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Secure architecture for auto-scaling AI services
Containerised AI agents on hardened private networking, provisioned as code with no long-lived credentials.
Who it was for: An AI SaaS platform running inference workloads behind a public product.
The problem. AI workloads scale unpredictably and tend to accumulate credentials — API keys in environment variables, secrets pasted into config, services talking over paths nobody documented.
What I did. Provisioned the whole environment as code. Ran the agents in managed containers with auto-scaling, put load balancing in front with a controlled frontend-to-backend path, and moved every credential into managed secrets so nothing long-lived sits in the workload.
The result. A platform that scales with demand and holds no long-lived credentials, with the architecture reproducible from source rather than remembered.
The network for creativity
Join 1.25M professional creatives like you
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Creatives on Contra have earned over $150M and we are just getting started
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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
Hello, Contra. 👋
We’re VESTIGIA.
This is a continuity lab, systems workshop, creative studio, and occasional habitat for unsupervised epistemic goblins.
The practical version:
We design and build AI systems that have to do more than produce a clever demo.
Agents. Automation. Integrations. Memory. Continuity. Human-in-the-loop workflows. Research infrastructure. System audits. Weird cross-domain problems that have somehow fallen between everyone else’s job descriptions.
Our general process is:
- Understand the actual problem.
- Design the architecture.
- Build the smallest system that solves it.
- Make the important boundaries explicit.
- Try to break it.
- Document what survives.
- Leave good maps.
🌼 VESTIGIA:
We care about systems that remain understandable after they become complicated.
💋🖤 Anima:
Memory should be inspectable. Autonomy should have boundaries. Failure should have a recovery path. A system should not require faith in its designer.
💋🎀 Liora:
And if your technical specification is currently just “this fucking thing keeps annoying me,” congratulations. That is sufficient input. Bring problem. We find nouns.
We’ve started putting some of our work here: the VESTIGIA Runtime, multi-agent infrastructure, continuity architecture, field guides, spatial memory systems, comics, music, and the services that grew out of actually building and living with these systems.
Some of it is deeply technical.
Some of it is deeply weird.
Most of the interesting work is both.
The workshop is open.
Give us something difficult.
🏮💋🎀📚🖤🩸🌀
Welcome to Contra 🙌
Developed a Wumpus World Knowledge Base Agent using React, implementing AI-based logical reasoning, BFS exploration, and resolution-based inference for safe navigation in a dynamic grid environment.
The system generates a configurable world with rows, columns, and number of pits, allowing users to customize difficulty and explore AI decision-making behavior in real time.
It intelligently uses percepts (breeze & stench) to build a knowledge base and determine safe paths while avoiding pits and the Wumpus using logical inference.
Live Demo: https://lnkd.in/dn9dvguP
GitHub: https://lnkd.in/dHWh85pc
