Recruitment Automation: Killing the Manual Salary Transfer
Everyone talks about AI in HR, but getting it to actually work without weird mistakes is harder than it looks. We took one concrete problem and solved it.
The problem
Our ERP pulls data from Huntflow and job boards. Recruiters were losing 2–3 hours daily just moving salary numbers around. Open a resume, hunt for the digits, jump to another system, convert currency, standardize the format, paste it in. And if the data was off, finding the right person for a budget was pure guesswork.
Plus candidates write their expectations in a hundred different ways: "120+ net", "$5k-7k", "looking for 180+", "min 150", or just "open to your range". All had to be normalized by hand.
The fix
Template parsers died immediately – resumes are too messy. We used Qwen with structured output. Feed it a raw resume, get back clean fields: role category, timezone, English level, grade, location, salary.
Huntflow was worse. The text there is full of junk like "200/year" or "$3000". Regex was useless, base LLMs kept messing up. So we grabbed a dataset from old records, annotated it, and fine-tuned YandexGPT 5 Lite with LoRA – trained specifically to get amount and currency right.
The outcome
Huntflow lookups dropped 90%. Data accuracy hit 95%. Recruiters now manually check only 5% of cases – the ones with truly bizarre formatting. Time that used to vanish into copy-paste hell now goes into actual recruiting.
Want the same for your team? We're here to help. Full case here.
Everyone talks about AI in HR, but getting it to actually work without weird mistakes is harder than it looks. We took one concrete problem and solved it.
The problem
Our ERP pulls data from Huntflow and job boards. Recruiters were losing 2–3 hours daily just moving salary numbers around. Open a resume, hunt for the digits, jump to another system, convert currency, standardize the format, paste it in. And if the data was off, finding the right person for a budget was pure guesswork.
Plus candidates write their expectations in a hundred different ways: "120+ net", "$5k-7k", "looking for 180+", "min 150", or just "open to your range". All had to be normalized by hand.
The fix
Template parsers died immediately – resumes are too messy. We used Qwen with structured output. Feed it a raw resume, get back clean fields: role category, timezone, English level, grade, location, salary.
Huntflow was worse. The text there is full of junk like "200/year" or "$3000". Regex was useless, base LLMs kept messing up. So we grabbed a dataset from old records, annotated it, and fine-tuned YandexGPT 5 Lite with LoRA – trained specifically to get amount and currency right.
The outcome
Huntflow lookups dropped 90%. Data accuracy hit 95%. Recruiters now manually check only 5% of cases – the ones with truly bizarre formatting. Time that used to vanish into copy-paste hell now goes into actual recruiting.
Want the same for your team? We're here to help. Full case here.
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Merch can't be great? At Evrone, we're proving otherwise – because we always create Internal merchandise items exactly the way our employees want them: comfortable, stylish, and practical.
This year's set includes a jacket, a thermos, and a travel mug. No flashy branding, no unnecessary details. The focus is on things that fit seamlessly into everyday life: office, commute, meetings, trips. The jacket is lightweight, windproof, water-repellent, and doesn't restrict movement. The thermos and mug simply keep drinks at the right temperature and feel comfortable in your hand.
At conferences, people constantly come up to us, ask where these items are from, and are surprised to learn it's our internal merch – and that it's not for sale.
To get great photos, we did a photoshoot with our designer who was involved in creating the merch. When it's a real team member wearing the items rather than an abstract model, it adds honesty and removes the distance between the object and the process.
The photos look like full-scale product shots – clean lighting, precise angles, attention to detail. But they're based on real items shot on a phone, followed by careful visual refinement using AI tools.
More photos of the merch we're proud of here.
This year's set includes a jacket, a thermos, and a travel mug. No flashy branding, no unnecessary details. The focus is on things that fit seamlessly into everyday life: office, commute, meetings, trips. The jacket is lightweight, windproof, water-repellent, and doesn't restrict movement. The thermos and mug simply keep drinks at the right temperature and feel comfortable in your hand.
At conferences, people constantly come up to us, ask where these items are from, and are surprised to learn it's our internal merch – and that it's not for sale.
To get great photos, we did a photoshoot with our designer who was involved in creating the merch. When it's a real team member wearing the items rather than an abstract model, it adds honesty and removes the distance between the object and the process.
The photos look like full-scale product shots – clean lighting, precise angles, attention to detail. But they're based on real items shot on a phone, followed by careful visual refinement using AI tools.
More photos of the merch we're proud of here.
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When it comes to fintech and crypto, many expect magic or overly complex systems. At Evrone, we take a simpler approach: if a task can be solved with classic tools without compromising quality – that's how we solve it.
The txn.pro project is a perfect example. It's a B2B service for sending and receiving crypto payments with automatic fiat conversion. The client needed a reliable, working product that wouldn't fall apart and could scale easily. So we built a minimum viable product with the right architecture from the start.
The backend is Ruby on Rails. Blockchain and liquidity integrations were moved into separate modules – keeping the core isolated and allowing load to scale independently. We chose a modular monolith: if the project grows, we can split it into microservices seamlessly. Our DevOps engineer planned the infrastructure from day one: DigitalOcean for staging, AWS for production.
The frontend is React with TypeScript, using a DDD approach – split into clear layers so development stays fast over time. For the design, we presented two concepts: one with dense, Excel-like data (familiar to experienced users) and a more creative alternative. The client chose the first – giving txn.pro its confident, industry-standard look.
Even in a dense interface, we kept beginners in mind. We added subtle hints and broke complex scenarios like 2FA setup into steps so users wouldn't get lost. Special attention went to transaction validation – in crypto, mistakes are costly, and the interface should prevent them at every turn.
The project is live, running smoothly, and we continue to provide support.
No uniqueness for the sake of uniqueness. Just a clear tech stack, clean architecture, and no headaches for the client during maintenance. If you need a crypto gateway or solid fintech development – we at Evrone deliver without surprises.
👉 More details.
The txn.pro project is a perfect example. It's a B2B service for sending and receiving crypto payments with automatic fiat conversion. The client needed a reliable, working product that wouldn't fall apart and could scale easily. So we built a minimum viable product with the right architecture from the start.
The backend is Ruby on Rails. Blockchain and liquidity integrations were moved into separate modules – keeping the core isolated and allowing load to scale independently. We chose a modular monolith: if the project grows, we can split it into microservices seamlessly. Our DevOps engineer planned the infrastructure from day one: DigitalOcean for staging, AWS for production.
The frontend is React with TypeScript, using a DDD approach – split into clear layers so development stays fast over time. For the design, we presented two concepts: one with dense, Excel-like data (familiar to experienced users) and a more creative alternative. The client chose the first – giving txn.pro its confident, industry-standard look.
Even in a dense interface, we kept beginners in mind. We added subtle hints and broke complex scenarios like 2FA setup into steps so users wouldn't get lost. Special attention went to transaction validation – in crypto, mistakes are costly, and the interface should prevent them at every turn.
The project is live, running smoothly, and we continue to provide support.
No uniqueness for the sake of uniqueness. Just a clear tech stack, clean architecture, and no headaches for the client during maintenance. If you need a crypto gateway or solid fintech development – we at Evrone deliver without surprises.
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We published our BeRealFan project on Behance. Thought I'd share a few thoughts on how we approached it.
Designing complex digital products is really about working within constraints. With BeRealFan, the tricky part wasn't making it look good – it was building one coherent interface across a bunch of different systems: microservices, payments, streaming, messaging, analytics. Each of them has its own logic. Design is what ties it all together into something that actually feels seamless.
The visuals are based on patterns people already know from social platforms – clear hierarchy, familiar interactions, consistent navigation. But there's also a bit of personality: subtle details and a lighter tone that make it stand out.
End result – a product where design helps manage complexity, not add to it.
More visuals on Behance.
Designing complex digital products is really about working within constraints. With BeRealFan, the tricky part wasn't making it look good – it was building one coherent interface across a bunch of different systems: microservices, payments, streaming, messaging, analytics. Each of them has its own logic. Design is what ties it all together into something that actually feels seamless.
The visuals are based on patterns people already know from social platforms – clear hierarchy, familiar interactions, consistent navigation. But there's also a bit of personality: subtle details and a lighter tone that make it stand out.
End result – a product where design helps manage complexity, not add to it.
More visuals on Behance.
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Invariance is something we love. Sometimes you don't need to create a new product to simplify life and optimize the work of an entire team. That's exactly what we did at Evrone when we improved our time tracking workflow – we took an existing system and customized it to fit our needs. That's how Timesheet came to be.
We use Yandex.Tracker for tracking work hours. The system works well and is convenient, but there were nuances we didn't really like. For example, we wanted not just to log time, but to manage entries, edit them, and see the full picture without unnecessary effort. Wait for Tracker's creators to improve it? Not our style. We simply built our own tool inside it.
We built in everything needed for smooth work:
▪️ Detailed reports on tasks and projects broken down by day, week, and month
▪️ The ability to edit time entries
▪️ Automatic loading of active tasks
▪️ Filters by status, queues, and projects
▪️ Support for two languages and time zones
▪️ Input in minutes by default — it really is simpler
And yes, we know: time tracking issues in Tracker aren't something only our team faces. That's why Timesheet is open source – it's open to everyone. Any team can take the code, customize the logic or interface to fit their own workflows.
For us, this project is an example of how customizing existing tools helps build transparent development processes. Sometimes you don't need to reinvent the wheel at all – you can just tweak it a little.
You can learn more about the project here.
We use Yandex.Tracker for tracking work hours. The system works well and is convenient, but there were nuances we didn't really like. For example, we wanted not just to log time, but to manage entries, edit them, and see the full picture without unnecessary effort. Wait for Tracker's creators to improve it? Not our style. We simply built our own tool inside it.
We built in everything needed for smooth work:
And yes, we know: time tracking issues in Tracker aren't something only our team faces. That's why Timesheet is open source – it's open to everyone. Any team can take the code, customize the logic or interface to fit their own workflows.
For us, this project is an example of how customizing existing tools helps build transparent development processes. Sometimes you don't need to reinvent the wheel at all – you can just tweak it a little.
You can learn more about the project here.
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The cost of a wrong tech choice can be higher than you think. Brag House found out the hard way.
They built a platform🎮 for student gamers – not hardcore pros who play 24/7, but casual players spending just a few hours a week gaming. College tournaments, live commentary, post-match interviews, leaderboards, virtual currency. The niche was wide open, and demand was real.
But their AWS Amplify prototype was holding them back. No room to grow – only to rewrite.🧐
We rebuilt the backend with Python Django. Stack: Python3, Django, Celery, AWS (EC2, RDS, S3), Stripe, Twitch API, Expo Push API. Along the way, we added message notifications and a daily free loot box with virtual currency.
The project secured funding, launched private tournaments, and expanded internationally with major partnerships.🔥
If your project is struggling to scale – we know how to fix it. Let’s talk.
👉 More details – here.
They built a platform
But their AWS Amplify prototype was holding them back. No room to grow – only to rewrite.
We rebuilt the backend with Python Django. Stack: Python3, Django, Celery, AWS (EC2, RDS, S3), Stripe, Twitch API, Expo Push API. Along the way, we added message notifications and a daily free loot box with virtual currency.
The project secured funding, launched private tournaments, and expanded internationally with major partnerships.
If your project is struggling to scale – we know how to fix it. Let’s talk.
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