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Automated Appointment Booking & Calendar Syncing System
Overview:
Engineered an automated end-to-end booking and scheduling workflow designed to process client booking requests, validate appointment availability, sync calendar events, and manage confirmations seamlessly.
Key Technical Capabilities:
Booking Request Validation: Checks incoming booking criteria and validates appointment availability before processing.
Calendar Event Integration: Automates the creation of Google Calendar events and updates booking statuses dynamically.
Status Tracking & Google Sheets Sync: Updates booking stages in real time (Mark Appointment Booked / Booking Pending / Booking Failed) inside Google Sheets.
Automated Confirmation Emails: Dispatches confirmation notifications to clients via Gmail API as soon as an appointment is locked in.
Complete Exception & Error Handling: Formats and captures booking errors, logs failure points, and marks booking status accordingly to ensure no missed client interactions.
Tech Stack & Tools:
n8n, Google Calendar API, Gmail API, Google Sheets API, Webhooks & JSON Data Transformation.
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𝐀𝐈 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 & 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝
Atomie™ is a modern AI-powered automation dashboard designed to help businesses simplify complex workflows, reduce repetitive manual work, and make better operational decisions.
The platform brings AI automation, workflow management, performance analytics, and business insights into one clean and intuitive interface. Instead of managing disconnected tools and repetitive processes, teams can use a centralized dashboard to monitor operations, automate tasks, and understand what is happening across their business.
What I Built
AI-powered business automation dashboard
Workflow and task automation interfaces
Real-time performance and operational insights
Clean, responsive dashboard experience
Intuitive navigation for complex business processes
AI-focused UX designed around simplicity and usability
Scalable interface structure for future automation features
The Goal
The main goal was to make advanced AI automation feel simple and accessible. The interface was designed to reduce complexity while giving businesses a clear overview of their workflows, performance, and automation activity.
Atomie™ demonstrates how AI, automation, and modern product design can come together to create a smarter operating experience for growing businesses.
Focus: AI Automation · Business Operations · SaaS Dashboard · Workflow Automation · Product UI/UX · AI Systems
Nice work
Looking for Data Entry & Virtual Assistant Projects
Hi everyone! 👋
I’m Aneetha, a Virtual Assistant specializing in:
• Data Entry & Data Collection
• Excel & Google Sheets
• Web Research
• Data Cleaning & Organization
• Admin Support
I’m available for both short-term tasks and ongoing projects.
If you need reliable and accurate support, feel free to connect with me. 😊
Hello I am interested this work
Enterprise RAG AI: Private Doc Intelligence
A fast-growing B2B software firm needed a secure, enterprise-grade Retrieval-Augmented Generation (RAG) system to query thousands of confidential internal documents (legal contracts, technical architecture specs, Notion pages, and employee SOPs) in real time without third-party data leakage.
Key Challenges:
High hallucination risk and irrelevant context retrieval using standard keyword search.
Managing multi-format files (scanned PDFs, tables, markdown) with strict access control based on user hierarchy.
Technical Implementation & Solution:
• Engineered an automated ingestion pipeline in Python with LlamaIndex and LangChain for semantic chunking and metadata preservation.
• Integrated a hybrid vector search architecture pairing dense embeddings (OpenAI text-embedding-3) with sparse BM25 indexing in Qdrant.
• Added a cross-encoder re-ranking layer (Cohere Re-ranker) to prune low-relevance chunks, reducing API token costs by 40%.
• Built robust Role-Based Access Control (RBAC) via FastAPI endpoints, ensuring departmental data isolation.
Results & Impact:
• Dropped internal information retrieval time from 20 minutes to under 4 seconds.
• Attained 98% factual precision with traceable in-line page citations.
• Full on-prem Docker deployment ensuring 100% private data security.
I'd test revoking a user's document access mid-conversation. In your RBAC flow, do cached retrieval results get rechecked against current permissions before they're sent to the reranker or model?