# ALTAI Blog

> Models, agents and the engineering behind ALTAI.

- [From 2.2 GB to 2.5 MB: Building the Fastest Turkish Sentence Embeddings with Model2Vec and TurboQuant](https://altai.dev/blog/turkish-sentence-embeddings-model2vec-turboquant/index.html.md) (2026-08-31): How we distilled a 24-layer multilingual transformer into a 2.5 MB static embedding model that runs 800x faster.
- [OpenSimula examples walkthrough](https://altai.dev/blog/simula-example/index.html.md) (2026-04-29): A guided explanation of the OpenSimula pieces used in examples/simula.
- [Convert your raw corpus to SFT data: A walkthrough with AfterImage to generate legal research conversations](https://altai.dev/blog/caselaw-rag-example/index.html.md) (2026-04-28): A guided explanation of the AfterImage pieces used in examples/caselaw_rag/generate.py.
- [AfterImage is now open source for infrastructure-level dataset generation](https://altai.dev/blog/afterimage-open-source/index.html.md) (2026-04-13): A Python library and CLI for synthetic conversational datasets — grounded, diverse, and observable. Today we are releasing AfterImage as open source.
- [Building a Small yet Highly Capable LLM-as-a-Judge: Fine-Tuning Gemma 3 4B for Evaluation Tasks](https://altai.dev/blog/llm-as-a-judge-gemma/index.html.md) (2025-11-20): Open-source language models have made incredible progress in reasoning and instruction following — yet they still struggle with one crucial skill: evaluation.
- [Fine-Tuning Is Not Dead: How Synthetic QA Fine-Tuning Makes RAG Smarter](https://altai.dev/blog/synthetic-qa-fine-tuning-rag/index.html.md) (2025-11-17): At Altai, we are frequently asked one question — why bother fine-tuning when you can just use RAG? Here is the answer.
- [Small Models, Big Impact: Why Altai's SLMs Outperform LLMs for Business Needs](https://altai.dev/blog/small-models-big-impact/index.html.md) (2025-07-03): Businesses are eager to harness AI — but the biggest, most expensive models aren't always the right answer. Here's why smaller, domain-specific models win.
- [Launching Altai: Unlocking Enterprise AI with Customization & Open Source](https://altai.dev/blog/launching-altai/index.html.md) (2025-05-29): Enterprises are eager to leverage LLMs but adoption is hard. Here's why we built Altai — and what we're doing differently.
- [Distilling Efficiency: Experiments in Compressing BAAI/bge-m3 using a Synthetic Dataset](https://altai.dev/blog/distilling-bge-m3/index.html.md) (2025-05-16): The rapid progress in NLP is mainly due to large neural network models — but size comes at a cost. Here's our approach to compressing bge-m3 with synthetic data.
- [Are you tired of getting only garbage instead of Markdown from PDFs? Yeah, same.](https://altai.dev/blog/llm-food-pdf-to-markdown/index.html.md) (2025-05-16): That's why we built llm-food — a FastAPI-based service that converts documents and URLs into clean, LLM-friendly Markdown with batch processing support.
- [Introducing AfterImage: Custom Vision Models Without the ML Overhead](https://altai.dev/blog/introducing-afterimage/index.html.md) (2025-04-01): Training a custom image classifier used to mean weeks of setup. AfterImage changes that — bring your data, get your model.
- [Why Most Enterprise AI Projects Fail (And How to Fix It)](https://altai.dev/blog/why-enterprise-ai-fails/index.html.md) (2025-03-01): According to Gartner, over 85% of AI projects never make it to production. After talking to hundreds of enterprise teams, the reasons are almost always the same.

Source: https://altai.dev/blog/