You will get AI features added to your .NET application
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Project details
You have a working .NET app and you want AI inside it. Not a separate tool, not a prototype. A feature your users actually touch.
I build this. BriefMeet is my own AI transcription SaaS on ASP.NET Core 8, Whisper across 90+ languages, Gemini for action items and transcript chat. WorkForge is my AI agent platform with a model router that logs real token cost per call. I have also built a local first RAG system with Tree-sitter, Qdrant and LlamaIndex.
Inside client systems I have shipped a real time voice agent for a healthcare platform, OpenAI anomaly detection on attendance data in an HR SaaS, and WhatsApp automation with AI assisted replies for a pharmacy platform.
You get the feature merged into your repo, environment config, per call token and cost logging, error handling and fallback when the model fails or rate limits, and a short handover doc.
Most AI work stops at the API call. Retrieval, cost control, and what happens when the model returns nothing are where these break. I handle those.
Model provider API costs are billed to your own account, separate from this price.
I build this. BriefMeet is my own AI transcription SaaS on ASP.NET Core 8, Whisper across 90+ languages, Gemini for action items and transcript chat. WorkForge is my AI agent platform with a model router that logs real token cost per call. I have also built a local first RAG system with Tree-sitter, Qdrant and LlamaIndex.
Inside client systems I have shipped a real time voice agent for a healthcare platform, OpenAI anomaly detection on attendance data in an HR SaaS, and WhatsApp automation with AI assisted replies for a pharmacy platform.
You get the feature merged into your repo, environment config, per call token and cost logging, error handling and fallback when the model fails or rate limits, and a short handover doc.
Most AI work stops at the API call. Retrieval, cost control, and what happens when the model returns nothing are where these break. I handle those.
Model provider API costs are billed to your own account, separate from this price.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI-Generated Code, Anomaly Detection, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, Jasper AIAI Models
GPT-4, LLaMA, OpenAI Codex, WhisperWhat's included
| Service Tiers |
Starter
$450
|
Standard
$1,200
|
Advanced
$2,600
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 18 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | |||
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | - |
Model Documentation | - | - | - |
Model Monitoring | - | - | - |
Model Testing & Optimization | - | - | - |
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | - | - |
Source Code |
Frequently asked questions
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About Nabeel
Senior AI Agent & MCP Engineer | .NET, TypeScript, Python
100%
Job Success
Lahore, Pakistan - 2:19 am local time
What you get:
• Speed: my own AI SaaS went from zero to live and selling in 16 days, built solo
• Less manual work: AI agents and n8n automations that run without a human watching, like an onboarding flow that cut new client setup time by 50 percent
• Production reliability: 231 automated tests and CI health checks stand behind my own product, and your build gets the same standard
Need an AI feature that survives production, or a backend that ships and stays up? That is my work. I work across the full build: architecture, REST and GraphQL APIs, multi-tenant data, payments, real-time, AI/LLM features, cloud, CI/CD. And I do the reliability work most people skip.
Selected production work:
BriefMeet. My own AI meeting transcription SaaS, solo-built and launched in 16 days. It runs Whisper transcription in 90+ languages and Gemini meeting intelligence on ASP.NET Core 8, PostgreSQL, React, and TypeScript. Billing goes through Lemon Squeezy with idempotent webhooks. 231 automated tests, CI/CD with production health checks, and Google indexed it on day one.
RoverDent. Multi-tenant dental and healthcare SaaS built as nine .NET 8 microservices behind a React 18 dashboard. Temporal runs multi-day patient follow-up across calls, SMS, email, and WhatsApp without losing state, and Python FastAPI services power a real-time voice AI agent on LiveKit. Stripe and Twilio run in production. HIPAA-aware audit trails, tenant isolation, RBAC.
VisionONE. Enterprise ERP and payroll on .NET 8, with EF Core for the domain model and Dapper for heavy ledger reads. The Angular 20 frontend uses NgRx and AG Grid for data-dense payroll views. The part I am most proud of is real-money payouts through M-Pesa STK Push, where webhook callbacks and status polling keep payment state correct. Hangfire runs the salary jobs, and TOTP two-factor auth secures access.
What I help clients build:
AI agents and MCP servers: Claude Agent SDK, OpenAI, tool design, human in the loop control, deployed and monitored
Multi-tenant SaaS with RBAC, tenant isolation, and secure architecture
AI and LLM features: OpenAI, RAG (Qdrant, LlamaIndex), voice agents, chatbots, automation with n8n and Temporal
REST and GraphQL APIs, microservices, CQRS, clean architecture
Modern frontends: React, Angular (NgRx, AG Grid), TypeScript, responsive UIs
Payments and billing: Stripe, PayPal, M-Pesa, Authorize.Net, PCI-ready infrastructure, webhooks, signature verification
Real-time features: SignalR, WebSockets, MQTT, event-driven backends
Cloud and DevOps: AWS, Azure, Docker, GitHub Actions, Azure DevOps, CI/CD
Legacy modernization: .NET Framework to .NET 8, desktop to web
Core stack: C#, ASP.NET Core, .NET 8/9, Python, FastAPI, Node.js, React, Angular, Next.js, TypeScript, PostgreSQL, SQL Server, Redis, Cosmos DB, AWS, Azure.
How I work: I learn your codebase deeply, flag risk and tradeoffs early, and give honest timelines with no surprises mid-project. My best work happens on long engagements where reliability matters. Hand me a hard problem and it gets shipped.
Tell me what you are trying to ship and your current stack. I will tell you straight if I am the right fit and how I would start. Happy to do a quick call through Upwork.
Steps for completing your project
After purchasing the project, send requirements so Nabeel can start the project.
Delivery time starts when Nabeel receives requirements from you.
Nabeel works on your project following the steps below.
Revisions may occur after the delivery date.
Codebase review and scope confirmation
I read your repo and requirements, confirm exactly what I will build, and flag anything outside scope before any work starts. If it does not fit this project, I tell you then, not on day three.
Build the feature in a branch
I implement the AI feature in a separate branch on your repo, with per call token and cost logging, and fallback handling for model failures, timeouts, and rate limits.