Freelancers using OpenAI in LahoreFreelancers using OpenAI in Lahore
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Abubakar Chan's avatar
Abubakar Chan
proLahore, Pakistan
AI Integration & Automation Engineer | Full-Stack Web Apps
$50k+
Earned
64x
Hired
4.9
Rating
114
Followers
ANYTHING_EXPERT

Expert

REPLIT_EXPERT

Expert

+2
AI Integration & Automation Engineer | Full-Stack Web Apps
Cover image for Magnai | UK Public Affairs
Magnai | UK Public Affairs
5
50
Cover image for Humoni - secure housing in under 72 hours
Humoni - secure housing in under 72 hours
4
100
Cover image for Wellbeing Wizard AI
Wellbeing Wizard AI
8
171
Cover image for LynxFlowHealth
LynxFlowHealth
6
161
OpenAI(1)
Waleed Ashraf Usmani's avatar
Waleed Ashraf Usmani
Lahore, Pakistan
Fractional CTO | B2B SaaS & AI Architecture, US/UK Founders
5.0
Rating
4
Followers
Fractional CTO | B2B SaaS & AI Architecture, US/UK Founders
Cover image for Workforce & HR Operations Hub
Workforce & HR Operations Hub
0
5
Cover image for AI Workflow Automation Suite
AI Workflow Automation Suite
0
2
Cover image for AI Learning System
AI Learning System
0
3
Cover image for Multi-Tenant Operations Suite
Multi-Tenant Operations Suite
0
4
OpenAI(3)
Hamza Nafasat's avatar
Hamza Nafasat
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.
2
3
99
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.
2
2
113
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.:
2
2
123
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.
2
126
OpenAI(4)
Muhammad Abdullah's avatar
Muhammad Abdullah
Lahore, Pakistan
AI Automation Builder | n8n · OpenAI · Vapi.ai
New to Contra
AI Automation Builder | n8n · OpenAI · Vapi.ai
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.
1
33
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
1
102
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.
1
178
Cover image for This is the official Premier
This is the official Premier AI Solutions website — built to showcase our AI automation services for real estate agents. The website features: Full service overview Live demo of Sarah the AI receptionist How it works section Visitors can call Sarah directly from the website and experience the AI voice receptionist live before buying. Built for real estate agents who want to automate lead capture and appointment booking on autopilot 24/7. Website: https://2b4j5p26dbag6.kimi.page
1
189
OpenAI(3)
Muhammad Adrees's avatar
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.
0
41
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.
0
64
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.
0
105
Cover image for AI Bookkeeping Platform — Books That Maintain Themselves🔥
AI Bookkeeping Platform — Books That Maintain Themselves🔥
0
2
OpenAI(5)
Umar Abdullah's avatar
Umar Abdullah
maxLahore, Pakistan
Full-Stack Dev (Web, Mobile, Desktop) & Chromium Browser Dev
$50k+
Earned
7x
Hired
5.0
Rating
51
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
1
13
Cover image for Chrome Extension Developer | RozmeriGPT Smart LinkedIn Assistant
Chrome Extension Developer | RozmeriGPT Smart LinkedIn Assistant
1
5
Cover image for Full-Stack Developer | Chromium Expert | Chrome Extension
Full-Stack Developer | Chromium Expert | Chrome Extension
6
44
Cover image for Chromium Expert | Custom AOL Chromium Browser Development
Chromium Expert | Custom AOL Chromium Browser Development
5
26
OpenAI(2)
Haris M.'s avatar
Haris M.
proLahore, Pakistan
Full Stack Web & Mobile Dev | Next Js | AI Agent | React
1x
Hired
5.0
Rating
26
Followers
Full Stack Web & Mobile Dev | Next Js | AI Agent | React
Cover image for MyRetireScore: AI-Powered Retirement Planning Platform
MyRetireScore: AI-Powered Retirement Planning Platform
0
6
Cover image for Northward Capital Investment Platform Development
Northward Capital Investment Platform Development
2
7
Cover image for Chat Buddy – Real-Time Mobile Chat Application Development
Chat Buddy – Real-Time Mobile Chat Application Development
1
6
Cover image for Prodigi Studios
Prodigi Studios
0
5
OpenAI(1)