LLM Integration for Commerce Operations
Claude, GPT-4, and Gemini connected to your Square catalog, order data, and customer history. AI-powered search, product descriptions, recommendations, and support — in production.
Four Commerce Problems LLMs Solve Better Than Rules
A customer searches "gift for a runner who hates socks." Keyword search returns nothing. An LLM-powered search layer understands intent and returns the right products. Connects directly to your Square catalog.
Feed your Square catalog to an LLM and get back a quality score for every product: missing images, weak descriptions, duplicate titles, SEO gaps. Fixes are suggested, not just flagged.
A Claude-powered support agent trained on your product catalog, return policy, and order history. Handles tier-1 support without a human — escalates to staff when confidence is low.
The Integration Stack
LLM Selection (Claude / GPT-4 / Gemini)
We select the model based on the use case: Claude for long-context catalog work, GPT-4 for function-calling agents, Gemini for multimodal product image analysis. Most projects use multiple models for different tasks.
Square Catalog as the Knowledge Base
Your Square product catalog is the source of truth. We build retrieval pipelines that keep the LLM context current as your catalog changes — no stale product data in AI responses.
Vector Search Layer (pgvector / Pinecone)
Semantic search requires embeddings. We build the vector pipeline: catalog ingestion, embedding generation, similarity search. Results are re-ranked by an LLM before returning to the customer.
GCP Vertex AI or Anthropic API
Inference runs on GCP Vertex AI (for Gemini) or directly against Anthropic/OpenAI APIs. All calls are logged, cost-monitored, and rate-limited to prevent bill shock.
Output Validation and Guardrails
LLMs hallucinate. We build validation layers that check every LLM output against your catalog before serving it to customers. No AI-generated product claims that contradict your actual inventory.
Frequently Asked Questions
Common questions about LLM integration for commerce.
Start Your LLM Integration Project
Tell us which commerce problem you want AI to solve. We will design the integration and model the cost.
Square catalog as the source of truth
LLM responses are grounded in your actual products.
Output validation included
Every AI response is checked before reaching customers.
Cost modeling before build
We estimate monthly inference costs before writing code.
Self-hosted options available
Run models on GCP Vertex AI for full data control.
Start Your LLM Integration Project
Tell us which commerce problem you want AI to solve. We will design the integration and model the cost.
Square catalog as the source of truth
LLM responses are grounded in your actual products.
Output validation included
Every AI response is checked before reaching customers.
Cost modeling before build
We estimate monthly inference costs before writing code.
Self-hosted options available
Run models on GCP Vertex AI for full data control.
