What if AI could help a technical interviewer prepare for an interview before the candidate even joins the call?
At Evrone, we built ArgusHire – an AI assistant that analyzes a CV against the project requirements and helps prepare an interview plan, including the topics and questions worth focusing on.
After the interview, ArgusHire analyzes the transcript, evaluates the candidate’s answers, and helps compare multiple candidates. It also analyzes the interviewer’s performance – how thoroughly the planned topics were covered and how closely the conversation followed the original plan.
Now, a bit about the engineering behind it.
The stack includes Ruby on Rails 8, Sidekiq + Redis, PostgreSQL with JSONB, and Next.js 16 / React 19 / TypeScript. Long-running AI tasks run in the background, so operations like CV and transcript analysis don’t block regular HTTP requests.
The AI layer is built with ActiveAgent and a custom model-calling pipeline. The business logic isn’t tied to a specific LLM API: out of the box, the system works with GigaChat-2-Max and deepseek-v4-pro, while the OpenAI-compatible API makes it possible to connect cloud services, corporate AI gateways, or locally deployed models via vLLM, LocalAI, and Ollama.
For each AI task, we store information about the model and the provider used to run it. This makes it possible to compare results and experiment with different models.
Another important component is custom prompts. Each workspace can have its own prompt templates for CV analysis, interview preparation, transcript analysis, and
competency assessment – with versioning, and without code changes or a new release.
And this isn’t an experiment in a vacuum: we use ArgusHire in real technical interviews at Evrone, compare its results with assessments from our technical specialists, and use the findings to refine the rules and prompts.
Learn more about the product and its architecture: https://evrone.com/blog/argushire
At Evrone, we built ArgusHire – an AI assistant that analyzes a CV against the project requirements and helps prepare an interview plan, including the topics and questions worth focusing on.
After the interview, ArgusHire analyzes the transcript, evaluates the candidate’s answers, and helps compare multiple candidates. It also analyzes the interviewer’s performance – how thoroughly the planned topics were covered and how closely the conversation followed the original plan.
Now, a bit about the engineering behind it.
The stack includes Ruby on Rails 8, Sidekiq + Redis, PostgreSQL with JSONB, and Next.js 16 / React 19 / TypeScript. Long-running AI tasks run in the background, so operations like CV and transcript analysis don’t block regular HTTP requests.
The AI layer is built with ActiveAgent and a custom model-calling pipeline. The business logic isn’t tied to a specific LLM API: out of the box, the system works with GigaChat-2-Max and deepseek-v4-pro, while the OpenAI-compatible API makes it possible to connect cloud services, corporate AI gateways, or locally deployed models via vLLM, LocalAI, and Ollama.
For each AI task, we store information about the model and the provider used to run it. This makes it possible to compare results and experiment with different models.
Another important component is custom prompts. Each workspace can have its own prompt templates for CV analysis, interview preparation, transcript analysis, and
competency assessment – with versioning, and without code changes or a new release.
And this isn’t an experiment in a vacuum: we use ArgusHire in real technical interviews at Evrone, compare its results with assessments from our technical specialists, and use the findings to refine the rules and prompts.
Learn more about the product and its architecture: https://evrone.com/blog/argushire