Freelance AI Agent Orchestrators in Islamabad
Freelance AI Agent Orchestrators in Islamabad
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Armughan Shahid
pro
Islamabad, Pakistan
AI SaaS Dev | LLMs, Agents, Voice & Automation | Web, Mobile
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AI SaaS Dev | LLMs, Agents, Voice & Automation | Web, Mobile
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AI Operations Agent: RAG-Powered Retail Intelligence & Task Automation This project was built for large-scale restaurant groups and multi-unit retail operators who manage high volumes of data across dozens or hundreds of locations. Specifically designed for Regional Managers and Operations Directors, the system serves as an enterprise-grade "Digital Consultant" that bridges the gap between fragmented POS/inventory data and daily on-the-ground execution. By transforming millions of rows of restaurant performance metrics into high-priority tasks, it provides a centralized platform for leadership to monitor KPIs, approve AI-suggested corrective actions, and ensure operational consistency across their entire portfolio. 1. What We Built We developed a production-ready Autonomous AI Operations Agent designed to bridge the gap between complex retail data analysis and daily execution. The system acts as a digital consultant for regional managers, transforming raw KPIs into actionable tasks. Analytical AI Chat: A free-form conversational interface where users can query performance data (e.g., "Show me the top 5 worst profitable stores in Istanbul for the last 3 months"). Task Management Dashboard: A structured workflow where AI-suggested actions are automatically logged for manager approval or rejection. Automated Action Logic: The agent uses an "Action Suggestion Map" to identify specific defects (like low audit scores or high food waste) and suggest precise corrective measures. Persistent Memory: Includes both short-term memory for the current chat session and long-term RAG memory to maintain context over time. 2. How We Built It (The Stack) The system was engineered for scalability and reliability using a modern, containerized stack:AI Orchestration: LangGraph was used to manage complex, multi-turn reasoning and agentic workflows. Frontend: React/Next.js 14 for a responsive, real-time user interface. Backend & Data: Node.js paired with a PostgreSQL database capable of handling 1M+ records. LLM Access: Integrated via OpenRouter to allow for flexible model selection and switching. Infrastructure: Fully Dockerized to ensure consistent deployment across environments. 3. Challenges We Faced As the system scaled from prototype to processing millions of records, we encountered several critical engineering hurdles: Response Latency: The initial monolithic prompt architecture led to response times exceeding 60 seconds, far slower than the required "ChatGPT-like" speed. Prompt Verbosity & Errors: Complex questions involving multiple variables caused the LLM to lose focus, leading to "reasoning errors" and incorrect SQL generation. Hallucination Risks: In multi-branch queries, the model occasionally fabricated data points, particularly around manager hours and performance metrics. Context Switching Bugs: The agent sometimes struggled to "let go" of a previous topic, continuing to reference an old store when the user had asked about a new city. 4. How We Solved It We re-engineered the core pipeline to transition from a single, heavy agent into a Modular Multi-Step Architecture: 75% Latency Reduction: By decomposing the main logic into smaller, task-specific nodes, we dropped processing time from 60s down to 15s. Task Decomposition & Specialized Models: We stopped using a "one-size-fits-all" model. Instead, we implemented a router that uses lighter, specialized models for SQL generation and action identification, and flagship models only for final reasoning. Granular SQL Generation: Breaking the metadata analysis into narrow sub-steps eliminated SQL hallucinations. The model now only "sees" the specific schema needed for the current sub-task, ensuring 100% accuracy. 10-Point Testing Protocol: We implemented a rigorous QA protocol that specifically verified bug fixes for context switching, task duplication, and chart coverage before final delivery.
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BudgetNest — AI-Powered Personal Finance SaaS Most people don't track their finances because the friction is too high. BudgetNest removes that friction entirely, every transaction captured automatically, categorised intelligently, and surfaced through analytics that actually help people make better decisions. The core problem it solves: Manual expense logging fails because people forget, get lazy, or simply don't have time. BudgetNest built an automated capture layer that works across every channel a user already operates in i.e. SMS alerts, bank emails, receipt photos, WhatsApp messages, and voice notes in English and Urdu. The system deduplicates intelligently across all input sources so nothing gets logged twice regardless of how it came in. What was built: A complete AI finance platform with five distinct automated capture modes SMS and email parsing for bank transaction alerts, PDF and image bank statement upload with AI extraction, OCR receipt scanning via camera, a WhatsApp bot that accepts text, images, and voice notes, and multilingual voice input for manual cash payments. Every transaction flows through an LLM-powered categorisation engine that auto-assigns categories and subcategories, recognises vendors, and learns from behaviour over time. Beyond capture, the system includes smart budgeting with AI-driven suggestions based on spending patterns, subscription detection for recurring transactions, shared expense and split-bill tracking, fraud detection for unusual transactions, and forecasting that projects deficit against income. Dashboards surface everything through charts, trend lines, and weekly and monthly summaries. Technical architecture: React Native across iOS and Android, Node.js and FastAPI backend, PostgreSQL and MongoDB, AWS infrastructure with EC2, S3, and RDS, Python-based NLP and OCR pipeline using Transformers and Tesseract, Twilio WhatsApp integration, Gmail API for email parsing, and Firebase for push notifications. Business model built in from day one: Freemium with premium automation features, B2B white-label capability for microfinance institutions and NGOs, and the OCR and SMS parsing logic architected as standalone APIs for third-party licensing meaning the AI layer has revenue potential independent of the consumer app.
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129
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We Step Together - Step-to-Donation Mobile App
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1
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Development of CarlsbergHub & ParkingHub
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1
AI Agent Orchestrator
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Khakan Hayder
Islamabad, Pakistan
Framer Expert
$1k+
Earned
1x
Hired
5.0
Rating
7
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expert
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Framer Expert
3
Voicecon AI delivers AI Voice Agents and AI Text Chatbots to automate customer support and inbound/outbound calls, driving efficiency and business growth.
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244
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A quick reel of recent client work delivered through VConekt. Each project went from blank canvas to live, conversion-ready Framer site: covering SaaS landing pages, fintech dashboards, AI product launches, local service businesses, and personal portfolios. What I bring to every project: • Official Framer Expert (verified) • Strategy-first design, not just pretty pixels • CMS, animations, responsive across every breakpoint • Clean handoff or full ongoing management Available for new projects starting June. Let's build.
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22
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Diamant Versatile
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2
1
✦ MILESTONE → My first plugin, Framemap, is now live on the official Framer Marketplace. 🔗 https://www.framer.com/marketplace/plugins/framemap/ WHAT FRAMEMAP DOES You describe your project in two sentences. Claude generates a complete responsive wireframe set — mobile (390px), tablet (834px), and desktop (1440px) — and drops it on your Framer canvas as fully editable frames. What used to take an hour of blank-canvas friction now takes seconds. WHAT'S INSIDE v1 → 23 section types — hero, features, bento grid, pricing, testimonials, FAQ, CTA, team, blog, gallery, contact, and more → Multi-breakpoint output in a single click → 4 style presets — SaaS, Agency, E-comm, Minimal → Drag-to-reorder before committing to canvas → Convert any section to native, editable Framer frames → BYO Anthropic API key — keys stay local, generations cost fractions of a cent 🟢 FREE during v1 — v2 will be paid. Anyone reading this can install it today at no cost. That window closes when v2 ships. WHY THIS MATTERS FOR CLIENTS HIRING ME The same depth that goes into a marketplace-reviewed plugin goes into every Framer project I deliver. I don't just drag components — I understand the platform from the API up, including its limits. Marketplace approval is Framer's own quality stamp. WHAT I'M OPEN TO RIGHT NOW → Framer site builds (landing pages, full marketing sites, portfolios) → Custom Framer plugins for your team or product → Product design for SaaS, agencies, and indie founders → Template & system work Currently booking 2–3 slots for the rest of this month. If you've been sitting on a Framer project, this is a good month to start. 📩 khakan@vconekt.com (mailto:khakan@vconekt.com)🌐 vconekt.com (http://vconekt.com)🔗 https://www.framer.com/marketplace/plugins/framemap/
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