Timotius Albert's avatar
Timotius Albert
Indonesia
High-Performance FullStack Web Dev Solutions 🚀
High-Performance FullStack Web Dev Solutions 🚀
Cover image for Indonesia Project
Indonesia Project
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Australian project
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Singapore Project
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Ardi Nugraha's avatar
Ardi Nugraha
Banten, Indonesia
Experienced in turning your ideas into real applications 💫
Experienced in turning your ideas into real applications 💫
Cover image for Backend Development - tokopedia.com
Backend Development - tokopedia.com
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Cover image for Website Development - imajin.id
Website Development - imajin.id
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Cover image for Website Development - provenantcloud.com
Website Development - provenantcloud.com
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indra putra's avatar
indra putra
Surabaya, Indonesia
Building web apps from zero to production
New to Contra
Building web apps from zero to production
Cover image for Pathfinder Education - Networking Platform
Pathfinder Education - Networking Platform
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Cover image for Jambu - Study Abroad University Platform
Jambu - Study Abroad University Platform
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Cover image for Study in Asia - Education Platform
Study in Asia - Education Platform
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Cover image for Stridash - Fitness Dashboard Application
Stridash - Fitness Dashboard Application
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PostgreSQL(2)