AI & BUSINESS
AUTOMATION.

We implement Retrieval-Augmented Generation (RAG) combined with strict systemic guardrails. Rather than letting the model answer freely from its general public training, we force the AI to first query a private Vector Database containing only your official policies and catalog details. The AI is structurally constrained to ONLY answer using the facts retrieved from your database, and if the info isn't found, it gracefully routes the conversation to a human support agent.

Yes. We write serverless automation scripts that trigger on standard commerce webhooks (e.g. order paid, inventory low). We can build workflows that automatically tag and categorize newly imported products, flag high-risk fraud orders based on historical data patterns, or automatically draft and queue personalized marketing emails for VIP shoppers.

We choose robust, industry-standard solutions. For database vectors, we use PGVector (extending our PostgreSQL schemas) or dedicated managed vector stores like Pinecone for rapid semantic lookup. We generate semantic text embeddings using OpenAI's `text-embedding-3-small` or Cohere's multilingual embedding models, guaranteeing high semantic matching accuracy in less than 50 milliseconds.

Absolutely. We route all queries through enterprise-grade API endpoints that explicitly guarantee in their terms of service that your inputs, documents, and customer prompts will **never** be stored or used to train public foundational LLM models. All vector databases are hosted in isolated virtual private clouds (VPCs) under strict end-to-end data encryption.