AI Automation Engineer | Full-Stack Apps & Integrations
AI Automation Engineer | Full-Stack Apps & Integrations
ML AI | Backend | Computer Vision | GenAI | LLM Agents
AI-Powered PDF Data Extraction
Organizations often face challenges in extracting useful information from a large volume of unstructured data in PDF format.
The lack of standardized data formats makes it difficult to efficiently retrieve relevant information, when dealing with documents.
The goal of this solution was to develop a flexible automated solution that allows users to specify entities to extract, enabling quick and accurate data retrieval from diverse PDFs.
The demo is available on request.
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LakeShield - AI-Powered Video Monitoring and Vessel Intelligence Platform
I led the development of LakeShield as the Senior AI/ML Engineer and Lead Developer, taking the platform from initial research and experimentation to a scalable production system.
My responsibilities included:
🔹 Designing the end-to-end AI and video-processing architecture
🔹 Building YOLO-based boat and vehicle detection pipelines
🔹 Developing object tracking and movement-analysis workflows
🔹 Implementing OCR for extracting boat registration information
🔹 Creating scalable pipelines for processing thousands of surveillance videos
🔹 Developing FastAPI backend services and automated data workflows
🔹 Building a Next.js analytics dashboard integrated with Supabase
🔹 Deploying and operating the AI pipeline on cloud GPU infrastructure
🔹 Optimizing model accuracy, inference speed, infrastructure costs, and reliability
🔹 Managing production monitoring, troubleshooting, maintenance, and continuous improvements
The platform transforms raw surveillance footage into structured operational insights, enabling automated vessel monitoring, vehicle activity analysis, registration extraction, and reporting.
This project involved complete technical ownership across Computer Vision, AI/ML, backend development, cloud infrastructure, data engineering, MLOps, and production operations.
#ComputerVision #VideoAnalytics #ArtificialIntelligence #ObjectDetection #OCR #MLOps #FastAPI #NextJS #Supabase #CloudEngineering
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Shelfr - AI-Powered Retail Shelf Intelligence Platform
I led the development of Shelfr as the Senior Computer Vision Engineer and Lead Developer, taking the platform from the initial idea and system architecture through development, deployment, and production operations.
My work included:
🔹 Designing the complete computer vision and backend architecture
🔹 Building product detection, shelf analysis, OCR, and image-processing pipelines
🔹 Developing APIs and scalable data-processing workflows
🔹 Deploying and managing production systems on GCP cloud servers
🔹 Optimizing model accuracy, processing speed, and infrastructure performance
🔹 Managing production monitoring, reliability, troubleshooting, and ongoing improvements
🔹 Leading technical decisions across AI, backend, cloud infrastructure, and DevOps
The platform converts real-world retail shelf images into structured product and shelf-level insights, helping automate retail auditing, product visibility analysis, and inventory workflows.
#ComputerVision #RetailAI #LeadDeveloper #AIEngineering #GCP #MLOps #Python #CloudEngineering
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⚖️ Built a French Legal AI Assistant powered by advanced RAG and LLM technology.
The system enables users to ask complex legal questions and receive accurate, context-aware answers grounded in French legal documents.
Key features include:
🔹 Custom legal document ingestion and chunking
🔹 Metadata-based vector search
🔹 Hybrid retrieval and reranking
🔹 Agentic RAG workflows using LangGraph
🔹 Source-grounded answers with legal references
🔹 Private deployment on an Azure VM using locally hosted LLMs
The main focus was improving retrieval accuracy, reducing hallucinations, and making large collections of legal documents easier to search and understand.
#LegalAI #RAG #LLM #ArtificialIntelligence #LangGraph #Azure #GenerativeAI #MachineLearning
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AI/ML & Data Solutions Engineer
Developed a full-stack language learning application tailored for Luxembourgish, combining speech recognition, natural language understanding, and generative AI. Fine-tuned OpenAI’s Whisper model for accurate Luxembourgish transcription and built a custom text-to-speech (TTS) engine for realistic audio feedback. A RAG-based architecture enables the app to answer user queries contextually, making learning highly interactive. The frontend is built with React, while Flask powers the backend. Designed to deliver an immersive, conversation-driven auditory learning experience.
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Hey Contra!
Excited to finally be here and connect with builders, founders, and innovators from around the world.
I'm Osman, an AI Engineer specializing in AI Agents, LLM Applications, Automation Systems, and Full-Stack Development. Over the past 3+ years, I've helped businesses transform ideas into intelligent products using OpenAI, Gemini, LangChain, LangGraph, FastAPI, Django, and modern cloud technologies.
Some of the solutions I've built include:
• AI Agents and Multi-Agent Systems
• RAG Applications with Private Knowledge Bases
• AI-Powered SaaS Products
• Automation Workflows with n8n, Zapier, and Make
• Voice AI Agents with Twilio and ElevenLabs
• Custom Chatbots and Internal AI Tools
• Data Automation and Web Scraping Systems
I enjoy solving complex problems and building products that create real business value—not just demos.
Looking forward to connecting with founders, startups, agencies, and teams working on exciting AI projects.
If you're building something ambitious with AI, let's talk.
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Worked on an RLHF (Reinforcement Learning from Human Feedback) pipeline focused on dataset creation, data annotation, and model evaluation. My role involved designing and curating high-quality prompt datasets, reviewing AI-generated responses, and providing structured feedback based on accuracy, relevance, safety, and helpfulness. Contributed to improving model performance by ensuring consistent evaluation standards and high-quality human feedback for training alignment and refinement.
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Trained a DreamBooth LoRA model to generate high-quality, personalized image outputs with consistent subject identity across different prompts and styles. The project involved dataset preparation, image captioning, and fine-tuning diffusion models using LoRA for efficient training and deployment. The solution enables fast generation of customized visuals while preserving subject consistency, style control, and high fidelity, suitable for creative, branding, and content generation use cases.
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Scaling B2B revenue via advanced agentic automation.
Autonomous AI Voice Agent: 24/7 Inbound Call & Appointment Scheduler
Built an end-to-end autonomous AI Voice Agent designed to handle inbound business calls, answer FAQs with ultra-low latency, and book client appointments directly into calendar systems.
🛠️ Key Features & Technical Architecture:
• Natural Conversational AI: Integrated Vapi AI for lifelike, natural-sounding voice interactions with zero awkward delays.
• Custom Python Backend: Engineered robust backend logic using Python to manage dynamic call flow states, session context, and lead data.
• Automated Scheduling Sync: Connected real-time webhook integrations to check live availability and instantly lock in calendar bookings during the call.
• 24/7 Business Coverage: Eliminates missed inquiries and converts cold inbound calls into confirmed client appointments automatically.
💻 Tech Stack: Vapi AI, Python, Webhooks, REST APIs, Calendar Integration
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Premium 3D Dental Infrastructure: Dynamic AI-Driven Conversion Engine
The Objective:
To modernize patient acquisition for high-end clinical practices by replacing outdated, static web layouts with an immersive, high-converting digital ecosystem.
The Solution:
Engineered a premium, fully responsive dental web infrastructure designed to capture high-value leads automatically. Moving completely away from traditional static pages, this platform utilizes high-performance dynamic components and fluid visual animations to build instant brand authority. The ecosystem features seamless lead generation architectures paired with a fully integrated, highly autonomous Agentic AI Chatbot to guide users from initial discovery to booking.
Core Capabilities & Tech Highlights:
Dynamic 3D Web Experience: Implements cutting-edge animations and interactive UI layers that break the mold of standard medical websites, significantly maximizing user session duration.
Agentic AI Integration: Features an intelligent conversational agent—not a basic keyword-matching chatbot—capable of interpreting complex patient inquiries, qualifying leads, and handling advanced intent workflows autonomously.
High-Conversion Lead Capture: Strategically positioned, interactive lead generation forms mapped to user behavior to maximize data capture while minimizing friction.
Automated Intake Pipelines: Built to capture user interactions and route pre-qualified inquiry data directly to back-end administrative systems, removing operational drag.
The Impact:
Developed a scalable, high-retention blueprint for the healthcare sector that transforms passive web traffic into automated, pre-qualified customer pipelines.
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AI Commerce Agent: Dynamic Product Retrieval System
The Objective:
To bridge the gap between inspiration and purchase by creating a seamless, agentic shopping experience for an e-commerce brand (Home Vibe Club).
The Solution:
Standard chatbots only return text, creating friction for the user. To solve this, I architected an advanced AI Commerce Agent capable of executing complex retrieval tasks. Instead of just answering questions, this agent actively queries the product database and renders interactive, highly visual product cards directly within the chat interface.
Core Capabilities & Tech Highlights:
Agentic Workflows: The AI autonomously decides when to trigger a product search based on user intent.
Dynamic UI Rendering: Automatically injects clickable, aesthetic product cards into the conversation stream.
Conversational Commerce: Reduces the barrier to entry, allowing users to browse, discover, and access products without ever leaving the chat window.
High-Retention Design: Built with a dark-mode, premium aesthetic to keep users engaged and increase session duration.
The Impact:
Transformed a static browsing experience into an interactive, high-conversion sales pipeline.
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Enterprise AI Outbound Engine: Programmatic Domain Auditing & Hyper-Personalized Lead Acquisition
The Objective
To eliminate the extremely low conversion rates associated with generic, high-volume cold email strategies. This asset was engineered to build an automated, zero-latency outbound prospecting infrastructure that conducts programmatic technical audits on target company websites in real time. By delivering immediate, hyper-personalized value to a prospect’s inbox, the system dramatically increases cold response rates and shortens sales pipeline velocity.
System Architecture & Workflow Breakdown
The underlying logic is built as an asynchronous, multi-stage agentic data pipeline that seamlessly processes targets from raw data cells to a finalized outbound delivery:
Stage 1: Automated Data Ingestion & Polling: The system monitors a centralized data core (Google Sheets) via automated webhooks, continuously scanning for newly appended prospect leads and tracking operational states to prevent duplicate processing.
Stage 2: Programmatic Web Scraping: The workflow executes custom HTTP GET requests to isolate, scrape, and ingest live front-end code, metadata, and structural configurations directly from the target company's domain.
Stage 3: Multi-Layered LLM Synthesis: The raw web payload is dynamically passed through dual-stage Large Language Model (LLM) API completion nodes. The first node isolates optimization bottlenecks on the prospect's site. The second node acts as a context-aware copywriter, translating those raw gaps into a polished, bespoke audit tailored specifically to the business owner.
Stage 4: Automated SMTP Deployment: The finalized custom audit and strategic pitch are formatted into an email payload and routed through an automated mail-server node to deliver the value statement directly to the decision-maker.
Stage 5: Closed-Loop State Sync: Upon successful delivery, a final write-back module updates the primary database row with full timestamp logs and execution states, establishing a clean audit trail.
Core Capabilities & Tech Highlights
Context-Driven Hyper-Personalization: Completely bypasses basic name-merge fields by extracting live, domain-specific text to generate authentic, high-impact value statements unique to every recipient.
Token-Optimized Payload Ingestion: Employs advanced parsing to strip out irrelevant source code before sending data to the LLM core, preserving maximum contextual relevance while drastically lowering API computational costs.
Scale-Ready Asynchronous Architecture: Engineered to execute complex web audits and custom draft writing sequentially across hundreds of target leads simultaneously without causing operational bottlenecks or memory overloads.
Inbound Funnel Multiplier: Directly interfaces with modern outbound deliverability systems, protecting domain authority by replacing blind spam with highly targeted, consultative tech audits.
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Engineering intelligent solutions with AI.
🚀 Built an AI Receptionist for Healthcare — Here's What It Can Do
Managing a healthcare front desk is challenging. Calls, appointment scheduling, patient questions, and administrative tasks often overwhelm staff, leading to long wait times and missed opportunities to deliver a great patient experience.
To address this, I built an AI Receptionist MVP designed specifically for healthcare providers. It can answer calls, schedule appointments, manage inbound and outbound communication, and respond to patient inquiries—all while staying within appropriate clinical boundaries.
Here's what it demonstrated during a live test with our demo clinic, Evergreen Community Health Center:
🧠 Intelligent Patient Conversations
Rather than following a fixed script, the AI understands context and knows its limitations.
✅ Recognized that dermatology wasn't offered and suggested available services instead:
General Practice
Pediatrics
Physical Therapy
Dental Care
✅ Clearly avoided giving medical advice by responding:
"As a receptionist, I'm not qualified to provide medical advice."
✅ Offered the appropriate next steps by either:
Booking a GP appointment for an initial assessment, or
Transferring the caller to clinical staff for medical questions.
📅 Smart Appointment Scheduling
The AI schedules appointments based on both clinic policies and patient preferences.
During the demo, it:
Collected the patient's information.
Suggested the next available appointment.
Adjusted the booking when the patient requested a later time.
Checked clinic operating hours automatically and successfully booked a 5:00 PM appointment before closing.
💳 Administrative Automation
Beyond booking appointments, the AI also handled routine administrative tasks by:
Explaining consultation fees.
Answering policy-related questions.
Confirming referral procedures.
Collecting the patient's email for appointment confirmation.
⚡ Built for Scale
Unlike a traditional reception desk, the AI can handle thousands of conversations simultaneously.
That means:
No busy signals
No waiting queues
No missed calls
Better patient accessibility 24/7
⚙️ Easily Customizable
Every healthcare provider operates differently.
The AI can be configured with:
Clinic-specific services
Staff availability
Business hours
Appointment rules
FAQs
Internal workflows
making it adaptable to virtually any healthcare organization.
This project demonstrates how conversational AI can streamline healthcare operations while allowing staff to spend more time focusing on patient care.
I'm excited to continue expanding its capabilities with integrations such as EMR/EHR systems, multilingual support, voice biometrics, and intelligent call routing.
💬 If you're exploring AI solutions for healthcare, I'd love to connect and discuss how conversational AI can modernize patient engagement.
#AI #ArtificialIntelligence #HealthcareAI #HealthTech #VoiceAI #AIReceptionist #Automation #ConversationalAI #PatientExperience #GenerativeAI #LLM #Innovation
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I just completed a production-ready AI Lead Generation Agent designed to automate how businesses discover, verify, and export qualified leads across multiple data sources.
This system replaces manual lead hunting with an intelligent, filter-driven pipeline that aggregates business data, verifies contact information, and delivers CRM-ready outputs in real time.
🔑 What this agent does:
• Searches businesses by location, industry, and keywords
• Aggregates data from Yellow Pages, Google Maps (API-ready), and extensible sources
• Applies smart filters (company size, founding date, industry relevance)
• Automatically verifies emails, phones, and websites
• Deduplicates leads for clean datasets
• Exports structured CSVs for sales & marketing teams
• Supports real-time queries via a FastAPI backend
• Schedules daily exports and weekly reports
🧠 Tech Stack Highlights:
• Python + FastAPI (async, high-performance backend)
• Selenium-based scraping with anti-bot handling
• Modular lead source orchestration
• Glassmorphism UI with real-time search
• CSV-based persistence (lightweight & scalable)
This project was built with real business use cases in mind — sales pipelines, outreach automation, and scalable lead discovery — not just experimentation.
🎥 Full walkthrough video:
👉
I’m actively building and sharing end-to-end AI systems focused on automation, data intelligence, and real-world impact.
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Building the Future of Hiring: Real-Time AI Interviews
I’m excited to share a demo of our AI-based recruiter automation tool, designed to streamline recruitment funnels with real-time video interviews, deep skill analytics, and cheating detection.
In this video, I demonstrate the full candidate journey:
🔹 Backend Automation: The system extracts skills from my resume and matches them against the job description automatically.
🔹 Interactive Interview: I speak directly with "Higher Vision AI" about my experience in Computer Vision, GenAI, and Agentic AI across industries like security surveillance and e-commerce.
🔹 Technical Deep Dives: We discuss real-world challenges, such as implementing voice-to-voice interaction using LiveKit and how I adapted to new integration hurdles.
🔹 Instant Feedback: The session wraps up with a comprehensive dashboard displaying my interview score, resume score, summary, and skills audit.
I also discuss how I stay ahead of trends using resources like daily.dev (http://daily.dev).
🎧 Note: Please excuse the slight echo on the agent's voice in this screen recording; it is a result of the recording setup, not the live system!
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I wanted to make an AI talk to me like a real human.
Not a chatbot that waits for you to finish speaking. Not a voice note exchange. I wanted an AI that could listen while I spoke, interrupt naturally, and respond instantly — like a personal assistant or customer-care agent. Something that didn’t just reply, but could actually do things — send emails, check dashboards, automate tasks.
So I started small. I used a basic text model with Kokui for speech-to-text. The first time it responded, it felt magical — until it started ignoring me mid-sentence, replying late, or not speaking at all. Latency was awful. It wasn’t a conversation; it was waiting for a robot to remember it existed.
I upgraded to DeepGram, tuned the audio, and still, it felt disconnected. I wanted real-time. I wanted the AI to exist in time with me, not after me.
So I went deep into research. GitHub, StackOverflow, documentation black holes, even different AI platforms — but nothing gave me the real-time link I needed.
Then I found one line buried in a doc:
“OpenAI uses LiveKit for real-time voice.”
That changed everything.
LiveKit works like Zoom — a room where participants join and talk.
I built my agent to join a LiveKit room, then I joined as well.
And for the first time, I wasn’t sending messages to a server — I was talking to an AI inside the same space.
The first test stunned me.
Latency dropped to about 50ms.
The AI listened while I spoke, and responded instantly.
For the first time, it felt alive.
Then came a strange bug.
The AI joined once, but couldn’t reconnect after leaving. I rewrote code, regenerated tokens, nothing worked — until I realized LiveKit doesn’t let you rejoin a room with the same token.
I changed the room name and boom — it worked flawlessly.
Now it runs inside LiveKit Sandbox, talking and listening in real-time.
It can send emails, check dashboards, handle automation — all with almost zero delay.
I started out trying to make a talking AI.
What I built feels more like a digital employee — one that works, listens, and speaks in real time.
And the best part?
It doesn’t feel like the future anymore.
It feels like now.
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AI Solutions Engineer | AI Automation & Web Development
Built a PhysioCare AI Voice Receptionist designed to handle physiotherapy-related conversations through a natural voice interface.
The assistant can interact with users, understand their requests, and support common reception workflows for a physiotherapy service. The project focused on combining voice AI, LLM-based conversation, and backend/API workflows into a practical healthcare application.
Key areas included:
AI voice assistant
Conversational AI
LLM integration
Physiotherapy-focused workflows
Tool/function calling
API integration
Backend workflow development
Voice conversation flow and testing.
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Built a PhysioCare MCP Server that connects an AI assistant with physiotherapy-focused tools through the Model Context Protocol (MCP).
The project demonstrates tool calling for physiotherapy use cases, allowing an AI system to interact with structured healthcare-related functions instead of relying only on static responses.
Key work included:
MCP server development
Tool/function calling
Physiotherapy-focused AI tools
Structured tool inputs and outputs
AI-to-tool communication
API-based workflow
Testing and debugging of MCP tools
This project was built as a practical AI Engineering project to explore how MCP can connect LLM-powered applications with real-world domain-specific tools.
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Physio MCP Server is a healthcare-focused Model Context Protocol (MCP) server built with Python and FastMCP.
This project demonstrates MCP client-server communication with physiotherapy-related tools, including patient progress tracking and pain improvement analysis.
The server provides tools to process patient pain scores, attendance data, and generate simple rehabilitation progress insights through an MCP client.
Tech Stack:
Python, MCP, FastMCP, MCP Client-Server Architecture
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AI Healthcare Assistant — AI Agent
Built an AI-powered healthcare assistant designed to help users with general health and physiotherapy-related guidance through an interactive AI agent interface.
Key Features
AI-powered conversational healthcare assistance
User-friendly chat interface
Health and physiotherapy-focused responses
Interactive AI agent workflow
Practical healthcare use-case implementation
Focus on accessible and personalized user support
Tech Focus
AI Engineering, LLMs, AI Agents, Python, API integration, and healthcare-focused AI workflows.
This project demonstrates my ability to design and build practical AI solutions for real-world healthcare use cases.
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AI Agents & Automation That Ship | Voice AI, LLMs, .NET
An AI agent that can move money is only useful if it can't move it on its own.
Haia lets people manage their banks, crypto wallets and brokerages from one place, just by saying what they want done.
I built the AI orchestration layer. The interesting design choice wasn't the model. It was deciding where the model is not allowed to act.
→ The agent turns a request into a step by step plan
→ Each step is routed to the right banking API, wallet, chain or brokerage
→ Security and compliance checks run before anything executes
→ The user approves every move, and transactions are signed on their own device It's fully non-custodial.
Zero private keys ever exposed, to us or to the model.
If you're building agents that touch money, health records or anything you can't undo, design the approval step first. Then the AI.
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Your support team goes home at 6. Your customers don't. An online retailer we worked with had a simple problem.
Shoppers asked questions at 11pm, on weekends, on holidays.
The answers came the next morning, and by then a lot of them had bought somewhere else.
We built a 24/7 AI chat into their store. Not a FAQ bot that says "I didn't understand that" three times.
It handles the common questions on its own, recommends products inside the conversation, and drops every chat into their CRM with the full customer context.
When a person does step in, they aren't starting from zero. → 50% more conversions → 35% faster response time → 40% less support workload The part people underestimate is the CRM link.
A chat that sells but forgets who the customer is just moves the problem to your team. Support and sales turned out to be the same conversation.
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On a phone call, silence is the bug.
A text chatbot can take three seconds to reply and nobody minds. A voice agent can't. Leave a caller in silence for a beat too long and they say "hello?", start talking over it, or hang up.
That one fact shaped everything we built on Talk-Lee, an AI voice agent that answers business calls for healthcare, real estate and finance teams. Scheduling, support, lead qualification, around the clock.
The goal was a reply in under 500ms. You don't get there with a faster model. You get there by making sure nothing waits for anything else.
→ Speech to text streams while the caller is still talking
→ An LLM and intent layer keeps track of what they actually want
→ Text to speech streams back, so the agent starts talking before the whole answer is ready
→ An orchestrator decides in real time whether to answer, book or hand off to a person
Then it has to do something useful. It books into Calendly, logs the lead in HubSpot, and passes the hard calls to a human with the context already attached.
Where it landed. Under 500ms responses, 1,000+ concurrent calls, 30+ languages, GDPR and TCPA compliant.
Third project in a row with the same lesson. The model is the easy part. The plumbing around it decides whether anyone keeps using it.
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AI Engineer | Building AI Agents, Chatbots & Automations
𝗔𝘂𝗿𝗮𝗹𝗶𝘀 𝗔𝗜 — 𝗢𝗻𝗲 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗔𝗴𝗲𝗻𝘁. 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀.
What if your business had one intelligent assistant helping you manage customer conversations, organize leads, coordinate tasks, and reduce repetitive work?
That's the vision behind 𝗔𝘂𝗿𝗮𝗹𝗶𝘀 𝗔𝗜, a concept for an AI-powered business agent designed to bring everyday operations into one connected workspace.
𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺
Businesses often manage customer messages, leads, CRM updates, scheduling, and documents across multiple disconnected tools.
This 𝗰𝗿𝗲𝗮𝘁𝗲𝘀 𝗿𝗲𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝘄𝗼𝗿𝗸, 𝗶𝗻𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝘂𝗻𝗻𝗲𝗰𝗲𝘀𝘀𝗮𝗿𝘆 𝗮𝗱𝗺𝗶𝗻𝗶𝘀𝘁𝗿𝗮𝘁𝗶𝘃𝗲 𝗼𝘃𝗲𝗿𝗵𝗲𝗮𝗱.
𝗔𝘂𝗿𝗮𝗹𝗶𝘀 𝗔𝗜 explores a simpler approach: one coordinated system that helps people and business applications work together.
𝗧𝗵𝗲 𝗣𝗿𝗼𝗽𝗼𝘀𝗲𝗱 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻
𝗔𝘂𝗿𝗮𝗹𝗶𝘀 𝗔𝗜 is designed around six core capabilities:
1) 𝗔𝗜 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 — Assist with common inquiries and customer conversations.
2) 𝗟𝗲𝗮𝗱 𝗤𝘂𝗮𝗹𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 — Capture inquiries, organize prospect information, and support follow-up decisions.
3) 𝗖𝗥𝗠 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 — Connect customer interactions with CRM updates and lead-management workflows.
4) 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴 & 𝗥𝗲𝗺𝗶𝗻𝗱𝗲𝗿𝘀 — Coordinate appointments, notifications, and recurring tasks.
5) 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 — Summarize documents, extract useful information, and assist with information processing.
6) 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 — Coordinate supported applications and business processes through agent-assisted actions.
Human Control Where It Matters
The proposed system also includes human review and approval for sensitive or consequential actions, helping businesses maintain oversight while benefiting from automation.
𝗗𝗲𝘀𝗶𝗴𝗻 & 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗗𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻
I developed the product concept and visual direction around a unified SaaS dashboard, combining customer conversations, agent activity, workflow management, and lead information in one interface.
The design uses an obsidian background, emerald and amethyst accents, layered glass panels, and structured information hierarchy to communicate a premium enterprise AI experience.
𝗧𝗵𝗲 𝗕𝗶𝗴𝗴𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻
My goal with 𝗔𝘂𝗿𝗮𝗹𝗶𝘀 𝗔𝗜 is to explore how businesses could move from disconnected manual processes toward one coordinated, intelligent system.
Less repetitive work. More organized operations. More time to focus on growth.
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𝗬𝗼𝘂𝗿 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗗𝗼𝗲𝘀𝗻'𝘁 𝗡𝗲𝗲𝗱 𝗠𝗼𝗿𝗲 𝗠𝗮𝗻𝘂𝗮𝗹 𝗪𝗼𝗿𝗸. 𝗜𝘁 𝗡𝗲𝗲𝗱𝘀 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗦𝘆𝘀𝘁𝗲𝗺𝘀.
Think about how much time you spend every week doing the same things.
Replying to customer emails.
Following up with potential clients.
Updating spreadsheets.
Managing leads.
Moving information between applications.
Sending reminders and notifications.
Now ask yourself:
𝗛𝗼𝘄 𝗺𝘂𝗰𝗵 𝗼𝗳 𝘁𝗵𝗶𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗻𝗲𝗲𝗱𝘀 𝘁𝗼 𝗯𝗲 𝗱𝗼𝗻𝗲 𝗺𝗮𝗻𝘂𝗮𝗹𝗹𝘆?
Many repetitive business tasks can be automated, yet businesses continue spending valuable hours doing them manually.
𝗧𝗵𝗲 𝘁𝗼𝗽 1% 𝗺𝗶𝗻𝗱𝘀𝗲𝘁 𝗶𝘀𝗻'𝘁 𝗮𝗯𝗼𝘂𝘁 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 24 𝗵𝗼𝘂𝗿𝘀 𝗮 𝗱𝗮𝘆. 𝗜𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗮𝘁 𝗺𝗮𝗸𝗲 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗺𝗼𝗿𝗲 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁.
Imagine a workflow where:
• A customer submits an inquiry, and their details are automatically recorded.
• Incoming emails are categorized, and relevant replies are drafted for review.
• New leads are added to your CRM without manual data entry.
• Important tasks trigger notifications and reminders automatically.
• Your business applications exchange information without constant copying and pasting.
• AI helps process documents, organize information, and handle repetitive administrative work.
𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝘁𝗮𝘀𝗸𝘀. 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀. 𝗢𝗻𝗲 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗲𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝘀𝘆𝘀𝘁𝗲𝗺.
That's what I help businesses build.
I've launched my new service on Contra:
𝗖𝘂𝘀𝘁𝗼𝗺 𝗔𝗜 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗳𝗼𝗿 𝗬𝗼𝘂𝗿 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀
I build workflows using n8n, Python, and APIs to connect business applications, reduce repetitive work, and simplify daily operations.
My approach is simple:
• Understand your existing business process.
• Identify the repetitive tasks taking up your time.
• Build an automation workflow around your actual needs.
• Test the workflow and make sure it performs as intended.
𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝘀𝗵𝗼𝘂𝗹𝗱𝗻'𝘁 𝗺𝗮𝗸𝗲 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗺𝗼𝗿𝗲 𝗰𝗼𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝗱. 𝗜𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝗺𝗮𝗸𝗲 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗶𝘁 𝗲𝗮𝘀𝗶𝗲𝗿.
You don't have to automate everything overnight. Sometimes, solving just one repetitive problem is the best place to start.
Your time should go toward growing your business, not repeating tasks that a system could handle.
Explore my service:
https://contra.com/s/2PUqmBeN-i-will-build-an-ai-automation-workflow-for-your-business
Business owners and founders: What's one repetitive task you wish you could automate today?
#AIAutomation #BusinessAutomation #WorkflowAutomation
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Veyra — AI Lead Qualification & CRM Automation
One Intelligent Platform. Multiple Business Solutions.
Managing leads, qualifying potential customers, updating CRM records, and following up with prospects can consume valuable time and resources. Businesses often rely on multiple tools and employees to handle these repetitive tasks.
Veyra is designed to bring everything together in one AI-powered workspace.
What Veyra Aims to Do
AI Lead Qualification: Analyze incoming leads, assess their potential, and recommend which opportunities deserve attention.
Smart CRM Management: Organize customer information, track sales pipelines, and manage opportunities in one place.
Automated Follow-Ups: Generate personalized messages, schedule reminders, and help prevent missed opportunities.
AI-Powered Workflows: Coordinate lead capture, qualification, CRM updates, and follow-up tasks with human approval.
Business Insights: Provide dashboards that help businesses understand their sales activities and prioritize next steps.
The Goal
To help businesses reduce repetitive administrative work, simplify sales operations, and manage more opportunities without constantly switching between different tools.
Veyra is more than a CRM concept — it's a vision for an intelligent sales operations assistant that supports teams from the first lead to the next business opportunity.
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Relay — AI Customer Support & Lead Capture
One intelligent platform. Multiple business solutions.
Running a business means handling customer inquiries, answering repetitive questions, managing leads, and following up with potential clients. These tasks often require multiple employees, tools, and valuable time.
Relay aims to bring everything together in one intelligent platform.
Designed to act as a 24/7 AI-powered business assistant, Relay helps businesses:
Automate Customer Support — Answer common questions without constant manual assistance.
Capture Potential Clients — Turn customer conversations into organized business leads.
Manage Conversations — Keep customer interactions and inquiries in one workspace.
Enable Human Handoff — Let staff take over conversations whenever needed.
Streamline Operations — Reduce repetitive administrative work and the need for separate tools.
The vision: One unified AI solution that helps businesses manage customer interactions, reduce operational workload, and grow without unnecessarily expanding their support team.
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