Conlatus is an AI-powered screening interview system designed to handle the repetitive first stage of candidate evaluation.
Initial screening is repetitive, time-consuming, and often follows the same basic evaluation pattern for every candidate.
Recruiters repeatedly ask similar questions across large candidate pools.
Valuable human attention gets spent on predictable first-stage conversations.
Structured evaluation can be difficult to maintain across many interviews.
Conlatus connects the candidate experience, interview logic, and evaluation workflow into a single system.
Conlatus separates the candidate experience from the interview and evaluation logic, creating a foundation that can evolve beyond a simple chatbot.
Conlatus is being developed as a working engineering project rather than a static design concept.
Clone the repository, create your Python environment, configure the Groq API key, and start the FastAPI backend.
Conlatus currently runs as a Python backend application. The setup is intentionally simple: create an isolated environment, install the dependencies, configure your API key, and launch the development server.
.env file or API
keys to GitHub.
Download the Conlatus source code and move into the project directory.
git clone https://github.com/conlatus/conlatus.git
cd conlatusCreate an isolated Python environment for Conlatus and activate it before installing dependencies.
python -m venv .venv
.venv\Scripts\activatepython -m venv .venv
source .venv/bin/activateInstall the Python packages required by the Conlatus backend.
pip install -r requirements.txtCreate your local environment file from the provided example and add your Groq API key.
cp .env.example .envGROQ_API_KEY=your_groq_api_keyLaunch the FastAPI development server using Uvicorn.
uvicorn backend.main:app --reloadConlatus is an evolving open-source engineering project. Explore the implementation, architecture, and development process.
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