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Button bots break on the second turn, public LLMs hallucinate pricing

Rule-based bot didn't understandTwo topics in one message triggers 'please choose from the menu.' The customer leaves frustrated, not confused.
Confident hallucinationsWithout RAG, the agent sounds like an expert while inventing terms that don't exist in your contract.
Queue goes silentThree minutes without an answer in the evening, and the buyer has already moved to a competitor with automated responses.
Operator lacks contextThe bot transfers to an agent, chat history is lost, and the customer has to repeat their entire order from scratch.

For teams that can't afford a 24/7 night shift

AI Agents for Service and Sales

Customers write in their own words. The agent maintains context, cites your documentation, and checks stock via API. If no source fragment is found — it hands off to a human in the Inbox, with no 'sorry, I don't understand' loops[1].

Build an AgentHow It Works

How It Runs a Shift

Understand — retrieve article — take action — hand off to human

Different roles: e-commerce consultant, tier-1 support, SDR. Grounding and guardrails remain unified.

Article first, then response

The agent searches for a verified excerpt. If there's no hit, it won't fill in the blanks from generic pre-training. An honest handoff is better than a fabricated discount.

You define topic guardrails: competitor policies and internal system prompts are strictly off-limits.

Live inventory and slots — not 'I think it's available'

Read calls query warehouse stock or calendars. Write actions (create order, process return) follow agentic action guardrails: structured schema, idempotency, and often human-in-the-loop confirmation.

Getting Started

One role, one knowledge base, then connect the channel

  1. 01

    Define the role

    Support FAQ, SKU selection, or lead qualification. Avoid trying to build a universal employee on day one.

  2. 02

    Upload articles

    The exact answers your team already sends. Fetch prices and inventory via API, not from a week-old PDF.

  3. 03

    Guardrails & handoff

    Define off-limit topics, escalation triggers, and conversation tone.

  4. 04

    Test and launch

    Run test queries, then deploy to your widget or messaging channel. Monitor escalations during week one.

Related Solutions

An agent without a knowledge base and Inbox falls back to 'not understood'

Text without action and escalation is just a demo, not customer service.

Methodology & Sources

How figures on this page are calculated

  1. [1]
    First response from agent in seconds instead of minutes in queue

    The agent's first outbound message is processed via inbound webhook and volbor API response without entering a human waiting queue. 'Seconds' is the target benchmark for the initial message during a live read call (stock, status). The comparison baseline is waiting for a live agent during business hours (minutes) and silence overnight. Not the average resolution time of all Inbox tickets.

  2. [2]
    After-hours leads go cold after waiting

    Scenario: a prospect messages outside business hours, receives no status or slot confirmation, and visits a competitor website. This is a qualitative funnel observation, not an industry-wide measured percentage. The 'lead → 0' counter represents the outcome of that delay, not a conversion guarantee post-implementation.

  3. [3]
    About two-thirds of tickets can be resolved autonomously

    When order status, inventory, appointment booking, and documented FAQs make up approximately ⅔ of inbound inquiries, an agent equipped with RAG and read-APIs can resolve them without Inbox escalation. Complex cases, damaged goods photos, and custom contracts go to human specialists. ⅔ is an inbound queue breakdown model, not an aggregate benchmark across all implementations. Check your actual autonomous resolution rate in volbor analytics.