Eulogik 🧠
Edge AI Infrastructure & Quantitative Systems Engineering
Website: eulogik.com
GitHub
We engineer hyper-efficient, domain-specialized, and edge-deployable AI models.
No bloated parameter sizes. No massive cloud dependencies. Just high-performance models designed to run locally, securely, and cost-effectively.
🔬 Active Research & Model Portfolios
We focus on three primary domains of edge-optimized machine intelligence:
1. 📄 Visual Document Understanding (VDU)
- TinyDoc-VLM-256M: A compact 290M-parameter vision-language model trained on SigLIP and SmolLM2. Optimized for document OCR, layout parsing, and information extraction.
- TinyDoc-VLM-LoRA: Parameter-efficient adapters for custom corporate legal and administrative document compliance.
2. 📈 Time Series & Quantitative Forecasting
- NanoForecast: A lightweight time-series foundation model family ranging from 200K to 6.5M parameters. Supports streaming RNN inference and zero-shot forecasting, optimized for running directly on edge hardware like Raspberry Pi.
3. 🐡 Intelligent LLM Routing
- fugusashi: An open-source, federated, human-interpretable model router. Learns user preferences and routes requests to the most optimal model, slashing inference costs by up to 70%.
4. 🇮🇳 Localized & Multilingual LLMs
- Bharat-Tiny-LLM: An ultra-compact 240M-parameter language model specialized for Hinglish and Indian cultural context.
⚡ Developer Demos (Spaces)
Test our models directly in your browser:
🏢 Licensing & Enterprise Collaboration
All our core public models are open-sourced under Apache 2.0 or MIT licenses.
For enterprise-grade integration, private custom model training, or commercial licensing of specialized components (such as our high-frequency quant trading predictors and legal document ingestion pipelines), get in touch:
© 2026 Eulogik. Engineered for efficiency.