DEV Community: Evrone

🚀 What It Really Takes to Migrate Kubernetes Without Downtime

Evrone — Thu, 23 Jul 2026 16:42:11 +0000

Cloud migration sounds straightforward until dozens of interconnected services, strict deadlines, and production traffic enter the picture. An international transportation platform needed to replace its cloud provider, migrate every Kubernetes workload, and adopt a new internal engineering platform—all within a single month.

For Evrone, the migration started with understanding the architecture rather than deploying containers. Evrone engineers analyzed every application, reviewed infrastructure configurations, and uncovered dependencies that were not immediately visible in deployment manifests.

🔎 Key preparation steps

Audit every Kubernetes service.
Verify databases, Redis, brokers, and APIs.
Identify hidden infrastructure dependencies.
Compare legacy and new deployment workflows.
Remove migration blockers before production.

Several services required namespace-specific resources that were unavailable in the new environment. Evrone documented every requirement, coordinated with infrastructure teams, and ensured the missing components were prepared before rollout.

⚙️ Standardizing the migration

Instead of handling each application differently, Evrone introduced a repeatable migration pipeline. Every deployment followed identical validation procedures before production traffic could be switched.

✅ Application startup
✅ Health probes
✅ Network connectivity
✅ Database access
✅ Messaging systems
✅ Internal APIs
✅ Monitoring and logging
✅ Rollback readiness

Every production deployment became a collaborative session involving developers, DevOps engineers, and platform specialists. Problems were resolved immediately, reducing delays and eliminating unnecessary communication overhead.

🚦To guarantee zero downtime, old and new service versions ran simultaneously during validation. Traffic moved only after Evrone confirmed stable behavior, healthy dependencies, and successful customer scenarios. Legacy services remained available until the new deployment proved reliable.

By the end of the migration, every service had been successfully transferred to the new cloud infrastructure. Evrone completed the transition on schedule while the transportation platform remained fully operational throughout the project.

☁️ How Evrone Helped Move a Complex Transportation Platform to a New Cloud.

🚀 How Evrone Built a DevOps Management System That Teams Actually Enjoyed Using

Evrone — Tue, 21 Jul 2026 15:54:41 +0000

DevOps environments rarely follow perfectly planned schedules. ⚡ New client requests arrive every hour, engineers support multiple projects at once, and priorities change faster than traditional planning methods can handle.

That was exactly the challenge Evrone addressed while working with a growing DevOps outsourcing company.

Instead of introducing another heavyweight platform, Evrone first analyzed how the teams actually worked. Managers, team leads, and engineers shared their everyday routines, allowing Evrone to identify where work disappeared, where communication slowed down, and where planning failed.

🛠️ Evrone focused on four operational layers:

Daily incoming requests
Weekly sprint planning
Project roadmaps
Knowledge management and reporting

Using Notion, Evrone created a lightweight operational system that connected all of these elements while preserving engineers’ current workflow.

✨ Every incoming request became a structured task with:

Project ownership
Priority
Status
Expected outcome
Sprint connection
Roadmap visibility

Automatic chat integrations ensured that urgent client requests instantly entered the operational queue instead of remaining inside endless conversations.

📊 Daily boards preserved flexibility, while weekly sprints introduced realistic forecasting for infrastructure improvements, recurring maintenance, and deployment work.

Project cards became centralized knowledge hubs containing documentation, responsibilities, client contacts, architecture notes, onboarding videos, and active risks.

📈 Results

✅ Daily meetings reduced by 75%
✅ Client dissatisfaction dropped dramatically
✅ Managers gained full workload visibility
✅ Engineers missed fewer requests
✅ Reporting became part of everyday operations instead of extra administrative work

Rather than replacing existing habits, Evrone designed a management system that reflected how DevOps teams naturally operate—making planning more predictable while preserving the flexibility clients expect.

⚙️ From Daily Chaos to Predictable DevOps Operations with Evrone.

How Evrone Uses Multiple AI Models to Automate Video Script Creation 🚀

Evrone — Mon, 20 Jul 2026 10:59:17 +0000

Many teams believe AI can generate text, but Evrone wanted something much more practical: an assistant capable of accelerating the entire content preparation process without replacing creative professionals.

Publishing several videos every week meant handling research, news monitoring, interview preparation, and long-form scripts. Manual workflows simply couldn't keep pace.

Instead of relying on one large language model, Evrone developed Screenwriter, an internal system where multiple AI models collaborate.

🔹 The workflow

1. Audience understanding

AI builds behavioral profiles instead of relying only on web analytics.
Every future topic is evaluated against audience interests.

2. Information filtering

Thousands of multilingual articles and news updates are analyzed.
Relevant stories receive automatic priority.

3. Script preparation

Selected topics become structured podcast or news scripts within minutes.

💡 Why multiple models?

Different models excel at different tasks.

✅ Information analysis
✅ Narrative organization
✅ Script writing

Using several providers also prevents production from stopping if one service introduces limitations or becomes unavailable.

📈 The outcome

Screenwriter dramatically reduced preparation time while lowering production costs. Teams could publish significantly more episodes without expanding editorial staff.

However, Evrone emphasizes an important principle:

AI supports creators—it does not replace them.

Editors and presenters still review every script, contribute industry expertise, and shape the final story.

For Evrone, the biggest innovation wasn't simply generating text. It was creating a scalable workflow where AI handles repetitive research and organization, allowing people to focus on creativity, expertise, and meaningful communication.

💡 Building an AI Content Pipeline: Lessons from Evrone's Screenwriter.

🛠️ What Evrone Learned While Modernizing a Growing Kubernetes Infrastructure

Evrone — Thu, 16 Jul 2026 15:16:34 +0000

Growth often exposes hidden technical debt. That was exactly the situation facing Native Commerce, whose expanding B2B e-commerce platform required both faster feature delivery and more reliable infrastructure.

Rather than replacing the existing platform, Evrone focused on modernization.

Why the infrastructure became a bottleneck

⚠️ Manual server provisioning.
⚠️ Legacy software versions.
⚠️ No Infrastructure as Code.
⚠️ Shared testing environments.
⚠️ Limited backend development capacity.

Every deployment required additional effort, while testing queues slowed releases.

How Evrone addressed the problem

1️⃣ Infrastructure as Code

Evrone introduced Terraform and transformed infrastructure management into a Git-driven workflow. Configuration became transparent, repeatable, and much easier to maintain.

2️⃣ Dynamic environments

The Evrone engineers updated Kubernetes routing and enabled temporary environments for every labeled branch. Developers could now validate features independently instead of waiting for shared QA environments. 🚀

3️⃣ Emergency response

When regional outages affected UAE availability zones, Evrone migrated the complete production environment to India in under twenty hours. Existing Terraform automation dramatically reduced recovery time.

4️⃣ Network optimization

Evrone improved connectivity between distributed data centers across Europe and Asia while introducing continuous network monitoring for better operational visibility.

Results

✅ Infrastructure expenses dropped by almost 40%.
✅ Deployments became predictable.
✅ QA bottlenecks disappeared.
✅ Engineering productivity increased.
✅ Additional Evrone backend developers accelerated client integrations.

For Evrone, this project demonstrated that infrastructure modernization does not always require replacing existing systems. Thoughtful automation, Infrastructure as Code, Kubernetes improvements, and close collaboration can unlock faster product delivery while helping engineering teams focus on innovation instead of maintenance. 🚀

🚀 Scaling Faster Without Rebuilding Everything: The Native Commerce Story.

🤖 AI Agents in 2026: Building Smarter Business Workflows with Evrone

Evrone — Mon, 13 Jul 2026 11:20:30 +0000

Artificial intelligence is entering a new development phase. Companies are moving beyond AI assistants that only answer questions and toward AI agents that can analyze goals, use external tools, connect with systems, and complete multi-step workflows.

At Evrone, we see agentic AI as one of the biggest transformations in modern software engineering. Businesses are no longer asking only “How can AI generate content?” They are asking “How can AI help our teams complete real processes faster and more efficiently?” ⚡

The market for AI agent platforms is growing quickly, but every solution has a different purpose.

Here are some of the most important platforms businesses are exploring in 2026:

1️⃣ OpenAI Agents
OpenAI Agents provide flexibility for developers building custom AI workflows. They can connect with APIs, databases, and internal tools. However, reliable production systems still require strong engineering, security, and monitoring.

2️⃣ Claude Agents
Claude Agents are designed for complex knowledge tasks, including research, technical documentation, and large volumes of enterprise information.

3️⃣ Microsoft Copilot Studio Agents
These agents are especially valuable for organizations already using Microsoft 365, Teams, and Dynamics because they integrate naturally into existing workflows.

4️⃣ Salesforce Agentforce
Sales and customer service teams can benefit from AI agents that work directly with CRM data and customer processes.

5️⃣ Google Gemini Agents
Gemini Agents bring AI closer to enterprise data platforms, supporting analytics, research, and document-based workflows.

6️⃣ UiPath, CrewAI, AutoGen, ServiceNow, and Relevance AI
These platforms support different business scenarios — from robotic process automation and enterprise operations to multi-agent systems and no-code AI workflows.

Evrone believes that selecting an AI agent should not start with the question: “Which model is the smartest?”

The better question is:

“Which business process can create the most value when automated?” 🚀

Successful AI adoption depends on three important factors:

✅ Integration with existing systems
✅ Strong governance and security controls
✅ Clear business outcomes

Modern AI solutions often combine several models and agents, allowing each component to handle the tasks it performs best.

At Evrone, our experience with AI development shows that the future belongs to carefully designed AI ecosystems — not isolated tools. The companies that succeed will be those that transform AI experiments into reliable solutions that improve everyday operations.

🚀 From Automation Scripts to AI Teams: How Agentic AI Is Changing Software Development.

🚀 When HR Software Stops Scaling: What Evrone Learned from Building Its Own ERP

Evrone — Fri, 26 Jun 2026 11:03:00 +0000

Growing companies often expect an HR platform to solve every operational challenge. In reality, standard HRMS products work well for common tasks, but business growth usually exposes gaps that no configuration can fully eliminate.

Evrone reached this conclusion after building its own ERP to support hundreds of employees before developing HR solutions for enterprise customers. That internal experience became the foundation for creating scalable HR ecosystems rather than isolated applications.

Why do companies outgrow standard HR software?

As organizations expand, HR becomes tightly connected with many other business areas:

📊 Resource planning and project allocation.
💰 Payroll models and financial operations.
👥 Recruitment and talent pipelines.
📈 Performance reviews and career development.
🔐 Security, permissions, and compliance.

At this stage, choosing an HRMS is no longer about selecting a user interface. Companies need an architecture that connects HR with CRM, ERP, accounting, BI, messengers, authentication services, and project management tools.

Where AI creates real value

Evrone follows a practical approach instead of chasing automation trends.

Examples include:

🤖 AI assistants that search internal knowledge bases using RAG.
📄 LLM pipelines that normalize candidate salary expectations.
⏱️ Automated timesheet processing for project teams.

Each feature focuses on structured, verifiable tasks where people remain in control of important decisions.

When custom development becomes the right choice

Custom HR software delivers the greatest value when companies require:

multiple legal entities;
complex approval chains;
project-based staffing;
deep integrations;
advanced analytics;
strict security policies.

Instead of replacing every existing system, Evrone designs HR ecosystems where each module has a clear responsibility while sharing reliable data across the organization.

The biggest lesson from Evrone's journey is simple: technology should adapt to business processes—not the other way around. ⭐

🤖 Custom HR Software, AI, and Architecture: Lessons from Evrone's Experience.

🚀 Building Private AI Agents: Why MCP, Skills, and Context Engineering Matter

Evrone — Thu, 25 Jun 2026 15:27:19 +0000

Many companies believe that deploying a local LLM automatically gives them an enterprise AI assistant.

In reality, the model is only the first layer.

A language model cannot access today's emails, inspect calendars, search internal systems, or execute business workflows from its training data alone. To perform those tasks, it needs controlled access to external tools.

MCP Servers Turn Models Into Agents

At Evrone, MCP servers play a central role in connecting AI systems with real-world services.

Typical integrations include:

Email platforms
Calendar systems
Internal databases
Search services
Corporate applications

The LLM decides which tool should be used, while the MCP server performs the requested operation and returns structured results.

Why Custom Integrations Matter

Every integration introduces security considerations.

Custom MCP servers provide:

Fine-grained permissions
Data minimization
Auditable actions
Explicit approval workflows

For example, reading emails may be allowed automatically, while deleting messages or sending replies may require user confirmation.

Skills Reduce Variability

One of the biggest challenges in agent development is inconsistent behavior.

Skills help standardize execution.

A skill can define:

Inputs
Procedures
Tool usage rules
Validation steps
Safety requirements

When a request matches a skill, the model follows an established workflow rather than improvising a solution.

Context Engineering Is a Core Discipline

A powerful model can still fail when overloaded with irrelevant information.

An AI agent must process:

User conversations
Tool outputs
Documents
System instructions
Historical interactions

The objective is not to provide more context.

The objective is to provide the right context.

Evrone treats context engineering as a critical engineering discipline because every token inside the context window competes for the model's attention.

Security Requires Multiple Layers

Private deployment protects prompts from external LLM providers, but other risks remain:

Excessive permissions
Prompt injection
Tool injection
External data leakage
Supply-chain vulnerabilities

A governance layer between the model and external services helps enforce policies, approvals, logging, and risk controls.

🚀 The Real Goal

Successful AI projects are not measured by model size.

They are measured by whether the agent can participate safely and predictably in actual business processes.

That is why Evrone combines private infrastructure, MCP architecture, skills, context engineering, and security controls to build AI agents that organizations can genuinely trust.

🔧 Beyond Local LLMs: How Evrone Designs Production-Ready AI Agents.

🚀 Vibe Coding Gets You to MVP — Engineering Gets You to Scale

Evrone — Wed, 03 Jun 2026 11:40:59 +0000

The rise of AI coding assistants has changed software development forever. Founders can now launch prototypes at a speed that seemed impossible only a few years ago. A few prompts can generate interfaces, APIs, database models, and even complete applications.

At Evrone, we view this shift as an opportunity rather than a threat.

Vibe coding has a clear purpose: rapid validation.

Instead of investing months into development, teams can quickly answer important questions:

Does the market need this product?
Will users engage with the solution?
Is the idea worth pursuing?
Can investors see potential?

For these goals, AI is incredibly effective. 💡

The challenge appears after validation succeeds.

As products gain users, technical complexity grows. Features interact with one another. Security requirements increase. Performance becomes important. Infrastructure costs begin to matter.

This is where many AI-generated projects start to struggle.

Evrone frequently reviews MVPs that were assembled using AI tools. Although the applications appear functional, deeper analysis often reveals structural problems:

Common issues

Business logic scattered across multiple layers.
Weak separation between frontend and backend responsibilities.
Database schemas that limit future growth.
Missing test coverage.
Security vulnerabilities hidden inside generated code.
Manual deployment processes.

⚠️ None of these problems are obvious during a demo.

AI excels at producing code that looks correct. Production systems, however, require much more than correct syntax.

Professional developers use AI differently.

Rather than delegating ownership, they use AI as a force multiplier:

Generating boilerplate code.
Creating initial implementation drafts.
Exploring technical alternatives.
Accelerating debugging workflows.
Producing documentation.

Human engineers still make architectural decisions, evaluate trade-offs, review security implications, and ensure long-term maintainability.

What happens when an AI-built MVP needs to grow?

Evrone typically starts with a technical audit.

The team evaluates:

Architecture
Infrastructure
Data models
Authentication systems
Module boundaries
Scalability risks

Sometimes incremental refactoring is enough. Sometimes a full rewrite is the most economical solution.

The key lesson is simple.

✨ AI helps determine what to build.

⚙️ Engineering determines how to build it for the future.

Companies that combine rapid AI-driven experimentation with experienced engineering teams gain the best of both worlds: speed today and scalability tomorrow.

🤖 AI Wrote the Code. Engineers Still Build the Product.

🧠 How Evrone Built Mimir: A Telegram AI Assistant for Real Business Tasks

Evrone — Fri, 29 May 2026 11:57:05 +0000

Many AI projects begin with curiosity. Employees test prompts, rewrite emails, summarize documents, or generate ideas. Yet most of these experiments stay personal and rarely become part of company workflows.

Evrone approached the problem differently. Instead of creating another isolated AI dashboard, Evrone developed Mimir — an AI assistant built with OpenClaw and integrated directly into Telegram. 🚀

The goal was simple: place AI where communication already happens.

Telegram became the perfect environment because teams already use it for:

• discussions;
• task coordination;
• sharing files and links;
• quick approvals;
• reminders and follow-ups.

Rather than forcing employees to switch platforms, Evrone embedded AI into existing habits. This made adoption significantly easier.

📌 Mimir supports daily workflows by:

searching for information;
drafting and editing texts;
summarizing conversations;
tracking reminders;
helping automate routine operations.

Originally, Mimir was a personal assistant created by Evrone CEO Oleg Balbekov. The assistant helped organize notes, ideas, and content workflows. Over time, Evrone’s marketing and SMM specialists began using the tool as well. The company quickly realized the assistant could support multiple departments at once.

⚙️ Technically, Mimir is far more than a simple bot. OpenClaw provides heartbeat mechanisms that allow the assistant to monitor tasks, preserve context between sessions, and proactively interact with users.

Evrone also designed the system with practical infrastructure in mind:

• lightweight hosting;
• flexible AI model usage;
• controllable monthly budgets;
• secure deployment options.

Another important direction involves automation with Python. Mimir can generate helper scripts, process data, monitor events, and automate repetitive operations. This transforms the assistant from a conversational tool into an active workflow participant. ✨

For teams, the biggest advantage is continuity. Important agreements, reminders, and discussions no longer disappear inside long chat histories.

Evrone now applies the experience from Mimir to build AI assistants for clients in:

• marketing;
• ERP systems;
• analytics;
• support;
• sales;
• internal operations.

The company believes the strongest AI assistants will not simply provide answers. They will become natural parts of communication and collaboration.

⚡ From Chatbot to Workflow Partner: Evrone’s Mimir Story.

🛠️ Building a Production-Ready Private AI Stack with Evrone

Evrone — Sun, 26 Apr 2026 12:36:13 +0000

Running LLMs inside your own infrastructure sounds simple—until real workloads begin. Evrone recently worked on a project where the goal was clear: build a private AI assistant that never depends on external APIs.

The client needed a secure assistant that could:

Understand natural language
Run agent workflows
Integrate with internal systems
Operate in isolated environments

Infrastructure First

This type of system needs serious hardware. In the main setup, Evrone used enterprise-grade GPU servers designed for stable inference, not demo workloads.

Still, Evrone also proved that smaller focused deployments can run on compact hardware for lighter tasks.

Why the Software Layer Is Harder

Many teams focus only on GPUs. Evrone focused on the full stack:

Kubernetes orchestration
Deployment pipelines
Monitoring
Runtime tuning
Model compatibility

The open-source ecosystem remains fragmented. Formats like Safetensors, GGUF, and MLX each serve different environments. No runtime solves every case perfectly.

Testing Models and Runtimes

Evrone benchmarked multiple options, including:

vLLM
Ollama
llama.cpp
mistral-rs
SGLang

After testing, Qwen became the best balance of quality and speed. SGLang became the practical runtime because it supported mixed model formats.

Some configurations reached only 20 tokens/sec. That number may seem fine, but multi-step agents quickly feel slow. Evrone optimized the production setup to roughly 160 tokens/sec.

Production Result

The system now runs live with:

GitOps workflows
Argo CD delivery
Reproducible infrastructure
Secure internal deployment

Final Thought

Private AI is no longer theory. Evrone demonstrated that on-prem LLM systems can become dependable business infrastructure when architecture matters as much as the model itself. 🔐

💡 Evrone’s next.dj Makes Django Feel Modern Again

Evrone — Wed, 22 Apr 2026 16:09:49 +0000

Django remains one of the most respected Python frameworks, but many developers know the hidden cost of productivity: repetitive setup. Evrone looked at that pain point and released next.dj, an open-source framework that rethinks how Django applications are structured.

The project started when Evrone engineers were building a side product. Even small features required repeated actions:

Create a view
Register a URL
Add templates
Connect forms
Handle validation
Maintain multiple UI states

That process works, but it slows iteration. Evrone wanted a cleaner workflow.

🚀 The Core Idea

next.dj introduces file-based routing. Instead of editing routing files manually, developers create page files and routes appear automatically. Templates and logic live together, making projects easier to understand.

This means developers can spend more time building features and less time wiring infrastructure.

🧩 What next.dj Includes

File-based routing
DJX templates
Built-in forms with CSRF protection
Context management
Dependency injection
Reusable components with props and slots

🎯 Who Benefits

Evrone designed next.dj for teams that build:

SaaS products
Internal dashboards
MVPs
Admin systems
Python-first web apps

Teams that prefer Python over splitting everything into backend + React may especially enjoy it.

🔮 Why Evrone’s Experiment Matters

Many open-source tools begin from frustration. That frustration often reflects a real industry need. Evrone transformed daily Django pain into a framework that feels modern without abandoning Django’s strengths.

next.dj is not trying to replace Django. It is trying to make Django smoother, faster, and more enjoyable. That makes the project worth watching for any Python engineer.

🐍 next.dj by Evrone: Less Boilerplate, More Building.

🎼 How Evrone Scaled a Streaming Platform with AI + Go

Evrone — Fri, 17 Apr 2026 11:33:20 +0000

Scaling a streaming platform means far more than adding new songs. A modern product must process millions of user actions, generate relevant recommendations, deliver fast search results, and control infrastructure costs. That was the challenge Evrone helped solve for a large streaming platform with 75M+ tracks.

The service already combined music streaming, podcasts, audiobooks, offline listening, recognition tools, and voice assistant integrations. Evrone joined internal teams to improve both product metrics and engineering efficiency.

🚀 Where Evrone Added Value

1. Recommendation Systems

Evrone ML engineers improved models that understand user behavior. Great recommendations depend on more than genre labels. Systems also analyze:

repeated plays
skips after a few seconds
time of day
content type
current mood signals

This created more relevant playlists and better discovery.

2. Retention Without Dark Patterns

Instead of pushing aggressive notifications, Evrone supported habit-based personalization. Users could receive:

🎵 a gym playlist
🎙️ a commute podcast
📚 an evening audiobook

3. LLM Search and SEO

Many tracks share identical names. Traditional search often struggles in that environment. Evrone improved internal search with contextual LLM logic. Evrone also supported automated SEO content for artist and release pages.

🛠 Engineering Work

The platform had legacy services consuming too many resources. Evrone modernized critical systems by rewriting heavy components in Go while preserving expected behavior.

Additional upgrades included:

splitting monoliths into services
migrating analytics tools from Rails to Python
reducing compute waste
improving maintainability

🤖 AI for Developers

Evrone also helped build internal assistants that can:

run endpoint tests
generate reports
update docs
assist code reviews

📈 Outcome

✔ 20–30% lower costs
✔ 20% faster performance
✔ improved retention
✔ faster development cycles

Evrone proved that smart engineering can drive growth just as effectively as marketing.

⚙️ Evrone Improved Music Tech Behind the Scenes.