Ertas
Ertas.ai is a visual platform that lets app builders fine-tune open-source LLMs without writing code, then export them as GGUF files for on-device deployment. Users upload datasets, train on cloud GPUs, and ship models that run offline via llama.cpp, eliminating per-inference API costs. The platform reports 94% domain task accuracy versus 71% for GPT-4 prompting, with a free plan and paid tiers starting at $10/month.
- Artificial Intelligence
- AI Agents
- Developer Tools
- Software Only
Funding
Founders
Product
Problem
Cloud-based AI APIs create scaling problems for mobile app developers: per-token costs grow linearly with user count, network round-trips add latency and fail offline, and user data leaves the device for third-party servers. Developers also face deprecation risk when API providers retire models, leaving apps broken on someone else's timeline.
Solution
Ertas.ai provides a visual training canvas that lets app builders fine-tune open-source language models without writing Python or managing infrastructure. Users upload JSONL datasets or import from Hugging Face, select a base model from a curated catalog, and configure training recipes visually. The platform handles the training pipeline on cloud GPUs and offers one-click export to GGUF, the open format compatible with llama.cpp, Ollama, and LM Studio. The resulting models run entirely on-device, delivering instant inference with zero per-token cost, full offline capability, and privacy by architecture since user data never leaves the phone.
Target Audience
Primary customers are indie developers, startup founders, agencies, and solo builders shipping AI-powered mobile or desktop applications who need custom models without ML engineering resources.
Features
- Visual training canvas with drag-and-drop dataset upload, base model picker, and recipe configuration—no code or ML expertise required
- Curated model catalog spanning Llama, Mistral, Phi, Gemma, Qwen, and LFM families with parameter counts from 230M to 22B+ and license badges for commercial-use decisions
- Bring-your-own-model support via Hugging Face URL validation for models outside the catalog
- One-click GGUF export producing open-format files compatible with llama.cpp, Ollama, and LM Studio, with no proprietary lock-in
- Two GPU tiers—T4 (16 GB VRAM) for models under 5B parameters and A10G (24 GB VRAM) for larger models with longer contexts
- Data Craft guided dataset builder for creating training data from scratch when no dataset exists
- Domain-specific fine-tuning that achieves 94% task accuracy versus 71% with GPT-4 prompt engineering, per the company's benchmarks