Custom AI for Product Discovery, Support, and Content at Zero Per-Query Cost
Ertas Studio lets e-commerce teams fine-tune AI models that understand your catalog, brand voice, and customer language — then deploy them on your own infrastructure with no per-token fees eating into your margins.
The Challenges You Face
AI API Costs Scale with Traffic
E-commerce traffic is spiky — product launches, seasonal sales, and viral moments can 10x your query volume overnight. API-based AI costs scale linearly with each interaction, turning your best sales days into your most expensive AI days.
Generic Models Do Not Know Your Catalog
A general-purpose LLM does not understand your product taxonomy, your brand's tone of voice, or the specific attributes that matter in your category. It cannot distinguish between your premium and budget lines or recommend products based on your actual inventory.
Product Content Is a Bottleneck
Writing unique product descriptions, SEO metadata, comparison guides, and marketing copy for thousands of SKUs is a content team's nightmare. Generic AI produces generic content that sounds like every other store.
Customer Support AI Feels Off-Brand
Chatbots powered by generic models give technically accurate but tonally wrong responses. They do not know your return policy nuances, your warranty terms, or the way your brand communicates. Customers notice.
How Ertas Solves This
Ertas Studio lets you fine-tune models on your actual product data, customer interactions, and brand guidelines. Upload examples of ideal product descriptions, support conversations, and search query-to-product mappings. Studio trains a model that speaks your brand's language and understands your catalog's structure.
The trained model exports as a GGUF file that runs on your own servers. Whether it is generating product descriptions, powering a support chatbot, or improving search relevance, every query runs at the cost of compute — not per-token API fees. Your Black Friday traffic spike does not trigger a surprise AI bill.
For e-commerce companies, this means AI features that genuinely reflect your brand, understand your products, and scale with your business without scaling your costs.
Key Features for E-Commerce Companies
Catalog-Aware Model Training
Fine-tune on your product data — descriptions, attributes, categories, reviews — so the model understands your catalog's structure, terminology, and relationships. It knows the difference between your product lines because it was trained on them.
Brand Voice Preservation
Train on examples of your best content and support interactions. The model learns your tone, vocabulary, and communication style — producing outputs that are on-brand by default, not through elaborate prompt engineering.
Traffic-Proof Deployment
Self-hosted GGUF models handle traffic spikes without cost spikes. Your AI costs are fixed infrastructure costs, not variable per-query fees. Scale your inference capacity by adding servers, not increasing your API budget.
Multi-Use-Case Models
Train separate models for different functions — product description generation, support chatbot, search enhancement — all from the same Studio workspace. Each model is optimized for its specific task rather than being a jack-of-all-trades.
Why It Works
- E-commerce companies switching from API-based AI to self-hosted fine-tuned models have eliminated per-query costs entirely, making AI-powered search and recommendations economically viable at any traffic level.
- Fine-tuned product description models produce catalog copy that matches brand voice guidelines without manual editing, reducing content production time by 70% or more.
- Support chatbots trained on actual resolution data answer policy-specific questions accurately, reducing escalation rates compared to generic LLM-powered alternatives.
- Self-hosted models handle Black Friday traffic volumes without cost increases, eliminating the trade-off between AI feature availability and cost management during peak periods.
- Catalog-aware models improve search relevance by understanding product relationships, attribute importance, and category hierarchies specific to the merchant's inventory.
Example Workflow
An online outdoor equipment retailer needs to generate unique product descriptions for 3,000 new SKUs. The content team exports their 500 best existing descriptions as examples, formats them as input-output pairs in JSONL, and uploads the dataset to Ertas Studio. They select a 7B model and start training.
Twenty minutes later, they test the model in the playground with product specifications from the new SKUs. The generated descriptions nail the brand's adventurous tone and correctly highlight the technical attributes that outdoor enthusiasts care about. The team exports the GGUF, deploys it on their existing API server, and builds a simple internal tool that generates draft descriptions from product specs.
The content team reviews and publishes descriptions 5x faster than writing from scratch. The model also powers a customer-facing chatbot that answers product comparison questions using actual catalog knowledge — running on the same server with no additional per-query costs.
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