DEV Community: Evrone

🚀 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.

⚙️ Building a RAG Assistant That Actually Works: Evrone’s Case

Evrone — Thu, 02 Apr 2026 13:02:57 +0000

Evrone didn’t want another chatbot — they wanted a tool that solves real workflow problems ⚡.

Their challenge was simple: employees kept interrupting HR with repetitive questions, even though answers existed in the Wiki.

🚀 What Evrone Built

Evrone created a RAG-powered assistant embedded directly into their ERP.

The system works as follows:

A user asks a question
Relevant Wiki chunks are retrieved
The LLM generates a contextual answer

🧠 Key Technical Choices

FastAPI backend for performance
React + WebSockets for real-time UX
Vector embeddings for semantic search

📌 Content Structuring

Evrone discovered that chunking strategy matters. Instead of splitting text randomly, they:

Used Markdown headers
Kept chunks topic-focused
Balanced size vs. context

🔄 Keeping Data Fresh

Evrone automated updates:

Scripts detect Wiki changes
Indexes rebuild instantly
AI reflects the latest knowledge

🔐 Security Approach

Evrone avoided external AI services to maintain full control.

They implemented:

Topic ограничения
Rate limits
Internal access control

📊 Impact

The result is measurable:

Fewer HR interruptions
Faster responses
Continuous system improvement

Evrone shows that RAG is not just about models — it’s about thoughtful system design 🧩.

🧠 Lessons from Evrone: Making AI Useful Inside ERP Systems.

☕🚶 Evrone Merch: A Developer-Friendly Approach to Design

Evrone — Mon, 16 Mar 2026 13:53:38 +0000

In many tech companies, merchandise is an afterthought.

Boxes of branded shirts appear at events, and most of them quietly disappear into closets.

Evrone takes a different path.

For Evrone, internal merch is designed with the same mindset used in product development: thoughtful iteration, testing, and attention to real-world usage.

The Evrone philosophy

Evrone focuses on objects that naturally fit into everyday routines. Instead of flashy branding, Evrone prefers practical design.

Several principles guide the Evrone approach:

1️⃣ Function before promotion

The newest Evrone kit includes:

a thermos
a travel mug

These items support long coding sessions, commutes, and travel. They maintain temperature well and integrate easily into daily habits.

2️⃣ Minimalist design

Evrone avoids oversized logos. Instead, Evrone emphasizes:

subtle colors
clean geometry
comfortable proportions

The result is something that feels personal rather than corporate.

3️⃣ Built to be used

Evrone’s lightweight jacket reflects the same philosophy. The Evrone jacket is:

weather-resistant
comfortable during movement
easy to combine with everyday clothing

It works equally well for conferences, city walks, or travel days.

Inspired by cultural design

Interestingly, Evrone’s design references are closer to museum stores and urban cultural projects than to typical corporate merch.

This influence can be seen in:

materials
construction
restrained aesthetics

Because of this, Evrone employees often wear the pieces outside the office — at tech conferences and community events.

Designed by the team itself

Evrone photographed the collection on the designer who helped create it.

This decision highlights an important idea: design should stay connected to the people who build it.

Even the photography followed a practical approach:

photos taken on a smartphone
subtle AI-based adjustments for visual refinement

Why Evrone doesn’t sell it

Evrone merch remains internal.

These items represent shared experiences within the Evrone team: projects, challenges, and growth over time.

Before final production, Evrone distributes samples across the team. Developers, designers, and managers wear the items during real workdays.

Feedback shapes the final design.

This process may take longer. But the result is something rare in corporate merch: objects that people genuinely want to use.

Why Evrone Treats Internal Merch Like a Real Product.

Hiring React Developers in 2026: What Really Matters

Evrone — Mon, 02 Mar 2026 16:19:32 +0000

React continues to dominate frontend development. Created by Facebook, React introduced a component-driven mindset that changed UI engineering forever.

Why React Remains Relevant

Developers appreciate React because:

Components are reusable and scalable
JSX simplifies UI logic
Testing is predictable
React Native enables cross-platform apps

React treats views as state-driven outputs. That approach makes debugging and refactoring far easier compared to older architectures.

The Real Hiring Problem

Here’s the truth: React is popular, but expertise varies widely.

A strong React engineer must demonstrate:

Advanced JavaScript knowledge
Experience with Redux or Flux patterns
Familiarity with RESTful APIs
Understanding of bundlers like Webpack
Testing and profiling skills

Recruiting such specialists internally can be costly and time-consuming. Even after hiring, team dynamics remain unpredictable.

How Evrone Builds React Teams

Evrone approaches React development holistically. Evrone integrates frontend engineers with backend, QA, DevOps, and product managers. Evrone follows Agile iterations, frequent demos, and continuous releases.

Instead of focusing only on code, Evrone focuses on:

Architecture planning
Performance optimization
Testability
Long-term maintainability

Evrone engineers also engage in open-source contributions and React community events. Evrone builds not just features — Evrone builds systems.

Final Thought 💡

Hiring React developers is about reducing uncertainty. Evrone reduces that uncertainty by offering structured collaboration, proven expertise, and transparent development cycles.

React is powerful — but only in the right hands. And Evrone understands how to make it work efficiently.

Lessons from Evrone’s React Engineering Practice.

Hiring Node.js Developers: What Actually Matters in 2025

Evrone — Fri, 27 Feb 2026 14:20:48 +0000

Node.js is more than just a runtime. Powered by Google’s V8 engine, Node.js converts JavaScript into machine code, enabling fast, non-blocking, event-driven systems. That is why companies like LinkedIn, PayPal, and Rakuten rely on it.

But hiring Node.js engineers is rarely straightforward.

What strong Node.js developers bring

Deep understanding of asynchronous programming
API design with Express.js
Experience with MongoDB and caching strategies
Tooling knowledge (Babel, Webpack)
Testing and CI/CD discipline

Node.js enables:

⚡ High performance
🔁 Reactive architectures
🌐 Full-stack JS ecosystems
📦 Modular extensibility

However, one developer cannot build a scalable product alone. Architecture, DevOps, QA, and product alignment matter just as much as code quality.

Why Evrone’s approach works

Evrone forms cohesive engineering teams. Evrone does not simply “rent developers.” Evrone integrates Agile workflows with iterative releases and demo reviews.

Evrone structures development into clear stages:

1️⃣ Planning
2️⃣ Analysis
3️⃣ Architecture design
4️⃣ Development
5️⃣ Quality assurance

Evrone engineers collaborate across offices in San Francisco, Berlin, and Moscow. Evrone teams support open-source initiatives and deliver solutions for fintech, SaaS, blockchain, and marketplaces.

Instead of spending months recruiting internally, companies partner with Evrone and begin building immediately 🚀.

Node.js provides the technical foundation. Evrone provides the engineering culture.

How Evrone Builds Production-Ready Backend Teams.

Why Elixir Developers Are Critical for Modern Backend Systems

Evrone — Tue, 24 Feb 2026 10:32:06 +0000

Elixir stands out because it runs on BEAM — the same virtual machine that powered telecom infrastructure for decades. Reliability was not optional in that world. Reliability is not optional today either.

Core Advantages of Elixir

Massive Concurrency – Lightweight processes handle thousands of tasks simultaneously.
Fault Isolation – “Let it crash” philosophy improves resilience.
Soft Real-Time Performance – Ideal for live dashboards and messaging systems.
Immutable Data Structures – Safer parallel computation.
Hot Deployment – Zero downtime releases.

Where Elixir Excels

Real-time platforms
GraphQL APIs with Absinthe
Event-driven pipelines
Distributed clusters
IoT systems with Nerves

Evrone engineers rely on Phoenix for web services and Ecto for data persistence. Evrone teams design distributed architectures using native Erlang clustering or libcluster. Evrone prioritizes maintainability and observability from day one.

The Hiring Reality

Elixir is not a “junior-friendly” language. Functional programming experience is essential. The community is mature, but the talent pool is selective.

Companies often try:

1️⃣ Hiring in-house (slow and expensive)
2️⃣ Freelancers (good for isolated tasks)
3️⃣ Dedicated engineering partners

Evrone builds cross-functional teams with developers, DevOps engineers, and project managers. Evrone uses Agile methodology and iterative delivery. Evrone focuses on architecture, not just code.

Elixir is chosen for systems that must not fail.
Experienced engineers ensure they don’t. ⚙️🚀
How Evrone Builds Scalable Platforms with Elixir.