Ertas for SaaS Product Teams

The Challenge

SaaS product teams are under enormous pressure to integrate AI into their applications: investors expect it, customers demand it, and competitors are shipping it. But the reality of building with generic foundation models is frustrating. Prompt engineering gets you 80% of the way there, then you spend months chasing the last 20% with increasingly brittle system prompts, few-shot examples, and retrieval pipelines that still hallucinate on your domain-specific terminology.

The operational burden compounds quickly. API rate limits throttle your throughput during peak hours, per-token pricing makes unit economics unpredictable, and every model provider deprecation cycle threatens to break features in production. Product managers can't roadmap around a dependency they don't control, and engineering teams waste sprint after sprint adapting to upstream changes instead of building differentiated product value.

The Solution

Ertas lets product engineering teams take ownership of the models behind their AI features. In Ertas Studio, ML engineers and backend developers fine-tune compact open models, from under 1B up to 8B parameters, on product-specific data: support tickets, user-generated content, domain taxonomies and internal knowledge bases. Upload a JSONL dataset, import one from Hugging Face, or build it in Data Craft, where product and domain experts review and approve examples. Training runs on Ertas's cloud GPUs through a visual canvas, and several runs can go in parallel so the team can compare base models and settings. The result is a model that already speaks the product's language, without three paragraphs of system prompt to approximate it.

The trained model is yours. Download it from Hub as full safetensors for vLLM or another server runtime, or as GGUF for llama.cpp and Ollama, and run it as a service in your own infrastructure with the health checks, autoscaling and dashboards your team already uses. Because you hold the model, an upstream deprecation can no longer break the feature, throughput depends on your own capacity, and usage costs become more predictable than per-token API pricing. Teams that would like Ertas to run it can ask about the adaptive model endpoint: one API for one task, where a custom model trained on your own requests and your team's feedback takes over a growing share of requests once it passes your evals, and hard cases stay on the frontier model you already use.

Key Features

Example Workflow

A B2B SaaS company building project management software wants an AI feature that auto-categorizes incoming tasks by team, priority and project area. The product team exports 200,000 historically categorized tasks, removes customer names and personal details from the free text, and formats them as a JSONL training set, holding back a few thousand tasks as a test set. In Ertas Studio, an ML engineer sets up LoRA fine-tuning runs on Qwen 2.5 3B and Mistral 7B and trains both in parallel. They download each model and score it on the held-out tasks, alongside the prompt-engineered API approach the team prototyped with. The stronger model is exported as safetensors and deployed on vLLM behind the application's API gateway, where the SRE team watches its latency and error rates next to every other service. The team ships the feature in its next release and retrains each quarter as new teams and project areas appear.