Store expert AI agents
for every store
shopsupport.ai connects to your live catalog, trains agentic systems on your products and compliance policies, and deploys expert shopping agents for retail: drugstore and beauty, wine and beverage, fashion, jewelry and cannabis.
Integration first, production always
Considered retail runs on specialized systems, Dutchie in dispensaries, Shopify or Magento in fashion, WooCommerce in a drugstore, with catalogs that change hourly and compliance rules that vary by state. Generic assistants can't handle that complexity.
shopsupport.ai starts with your live data. We connect to your POS, ingest your catalog, embed your policies, and deploy agents that know what's in stock right now, not what was in stock when someone last updated a spreadsheet.
Everything we build gets evaluated against one question: does it help a shopper find the right product and complete a purchase? If the answer is yes, it ships.
Towards intelligent commerce at scale
Every shopper deserves expert guidance: the recommendation a specialist would give, effect based matches, and compliance aware suggestions, without waiting for staff. That's what we're building.
Vertical Commerce
Compliance aware product discovery for each vertical. Strains and purchase limits in cannabis, claims and skin types in beauty, pairing and age gates in wine, fit and size in fashion, all on live inventory. Every suggestion is in stock and within regulations.
Demand Intelligence
Every conversation is a demand signal. shopsupport.ai surfaces the products, effects, and price points shoppers search for, including demand you cannot see on a sales report.
POS Native Intelligence
General purpose LLMs don't know your inventory. shopsupport.ai agents query your POS in real time, enforce compliance policies, and reason over your specific catalog of products, products, and SKUs, with budtender level expertise.
From your data to a working AI, practically
RAG (Retrieval Augmented Generation) sounds complex. The practical version is straightforward: the AI retrieves relevant pieces of your knowledge before generating an answer. Here's what that looks like in production.
You have an API
If your product data, knowledge base, or content is already served via a REST or GraphQL API, we connect directly. The AI queries your live data at inference time, meaning it always reflects the current state of your catalogue, pricing, or policies without any re indexing.
You have documents
PDFs, Word docs, support transcripts, policy manuals, product spec sheets. We ingest, chunk, embed, and index them into a vector store. Chunking strategy matters enormously: we use semantic boundaries, sliding windows, and metadata tagging so the retriever finds the right passage, not just the right document.
Why chunking strategy matters
Chunking is where most RAG implementations fail silently. Split too aggressively and you lose context. The retrieved passage makes sense in isolation but misses the surrounding reasoning. Split too broadly and retrieval becomes imprecise. You get the right page but not the right paragraph.
We use a layered approach: semantic chunking at sentence boundaries, overlapping windows for context continuity, and per chunk metadata (section title, document type, date) that can be used as a retrieval filter. A compliance query filters to policy documents. A product query filters to a specific category and potency range. The retriever finds not just semantically similar content, but the right kind of content.
Training AI into subject matter expertise
Retrieval can answer questions about your data. Fine tuning shapes how the model reasons, responds, and represents your domain. The two approaches are complementary. Most production systems need both.
We work with LoRA (Low Rank Adaptation) for efficient fine tuning of large models without full parameter updates, making it practical to specialize a strong base model on domain specific instruction data. For smaller models that need to run on premise or with strict performance requirements, we handle full fine tuning and quantization.
The result is a model that writes in your brand voice, knows your terminology, handles your edge cases, and refuses gracefully when a question falls outside its competence, rather than hallucinating a confident sounding wrong answer.
Built for retail that takes expertise to sell.
We work with a focused set of stores where expertise sells: drugstores, wine merchants, fashion, jewelry, dispensaries. We connect to your catalog, train on your products and policies, and deploy a store expert agent that drives measurable conversion lift. Tell us about your store.