Engineering Notes: The Hygge Software Development Blog
Short pieces on the decisions that come up before a build starts: what data readiness means, where model accuracy goes after launch, when RPA beats an agent, what a portal should cover first. Every post on this software development blog comes from a project that shipped, written by the engineer who made the call.
Insights
How to Build a Real-Time Video Analytics System: Architecture, Latency and Cost
Learn how to build real time video analytics software for live camera feeds. Explore architecture, latency, infrastructure costs, and real-time video analytics for safety and industrial use
Yevhenii Sukhov·
How to Choose a Software Development Partner for a Startup
Learn how to choose the right software development partner for your startup. Compare expertise, delivery models, costs, and risks to find a reliable software development partner that can scale.
Alina Sukhova·
AI MVP Development Roadmap: Scope, Team, Timeline and Budget
How to scope, staff, schedule and budget an AI MVP in 2026: what it must prove, which strategy fits, the five stage process, team, timeline and where the money goes.
Yevhenii Sukhov·
IoT in the Retail Industry: A Guide to Applications and Connected Stores
What connected devices can report in a retail store, which use cases pay for themselves, how device events reach inventory and POS, and how to measure a pilot before rolling it out.
Yevhenii Sukhov·
How POS Integration Connects Your Retail Operations
How a point of sale connects to the rest of a retail business: what each system needs from one sale, which method fits, how to map products and tax, and the problems that appear after launch.
Yevhenii Sukhov·
How to Plan B2B Ecommerce ERP Integration Around Your Sales Process
How to scope an ERP and B2B storefront integration: which system owns each object, how to pick between a connector, a platform and custom work, what must sync live, and the failure cases to test before launch.
Yevhenii Sukhov·
How to Build an AI Product: From Business Idea to Production
A practical guide to the AI product development process: validation, data readiness, architecture, team, timeline, cost and what breaks between prototype and production.
Yevhenii Sukhov·From Scattered Sources to One Reliable Layer
Tell us what systems you run and what question you cannot answer today. You get a source inventory, a scope, and a price.
Tell Us What's Running Unmonitored
Share the models, the stack, and where deployment or monitoring is still manual, whatever's costing your team time right now.
Get a First Consultation
We map your current models and infrastructure for anything that would change scope, cost, or timeline.
Receive a Detailed Proposal
A scoped plan with the approach, timeline, and cost, built around your actual stack.