Freelancers using OpenAI in Lahore

Freelancers using OpenAI in LahoreFreelancers using OpenAI in Lahore

Projects People

proLahore, Pakistan

AI Automation Engineer | Full-Stack Apps & Integrations

66x: Hired

4.9: Rating

158: Followers

Expert

Expert

+2

AI Automation Engineer | Full-Stack Apps & Integrations

Cover image for Gut Health SaaS Platform Development

Gut Health SaaS Platform Development

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19

Cover image for Magnai | UK Public Affairs

Magnai | UK Public Affairs

6

89

Cover image for Humoni - secure housing in under 72 hours

Humoni - secure housing in under 72 hours

7

148

Cover image for Wellbeing Wizard AI

Wellbeing Wizard AI

8

190

Toolshed (Data, Automation, AI Agents, Buildship, Framer)

maxLahore, Pakistan

Data, Automation, AI Agents, Buildship, Framer, Bubble

$100k+: Earned

7x: Hired

5.0: Rating

76: Followers

Top

expert

+2

Data, Automation, AI Agents, Buildship, Framer, Bubble

Cover image for BuildShip AI Automation & Internal Tool for Job Analysis

BuildShip AI Automation & Internal Tool for Job Analysis

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7

Cover image for Multi-Tenant Insurance BI Platform

Multi-Tenant Insurance BI Platform

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Cover image for AI Agents & GoHighLevel Workflow Automation

AI Agents & GoHighLevel Workflow Automation

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58

Cover image for Construction FP&A & Revenue AI Platform

Construction FP&A & Revenue AI Platform

6

316

Muhammad Abdullah

Lahore, Pakistan

AI Automation Builder | Website Design

New to Contra

AI Automation Builder | Website Design

Cover image for [4:07 PM, 7/6/2026] Abdullah: AI

[4:07 PM, 7/6/2026] Abdullah: AI agent that handles scheduling end-to-end 🤖📅 [4:07 PM, 7/6/2026] Abdullah: • Hold context-aware conversations (with memory across the session) • Check your Google Calendar availability • Book new appointments directly • Send confirmation emails via Gmail • Log every interaction to Google Sheets for tracking [4:08 PM, 7/6/2026] Abdullah: No manual back-and-forth. No double bookings. Just a conversational front door that turns “can we find time to talk?” into a booked, confirmed, logged event — automatically. This is the kind of workflow I build for real estate and home services businesses who are tired of losing leads to slow follow-up. If you’re exploring how AI automation could save your team hours every week, let’s talk.

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Cover image for Losing leads because you respond

Losing leads because you respond too slow? Responding within 5 minutes = 9x more likely to close. Most agents take hours. Or never respond at all. I’ll build you an AI Lead Qualification System that works the second someone fills your contact form: •Scores every lead Hot, Warm, or Cold instantly •Sends a personalized follow-up email in seconds #Logs everything to Google Sheets automatically No more guessing who to call first. Your hot leads get instant attention — you just focus on closing. ⏱️ Delivered in 24-48 hours #AIAutomation #n8n #EmailMarketing #LeadGeneration

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Cover image for The Problem
Most real estate agents

The Problem Most real estate agents miss 30% of their calls. A buyer calls at 9 PM. No answer. They call the next agent. That agent closes the deal. Not you. Hiring a receptionist costs $3,000/month. And they still can't work 24/7. The Solution An AI Voice Receptionist that answers every call instantly — day or night — qualifies the buyer and books the appointment directly into your calendar. Automatically. Results 100% call answer rate — zero missed leads Appointments booked overnight while you sleep 10+ hours saved per week on call handling System cost: fraction of a human receptionist Works 24/7 — no sick days, no lunch breaks Before vs After Before: Missed calls, lost leads, manual follow-ups, paying for a receptionist, losing deals after hours. After: Every call answered instantly by AI, every lead qualified automatically, every appointment booked directly to your calendar — while you focus on closing deals. Your competitors are still missing calls. You won't be.

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Cover image for Built a fully automated cold

Built a fully automated cold email outreach system using n8n, Gmail and Google Sheets. The system sends personalized cold emails to leads automatically, follows up with those who don't reply, tracks every response and logs replies directly into Google Sheets — all without any manual work. Results: 100% follow-up rate Replies tracked automatically Zero manual sending Runs 24/7 on autopilot

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sharjeel mansha

Lahore, Pakistan

AI Automation Engineer, AI Agents, CRM & AI Full-Stack Dev.

New to Contra

AI Automation Engineer, AI Agents, CRM & AI Full-Stack Dev.

Cover image for 𝐀𝐈 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 & 𝐂𝐥𝐨𝐮𝐝 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞

𝐀𝐈 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 & 𝐂𝐥𝐨𝐮𝐝 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐀𝐩𝐩 A modern AI automation SaaS concept designed to give businesses one command center for managing AI agents, cloud infrastructure, and automated workflows. The experience combines global server monitoring, real-time AI activity, task breakdowns, and work previews into a clean, intuitive mobile interface. Focus: AI Automation · SaaS · AI Agents · Cloud Infrastructure · Mobile App · Dashboard UI

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Cover image for AI Slack Automation | n8n, OpenAI & CRM Integrations

AI Slack Automation | n8n, OpenAI & CRM Integrations

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2

Cover image for 𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐋𝐞𝐚𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 & 𝐂𝐑𝐌

𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐋𝐞𝐚𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 & 𝐂𝐑𝐌 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐒𝐲𝐬𝐭𝐞𝐦 I built an automated lead generation system designed to simplify the entire sales pipeline from lead capture to follow-up. The system collects leads from multiple sources, processes and qualifies them, adds them directly into the CRM, updates pipeline stages automatically, and triggers personalized follow-up sequences based on lead activity. Instead of managing leads manually across different tools, the entire workflow runs through one connected automation system. 𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐬𝐲𝐬𝐭𝐞𝐦 𝐡𝐚𝐧𝐝𝐥𝐞𝐬: -Lead capture from multiple sources -Lead enrichment and qualification -Automatic CRM entry -Pipeline stage updates -Automated follow-up sequences -Reply and meeting tracking -Opportunity management The goal is simple: reduce manual work, improve response speed, and make sure valuable leads don’t get missed. 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐚 𝐬𝐢𝐦𝐢𝐥𝐚𝐫 𝐥𝐞𝐚𝐝 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐂𝐑𝐌 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐬𝐲𝐬𝐭𝐞𝐦 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬? 𝐋𝐞𝐭’𝐬 𝐜𝐨𝐧𝐧𝐞𝐜𝐭.

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Cover image for 𝐓𝐮𝐫𝐧 𝐎𝐧𝐞 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐈𝐦𝐚𝐠𝐞 𝐈𝐧𝐭𝐨

𝐓𝐮𝐫𝐧 𝐎𝐧𝐞 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐈𝐦𝐚𝐠𝐞 𝐈𝐧𝐭𝐨 𝐚 𝐂𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐔𝐆𝐂 𝐕𝐢𝐝𝐞𝐨 𝐂𝐚𝐦𝐩𝐚𝐢𝐠𝐧 𝐰𝐢𝐭𝐡 𝐀𝐈 Creating multiple product videos manually can take hours. This AI-powered workflow simplifies the entire process from start to finish. Upload a single product image, and the system automatically analyzes the product, creates a complete UGC storyboard, generates multiple creative scenes, sends each scene to Google Flow for AI video generation, and then organizes everything into a final editing workflow. The result is a faster and more scalable way to create product content without repeating the same manual process for every scene. 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰: Product Image → AI Analysis → UGC Storyboard → Scene Generation → Google Flow → Video Clips → Automated Editing Built for brands, eCommerce businesses, agencies, and content teams that need to produce more product videos with less manual work. 𝐖𝐚𝐧𝐭 𝐚 𝐬𝐢𝐦𝐢𝐥𝐚𝐫 𝐀𝐈 𝐯𝐢𝐝𝐞𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐬𝐲𝐬𝐭𝐞𝐦 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬? 𝐋𝐞𝐭’𝐬 𝐛𝐮𝐢𝐥𝐝 𝐢𝐭.

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150

proLahore, Pakistan

AI Agents & Automation That Ship | Voice AI, LLMs, .NET

New to Contra

AI Agents & Automation That Ship | Voice AI, LLMs, .NET

Cover image for Bagallery: 24/7 AI Sales and Support Chat

Bagallery: 24/7 AI Sales and Support Chat

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3

Cover image for Haia: AI Agent for Banks, Crypto and Investments

Haia: AI Agent for Banks, Crypto and Investments

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3

Cover image for 𝗖𝗮𝘀𝗲𝗣𝗮𝘁𝗵 — 𝗔𝗜 𝗖𝗮𝘀𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁

𝗖𝗮𝘀𝗲𝗣𝗮𝘁𝗵 — 𝗔𝗜 𝗖𝗮𝘀𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗳𝗼𝗿 𝗖𝗵𝗶𝗹𝗱 𝗪𝗲𝗹𝗳𝗮𝗿𝗲 & 𝗙𝗮𝗺𝗶𝗹𝘆 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 Recently, I built a modern SaaS case management platform for social work agencies, centered on an AI case-summary engine. The system cut documentation time by 90%, sped up supervisor case decisions by 83%, and added 20% more caseload capacity — with no new hires. Designed to reduce administrative burden on caseworkers so they can focus on families, while giving supervisors faster, clearer visibility into case status and decisions. https://mycasepath.com/

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Cover image for Your support team goes home

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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Lahore, Pakistan

AI Developer | RAG Chatbots, AI Agents & Next.js SaaS

New to Contra

AI Developer | RAG Chatbots, AI Agents & Next.js SaaS

Cover image for Meet AI is a video

Meet AI is a video conferencing SaaS where AI agents join live meetings as active participants and respond in real time. I built the full application stack. The hard part is the real-time layer. The backend provisions an AI agent the moment a meeting starts and keeps it running for the full call. Speech runs through a transcript pipeline and generates responses through the OpenAI API in near real time, so the agent replies while the conversation is still moving, not minutes later. After the meeting, the backend writes a structured summary through the same API, handled async by Inngest so nothing blocks the live app. The stack is Next.js 15 and React 19 with the Stream Video and Stream Chat SDKs, tRPC and Drizzle ORM on Neon PostgreSQL, and Better Auth across the stack. The platform runs 30 simultaneous live sessions, each with its own active AI agent, with no backend slowdown. What the client gets: live AI agents that take part in real meetings, plus automatic summaries, on a stack built to hold many sessions at once.

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Cover image for Nodebase is a workflow automation

Nodebase is a workflow automation SaaS where users build AI workflows visually with no code, backed by a custom execution engine. I built the full product end to end, alone. The visual builder runs on React Flow inside Next.js 15. Users connect trigger nodes, AI nodes, API nodes, and conditional logic into full pipelines. The backend engine uses topological sort to resolve node dependencies before running each step, so nothing fires out of order. Inngest handles background jobs, retries, and scheduled triggers. Webhooks let outside services start a workflow by hitting an endpoint. This is AI automation in practice. OpenAI, Claude, and Gemini run as built-in AI nodes next to Slack, Discord, and Stripe. A user can route data through an LLM, act on the result, and pass it to the next step, all without code. Credentials are encrypted and pulled at runtime, so keys never sit in plain text. Prisma with Neon PostgreSQL handles workflow ownership, execution history, and job logs. tRPC connects frontend and backend with full type safety. What the client gets: a no-code platform where non-technical staff build their own AI workflows, on an engine reliable enough to run them on a schedule without supervision.

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Cover image for Echo is a multi-tenant SaaS

Echo is a multi-tenant SaaS for AI customer support with realtime chat, an AI voice agent, and AI automation. I was the primary developer on the full build, frontend, backend, and the AI layer. The AI is the core. It runs OpenAI, Claude, Gemini, and Grok through one multi-model setup, so a client can switch providers without a rewrite. A RAG pipeline connected to a vector database grounds every answer in the client's own content, so the chatbot never returns generic output. VAPI powers the voice agent, so customers can speak to support on a live call. Each tenant gets its own AI agent built on its own documents. The stack is Next.js 15 and React 19 inside a Turborepo monorepo, with separate apps for the dashboard, the embeddable chat widget, and backend services. Realtime chat runs on Convex. Clerk handles auth. API keys are encrypted per tenant through AWS Secrets Manager, so no two clients share credentials or data. Launch day held 60 live conversations at once with zero dropped sessions. What the client gets: an AI chatbot and voice agent that answer from their own content, work across multiple LLM providers, and stay isolated and secure per tenant.:

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Cover image for I designed and built a

I designed and built a Sentry-style error tracking SaaS from scratch as the Next.js developer and AI integration developer behind it, to prove out multi-tenant architecture and AI integration. Admins manage sub-users with per-project access control. Each admin attaches their own OpenAI, Claude, or Gemini key, encrypted with AES-256-GCM, and picks a model per project for AI fix suggestions. I built two SDKs (Node.js and browser, with a React error boundary) plus a CLI that uploads source maps on build. Errors resolve to original source code, not minified output, the same technique Sentry uses.

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Muhammad Adrees

Lahore, Pakistan

AI Engineer | LLM Workflows & Agents for B2B SaaS

New to Contra

AI Engineer | LLM Workflows & Agents for B2B SaaS

Cover image for Email Intake Automation — Stips,

Email Intake Automation — Stips, Sorted Without a Human Touching Them The problem (any MCA ops person knows this pain)💯 Every funding application generates a flood of supporting documents ("stips") likd bank statements, tax returns, voided checks, business licenses, driver's licenses, landlord references, lease agreements, articles of incorporation, and dozens more. They arrive over days or weeks via email, attached as PDFs, JPGs, screenshots, and forwarded chains. They're named things like "scan_2.pdf" or "IMG_4847.jpeg". Someone on the ops team has to: download every attachment, figure out which application it belongs to, identify what kind of document it is, rename it properly, place it in the right folder in the case management system, and update the application checklist. For shops funding 50+ deals a week, this single workflow eats hours of operational labor every day. What I built🔥 - An intake pipeline that watches a shared inbox, pulls every attachment, identifies which application each one belongs to, classifies what type of stip it is, renames it to the firm's naming convention, drops it into the correct location, and updates the application's stip checklist automatically. - By the time a human looks at the case, it's organized. Outcomes🙌 - Stips routed and labeled without manual intervention - Application checklists update themselves - Funder/processor time freed up for actual review work - Reduced lost-document errors during back-and-forth with ISOs - Faster turnaround from "submission" to "ready for underwriting" Stack🧠 Python, IMAP/email processing, AI classification, async pipelines. Best fit for👍 MCA funders, ISOs, lenders, and any back-office operation that receives documents by email and burns hours sorting them. If your ops team manually downloads, renames, and routes attachments, this solves that.

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Cover image for AI Bookkeeping Platform — Books

AI Bookkeeping Platform — Books That Maintain Themselves🔥 The problem💯 Small business owners don't want to think about bookkeeping. They want to know if the business is healthy, whether they can afford a new hire, and what their tax bill will look like. But getting answers from their books still requires effort, opening the app, navigating menus, running reports, interpreting numbers, and fixing categorization mistakes along the way. The tools have improved, but the workflow hasn't really changed, users still drive the software. What I built🙌 - A bookkeeping platform where the AI agent drives the workflow, not the user. - Instead of opening a dashboard and clicking through menus, users just ask. "How's revenue this month?" "Did I pay the AWS bill yet?" "Categorize the Amazon charge as Supplies and remember it." - The agent answers in plain language, takes real actions inside the books, creates rules from corrections, and only asks the user when something genuinely needs their input. The interface becomes the conversation. Outcomes🙌 - Users get answers without learning the app - Transactions get categorized and corrections become rules - Receipts auto-match to transactions - Reports update continuously, available on request - Bookkeeping shifts from a weekly chore to a quick chat Stack🧠 Python, GCP, event-driven architecture, LLM tooling, React, TypeScript. Best fit for👍 Fintech and SMB finance products, bookkeeping services going AI-native, accounting platforms exploring agent-first interfaces, or any product where the next product surface is a conversation, not a dashboard.

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Cover image for AI Agents & Workflow Automation

AI Agents & Workflow Automation — LLMs That Take Real Actions, Not Just Chat🔥 The problem💯 Most "AI agents" on the market are still chatbots wearing a costume. They can answer questions, summarize docs, and sound convincing but they can't actually do anything inside a real system. They don't update records. They don't call APIs. They don't make decisions that matter. When teams try to put them into production workflows, they either hallucinate, break under edge cases, or require so much guard railing that the agent becomes slower than the manual process it was supposed to replace. What I built🔥 - Production AI agents that operate inside real workflows, taking structured actions, calling internal tools and APIs, working through multi-step decisions, and knowing when to ask a human. - The agents handle the operational work teams actually want automated: querying data and answering follow-ups, classifying and routing incoming items, updating records, generating reports on request, running multi-step workflows that combine several tools, and recovering gracefully when something doesn't fit the expected pattern. Outcomes🙌 - Agents that take real actions, not just generate text - Tool using LLMs with structured outputs and validation layers - Multi-step workflows that complete reliably end-to-end - Audit logs so every agent action is reviewable - Human escalation built in agents know what they shouldn't decide - Evaluation suites so improvements can be measured, not guessed at Stack🧠 Python, LLM orchestration tooling, async pipelines, structured outputs, evaluation frameworks. Best fit for👍 SaaS products adding in-app AI assistants, ops teams automating internal workflows, fintech and back-office products with repetitive decision work, and any team that's tried building agents and watched them fall apart in production.

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Cover image for AI Bookkeeping Platform — Books That Maintain Themselves🔥

AI Bookkeeping Platform — Books That Maintain Themselves🔥

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maxLahore, Pakistan

Full-Stack Dev (Web, Mobile, Desktop) & Chromium Browser Dev

8x: Hired

5.0: Rating

161: Followers

Full-Stack Dev (Web, Mobile, Desktop) & Chromium Browser Dev

Cover image for Custom Web Developer | Ai Platform Development for RozmeriGPT

Custom Web Developer | Ai Platform Development for RozmeriGPT

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Cover image for Chrome Extension Developer | RozmeriGPT Smart LinkedIn Assistant

Chrome Extension Developer | RozmeriGPT Smart LinkedIn Assistant

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Cover image for Full-Stack Developer | Chromium Expert | Chrome Extension

Full-Stack Developer | Chromium Expert | Chrome Extension

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Cover image for Chromium Browser Development for enterprise

Chromium Browser Development for enterprise

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