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 their AI stack. With Studio, ML engineers and even backend developers can fine-tune compact models (7B-14B parameters) on product-specific data — support tickets, user-generated content, domain taxonomies, and internal knowledge bases — producing models that nail your use cases without prompt acrobatics. The result is a model that speaks your product language natively, not one that needs three paragraphs of system prompt to approximate it.

Ertas Cloud turns those fine-tuned models into production-ready inference endpoints with autoscaling, canary deployments, and latency SLOs — the same operational patterns your team already uses for traditional microservices. Because you own the model weights, there are no surprise deprecations, no rate limits beyond your own infrastructure capacity, and per-request costs drop dramatically compared to commercial API pricing. Your AI features become as reliable and predictable as any other service in your architecture.

Key Features

Example Workflow

A B2B SaaS company building project management software wants to add an AI feature that auto-categorizes incoming tasks by team, priority, and project area. The product team exports 200,000 historically categorized tasks as a JSONL training set and uploads it to Vault. In Studio, an ML engineer selects a Mistral-7B base from Hub, configures a LoRA fine-tuning run targeting the classification task, and kicks off training. The resulting adapter achieves 94% accuracy on a held-out test set — up from 71% with the best prompt-engineered GPT-4 approach. The model is deployed to Cloud as a low-latency endpoint behind the application's API gateway, handling 500 requests per second with p99 latency under 200ms. The team ships the feature in their next release, with inference costs 8x lower than their previous third-party API setup.