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I shipped my own website kmzhasan.com — Infrastructure Architecture Matrix is live.
Stop leaking confidential company data to public AI tools.
Most teams are paying Public LLM API Tax: Data leaves VPC → US servers, 30d retention, you rent intelligence.
I build Sovereign Private VPC instead:
100% Inside your Cloudflare VPC, 0KB egress proof. You own the factory.
What we build inside your private cloud:
→ Private AI search engines
→ Automated digital workforces
→ Secure database connections
Stack: vLLM Llama 3.1 70B • Qdrant • MCP • Cloudflare Zero Trust • Local PII Scrubbing
Why high-compliance enterprises are abandoning OpenAI/Anthropic wrappers for 100% self-hosted:
Public API = Data Boundary broken, 30-day retention on their servers.
Sovereign AI Infra (kmzhasan.com) = 100% Inside your Cloudflare VPC, immutable audit logs, EU AI Act ready.
Check Architecture → Workforce → Pricing on kmzhasan.com
#SovereignAI #PrivateLLM #vLLM #Qdrant #MCP #Cloudflare #ZeroTrust #AIInfrastructure #RAG #AISecurity
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LEAKLENS - revenue-risk dashboard
What it does
LeakLens is a revenue-risk dashboard that turns raw billing and usage data into a live account health view, flagging which customers are quietly drifting toward churn before it shows up in MRR.
The problem it solves
According to MGI Research, 42 percent of businesses experience revenue leakage, and SMB-focused SaaS companies face annual churn as high as 31 to 58 percent, often because billing and usage signals sit in disconnected systems no one is watching in real time. LeakLens closes that gap by turning scattered data into one clear risk view, before the cancellation happens.
How I built it
Built entirely in Bubble, using Bubble AI to scaffold the initial page layout and design system, then hand-refined the data model, workflows, and logic. The core architecture mirrors patterns from tools like Stripe (billing event ingestion) and Amplitude (usage event tracking). Two workflows handle CSV upload and parsing, feed the data into a unified account record, and trigger the dashboard build once both sources are loaded.
How to use or test it
There are two ways to get a live dashboard running:
Demo mode: Click Create Dashboard, then click Load Demo Data. Once the confirmation reads "Demo Data Loaded," click Build Dashboard.
File data: Upload the billing CSV and usage CSV (Download CSV here), click Upload CSV. Once it reads "Billing and Usage Data Loaded," click Build Dashboard.
Click on the account in Dashboard to get more details and recommendation on it.
What's next
LeakLens is currently an MVP, built to prove the core risk-detection logic end to end. The roadmap includes:
Live integrations with Stripe and Amplitude, replacing manual CSV uploads with real-time billing and usage data feeds
An AI response layer that turns each risk flag into action: drafting a personalized outreach email, suggesting a check-in call, or pinging the account's CSM directly, based on what the risk signal actually indicates.
Thank You!
😍 #BuiltWithBubble #NoCode #RevOps #ChurnPrevention
Impressive! Works very well...Wish you the best.
MyCarrier: DevOps & Cloud Transformation for Scalable Logistics SaaS
Preview description:
We helped MyCarrier move toward cloud-ready DevOps: adapted the system for microservices, implemented CI/CD and test automation, moved workloads to Azure, achieved 99.9% availability, decreased update delivery time to minutes.
Full description:
Problem
MyCarrier was growing fast, and the monolithic codebase became harder and more expensive to maintain. It was heavy, resource-consuming, and less suitable for the client’s long-term scaling plans. The team also needed faster delivery processes, but changes in the codebase and cloud migration had to stay unnoticed by users. Their logistics workflows could not be interrupted.
Solution
IT Craft aligned the project infrastructure with a microservices architecture and prepared the system for cloud operation. The team started with a technical audit and developed the migration strategy consisting of 20-28 items. The process was divided into pre-migration, migration, and post-migration phases, thus the product could keep running while infrastructure changed step by step.
The team moved non-critical standalone jobs to the cloud, connected virtual machines to Azure Storage Account, moved logging to MongoDB Atlas, and transferred user data and related processes to the cloud. New app functionality was released as microservices. IT Craft also designed and fine-tuned a CI/CD pipeline, automated infrastructure deployments, and reduced update delivery time through regression test automation.
Results
· Achieved 99.9% system availability.
· Maintained high fault tolerance using three shared clusters.
· Reduced update delivery time from hours to minutes.
· Enabled Sprint focus changes every two weeks based on client priorities.
Tech Stack:
· Azure Cloud
· MongoDB Atlas
· Azure SQL
· Azure App Service
· Azure Functions
· Azure Storage Account
AgentFlow10 — AI Agent & Automation Platform
Lead Engineer · Self-hosted AI agent platform with a visual workflow builder
The problem
Automation tools are built for businesses that already have a card on file, reliable bandwidth, and a Slack workspace. That leaves most of the world out. In markets where WhatsApp is the customer channel and mobile money is the payment rail, the standard automation stack either doesn't connect or is priced in currencies that make it a non-starter.
There was a second problem. Every serious automation platform is someone else's cloud. If your data is sensitive, or your compliance rules keep it in-country, or you just don't want to hand your customer records to a vendor, your options run out fast.
What I built
AgentFlow10 is an AI agent and automation platform you can run on your own infrastructure. A visual builder lets you compose agents and multi-step workflows without writing code, connect them to the tools you already use, and deploy them where your data lives.
The design goal was to make AI agents something a small team can actually operate — not a research project requiring an ML engineer on staff.
What I did
I built and run it end to end: product direction, backend architecture, LLM orchestration, integration layer, the workflow engine, frontend, brand system, and go-to-market. Everything from the database schema to the landing page copy.
Notable pieces of engineering:
A workflow execution engine that handles branching, retries, and failure states — because an agent that dies silently mid-run is worse than no agent
LLM orchestration with prompt management and output validation, so results are structured and usable rather than free text
A self-hosting path that a non-specialist can actually deploy
Integration architecture designed for channels that matter outside Silicon Valley, including WhatsApp and mobile money flows
Why it matters for clients
This is the project I point to when someone asks whether I can build AI systems rather than just call an API. Running a platform means I've dealt with the parts that break in production — cost control, rate limits, non-deterministic model output, failure recovery. That experience is what I bring to client work.
Built with: Python, TypeScript, React, Next.js, Node.js, PostgreSQL, LLM APIs, Docker
