AI · Review-First

AI Countertop Software for Stone Shops

Shops searching for AI countertop software usually want faster quoting, cleaner price lists, and searchable slab context — without losing control. Kallisti Pro uses AI inside a review-first countertop workflow: Customer → Job → Areas → Materials → Layout → Quote → Deposit → Production.

What AI countertop software should mean in a real shop

AI should reduce tedious document work, not replace your pricing judgment or production decisions.

Kallisti Pro keeps AI outputs in review queues and company-scoped records before they affect live quotes.

  • Vendor price list parsing with human review before publish
  • Knowledge Vault catalog ingest with review gates
  • Shop Knowledge Assistant over company SOPs, machines, and indexed vault docs
  • Countertop quote workflow, layout, and customer quote packets alongside AI helpers

Built vs verify in your tenant

Feature availability depends on Firebase configuration, AI provider keys, and your company setup. Treat the list below as architecture intent — confirm each surface in a demo environment before sales promises.

  • Partially built: AI-assisted vendor price imports, catalog ingest review, Shop Knowledge Assistant
  • Needs verification: AI slab catalog search/indexing, public website chat widget
  • Not verified Golden Path: AI drawing takeoff — keep gated or demo-only until proven

How this differs from generic AI tools

Kallisti Pro is countertop quoting and production workflow software first. AI assists document ingest, search, and shop questions inside that workflow — not as a standalone chatbot bolted onto spreadsheets.

Compare with tools like 2cm.ai on workflow fit, review controls, and what actually ships in your environment.

Questions buyers should ask any AI countertop vendor

Use the same checklist for Kallisti Pro and every alternative:

  • Who approves AI-parsed prices before they hit customer quotes?
  • Can old quotes keep snapshots when price books change?
  • Does AI search stay inside your company data boundary?
  • What happens when the model is wrong — fail safe or silent publish?