Four operations verticals. One shared architecture.

AnswerBnB is not four separate products — it's one context, knowledge, and AI decision architecture applied to four kinds of businesses that all share the same underlying need: answer accurately, act only when authorized, and escalate the rest.

AI Property Operations

Guest communication that knows which property, which reservation, and which guest it's talking to — before it says a word.

Problem

  • Hosts field the same questions across every property, every day
  • Generic chatbots don't know which listing a guest is even asking about
  • A wrong answer about check-in time or house rules costs real trust

Workflow stages

  • Pre-arrival: check-in details, missing-information detection, reminders
  • During stay: questions, issues, maintenance requests, escalation
  • Post-stay: checkout information, feedback, follow-up

How AnswerBnB approaches it

  • Every conversation resolves to a real organization, property, guest, and reservation
  • Answers are grounded in that property's own knowledge — nothing shared across listings
  • Low confidence or an out-of-scope request hands off to a human with full context

Example questions

  • “What time is check-in?” / “Is there parking?” / “What's the Wi-Fi password?”
  • “Can I check in early?” — classified and routed by rule, not guessed
  • A maintenance report — logged as a real work item, not lost in a chat log

Ecosystem: designed to connect with property-management platforms such as Guesty, Hostaway, Hospitable, Lodgify, OwnerRez, and Smoobu. Architecture Compatible the adapter layer these connectors would plug into · vendor connectors themselves are Planned. See Integrations for the full picture.

AI Commerce Operations

Product, order, and customer questions answered from real store data — not a generic storefront FAQ.

Problem

  • Pre- and post-purchase questions overwhelm small commerce teams
  • Product specs, compatibility, and policy answers need to be exact, not approximate
  • A missing or damaged order needs the right order record, not a generic reply

Workflow

  • Store → product → inventory → customer → order → conversation → AI → answer/action/escalation
  • Product questions, specifications, compatibility, availability, shipping, returns, refunds, exchanges
  • Missing or damaged order support, post-purchase support, proactive updates

Ecosystem: designed to connect with ecommerce platforms such as Shopify, WooCommerce, and BigCommerce. Planned

AI Customer Support Operations

A support layer that identifies the customer and the intent before it retrieves anything — then answers, clarifies, or escalates.

Decision chain

  • Identify customer → identify intent → identify relevant context
  • Retrieve knowledge → evaluate evidence → determine confidence
  • Answer, act, clarify, or escalate — never guess

Channels

  • Web chat and email — the channels the current architecture is built around
  • WhatsApp, SMS, Messenger, Instagram, and voice are on the roadmap, not live today

Positioned as an AI operations layer that can sit around existing systems — Salesforce Service Cloud, Zendesk, Intercom, Freshdesk, HubSpot — rather than requiring replacement. Planned

AI ISP & NOC Operations

Outage reports, speed complaints, and device troubleshooting resolved with real service and incident context — not a scripted flow.

Workflow

  • Customer → service → circuit/account → device → network → incident → conversation → AI → action/ticket/escalation
  • Outage reports, slow-speed complaints, device troubleshooting, billing questions
  • Incident classification, technician escalation, resolution documentation

What this does not claim

  • No direct network monitoring is implemented today
  • No native connector to any NMS, OSS/BSS, or RADIUS platform exists yet
  • The architecture (adapters, incident context, conversation model) is built to support this vertical

Ecosystem: Splynx, Sonar, UISP, MikroTik, RADIUS, SNMP and similar systems. Planned