Why generic AI wasn’t enough — and how we built a model that speaks the language of retail.

The Problem with Generic LLMs

Large Language Models like GPT, Claude, and LLaMA are incredibly powerful.
But when we applied them to real e‑commerce workflows, we ran into three recurring problems:

1. Lack of Context – Generic models don’t understand the structure of a BigCommerce product object or the API schema for Shopify inventory updates. 
2. Inconsistent Outputs – Without domain tuning, responses varied in format and sometimes broke workflows. 
3. Shallow Understanding of Retail Nuance – Concepts like margin, SEO keyword density, promotional pricing rules, and SKU variants often needed manual explanation each time. 

For AI agents to work reliably in live merchant stores, “good enough” answers weren’t good enough. We needed a domain‑specific LLM tuned for e‑commerce reasoning and execution.

Our Goal

Build an LLM that could:

• Understand e‑commerce APIs and JSON schema natively 
• Speak in the merchant’s business language (“reduce clearance prices by 15% and update SEO descriptions”) 
• Output results in execution‑ready formats 

Apply safe defaults (e.g., don’t set prices below cost, don’t delete live listings without approval)

Our Training Approach

1. Data Curation

We combined multiple data sources:

• Platform Documentation – BigCommerce, Shopify, Adobe Commerce API specs 
• Historical Merchant Workflows – Anonymised data from staging and test environments 
• E‑Commerce Playbooks – SEO guidelines, inventory management best practices, promotional calendar templates 

Error Logs – Common mistakes and their corrections, to train the model to avoid them

2. Instruction Fine‑Tuning

We trained the model on thousands of instruction‑response pairs that mapped business requests to API calls, JSON payloads, or markdown outputs.
Example:

Instruction:

“Create a 10% off promotion for all products tagged ‘summer‑sale’ in BigCommerce.”

Response:

json

CopyEdit

{

  “name”: “Summer Sale 10% Off”,

  “discount_type”: “percentage”,

  “discount_amount”: 10,

  “applies_to”: {

    “entity”: “category”,

    “ids”: [“summer-sale”]

  }

}

3. Safety Layer Training

We introduced guardrails directly in the model’s reasoning process:

• Check constraints before generating an action (e.g., price floor) 
• Flag high‑impact changes for human approval 

Automatically generate rollback steps

4. Integration Testing

We validated outputs by running them in staging environments for BigCommerce, Shopify, and Adobe Commerce.
If the model’s output caused errors, we fed the failed request back into training with corrections.

Results

Our domain‑specific LLM can now:

• Generate valid API requests for supported platforms with >95% accuracy in testing 
• Interpret natural‑language merchant requests without extra prompt engineering 
• Maintain consistent formatting, enabling fully automated execution by our AI agents
• Reduce need for manual QA by over 70% in agent workflows

Example in Action

Merchant Prompt:

“Check which SKUs in the ‘Organic Cotton Bedding’ category have a conversion rate under 1% in the past 30 days, lower the price by 5%, and update the product description with the keyword ‘luxury sustainable bedding’.”

What the Model Does:

1. Queries GA4 and e‑commerce platform data 
2. Identifies SKUs meeting the criteria 
3. Prepares price update API calls 
4. Generates SEO‑optimised product descriptions
5. Submits for approval or pushes live with full audit log

Why This Matters

This isn’t just about building a smarter chatbot — it’s about building reliable execution intelligence for live commerce.
With a domain‑specific LLM, our agents can:

• Work faster and safer 
• Reduce operational costs 
• Increase merchant confidence in automation

Final Word

Generic AI can write you a paragraph about your products.
A domain‑specific LLM can rewrite 1,000 product descriptions, update prices, launch a campaign, and log every change — all in one pass.

At Vortex IQ, we believe this is the foundation for the AI Agent Operating System for E‑Commerce. And we’re just getting started.