Answers what it can · hands over what it can't

Support automation that knows when to stop.

Most people have been trapped in a chatbot loop and have not forgotten it. The difference between a support bot customers tolerate and one they resent is not how clever it sounds — it is how quickly it recognises it is out of its depth and gets a person involved.

A well-built one handles the genuinely repetitive questions — where is my order, what are your hours, how do I reset this — and passes everything else on with the conversation already summarised, so nobody has to explain themselves twice.

Watch it hand over
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HandoverWith context
Answers fromYour content
Out of hoursStill covered
Every gapLogged

The line

What it should answer, and what it should pass on

Drawing this line is the entire design job. Get it right and customers barely notice the automation. Get it wrong and you have built the thing everyone complains about.

The temptation is always to widen the left column. Resist it. A bot that handles sixty per cent of contacts brilliantly is worth far more than one that attempts everything and frustrates a third of the people who use it.

Handles it

Repetitive and answerable

  • Order status. Where is it, when does it arrive, what does that tracking status mean.
  • Policy questions. Returns, warranty, delivery areas, opening hours, what is included.
  • Account self-service. Reset a password, update an address, resend an invoice or receipt.
  • Product questions. Specifications, compatibility, what fits what, what is in stock.
  • Booking and scheduling. Finding a slot, confirming, rescheduling, sending the reminder.
  • Triage. Working out what is actually being asked and collecting the details before a person reads it.
Hands it over

Human, or high stakes

  • Anyone who is upset. Frustration is detectable and it is always the wrong moment to insist on a form.
  • Money moving. Refunds, disputes, cancellations, anything with a financial consequence.
  • Complaints. A person needs to hear it, not a summary of it filed by a bot.
  • Anything unusual. If confidence is low, it should say so rather than approximate an answer.
  • Explicit requests. "Let me talk to someone" is the end of the conversation, immediately, every time.
  • Second attempts. If the same question comes back, the answer did not land. Escalate rather than repeat.

Expectations

What deflection actually looks like

Vendors quote containment rates in the nineties. Those numbers usually count a conversation as contained if the customer gave up. Drag the slider and look at the shape rather than the headline.

Everything that arrives1,000 all conversations
Repetitive enough to automate550 in scope for the bot
Actually resolved440 answered end to end
Reaches a person560 escalated, with context attached

Assumes the bot resolves about eight in ten of the questions genuinely within its scope — a realistic figure for a well-grounded system, not a best case. The remainder escalate, which is the system working correctly rather than failing. The goal is never one hundred per cent. A bot that refuses to hand over is how you lose customers who were only mildly annoyed to begin with.

The stack

Built into the tools your team already uses

Nobody wants another inbox. The handover lands in the help desk your team already works in, and conversations sit alongside the ones that arrived by email.

Channels

WhatsApp
Messenger
Telegram
Instagram DM
Slack
Twilio SMS & voice

Help desk & CRM

Zendesk
Intercom
LiveChat
HubSpot
Salesforce
Google Calendar

Commerce & scheduling

Shopify
WooCommerce
WordPress
Stripe
Calendly
Zapier

Models & runtime

OpenAI
Anthropic Claude
Google Gemini
Rasa (self-hosted)
Python
Node.js

Channels

Six places support conversations start

The same knowledge and the same handover rules, surfaced wherever your customers already are. One system, several front doors.

Channel 1 of 6
01 — Web

The widget on your site

The obvious one, and still the highest volume. Appears where it is useful rather than ambushing everyone on arrival.

  • Knows which page you are on
  • Recognises signed-in customers
  • Keeps history across visits

Best starting point for most businesses

02 — Messaging

WhatsApp and Messenger

Where a large share of customers would rather talk. Conversations persist, so nobody re-explains anything a week later.

  • Threads survive between contacts
  • Photos for damage and returns
  • Notifications people actually read

Strong for retail and trades

03 — Email

Inbox triage and drafting

Not a chat window at all. Incoming email classified, enriched with account context, and answered or drafted for approval.

  • Drafts sit for review, not auto-send
  • Sorts by intent, not keyword
  • Works with your existing help desk

The quiet, high-value one

04 — SMS

Text and short codes

For simple status questions and appointment traffic, where opening an app is more effort than the question deserves.

  • Confirmations and reminders
  • Reply to reschedule
  • Very high open rates

Bookings, deliveries, service calls

05 — Internal

Slack and team channels

Pointed inward instead of outward. Staff ask about policy, process and accounts without interrupting a colleague.

  • Answers cite the internal document
  • Respects existing permissions
  • Cuts the repeat questions

Often the easiest first launch

06 — Voice

Phone, with a clean handoff

Calls answered and qualified out of hours, with a transcript and summary waiting for whoever picks it up next morning.

  • Captures the details while fresh
  • Transfers live where staffed
  • Never traps anyone in a menu

See Voice & Phone AI Agents

Interactive

Design the handover rules

This is a real conversation we have at the start of every support project. Switch rules on and off and watch six live conversations get routed. There is no correct configuration — there is only the one that matches your risk tolerance.

Turn everything off and the bot attempts all six, including the refund and the person who has already asked twice. That configuration is how support bots earn their reputation.

Escalation rules

handled by bot escalated

Honesty

Why people hate most support bots

Every one of these is a decision somebody made, not a limitation of the technology. Which means every one of them is avoidable.

01

No way out

The single biggest cause of resentment. If there is no obvious route to a person, customers stop trusting the whole channel — including the parts that worked.

02

Pretending to be human

A fake name and a stock photo. People work it out within two messages and feel handled. Say what it is; nobody minds a bot that is upfront.

03

Answering when it should not

Producing something plausible instead of admitting uncertainty. One confidently wrong answer about a policy costs more than fifty deflections saved.

04

Losing the context on handover

The customer explains everything again to the agent. This alone undoes the entire time saving and reads as contempt.

05

Interrupting immediately

A pop-up three seconds after the page loads, before anyone has read anything. It is the digital equivalent of being followed around a shop.

06

Rigid menus

Endless button trees where nothing matches the actual question. If it cannot handle free text, it is a phone menu wearing a chat window.

07

Forgetting between messages

Asking for an order number that was supplied two messages earlier. Small, and it destroys confidence faster than almost anything else.

08

Built on nothing

Deployed over documentation that is thin, contradictory or years out of date. The bot did not invent the confusion; it exposed it.

09

Never reviewed after launch

Nobody reads the transcripts, so the same failure repeats for months. Support automation is a system that needs tending, not an installation.

Scope

What a support automation build includes

It begins with reading your existing conversations. Everything worth knowing about what to automate is already in them.

01

Transcript analysis

Reading a real sample of your past conversations to find what genuinely repeats, how customers actually phrase it, and where agents spend their time.

02

Scope agreement

An explicit written list of what the bot will attempt and what it will always pass on, agreed before anything is built.

03

Knowledge grounding

Connecting your policies, help articles and past resolutions so answers come from your material with a source, not from a model's general impression.

04

System connections

Order lookup, account status, booking availability. Without these it can only recite policy, which is a fraction of the value.

05

Handover design

The rules, the summary format the agent receives, and the routing. This gets more attention than the answering does.

06

Tone and copy

Wording that sounds like your business, including how it declines, apologises and admits it does not know.

07

Testing on real cases

Run against actual past conversations with known outcomes before a single customer sees it.

08

Reporting

Volume, resolution rate, escalation reasons, and the questions nothing could answer — the last being the most useful of the four.

09

A review rhythm

A scheduled read of transcripts after launch, because the first month teaches you more than the whole build did.

Questions

Support automation, answered

Will customers know they are talking to a bot?

Yes, because we tell them. It is labelled clearly and never given a fake human name or photograph.

People are entirely comfortable with a bot that is upfront and gets them to a person quickly. What they object to is discovering they were misled.

What if it gives a customer the wrong answer?

The design goal is that it declines rather than guesses. Answers are grounded in your documentation with a confidence threshold, and anything below it becomes a handover instead of an attempt.

Every conversation is logged, so when something does go wrong you can see exactly which passage caused it and correct the source.

Is this going to replace our support team?

Not in any project we have been involved in. What changes is the mix — the repetitive volume drops, and the work that remains is the difficult, judgement-heavy kind that people are better at anyway.

Teams generally report the job improving. If your goal is specifically headcount reduction, we are probably the wrong fit and would rather say so now.

How long before it is live?

A first version handling a defined set of questions on one channel is usually weeks, not months. We start deliberately narrow.

The first few weeks of real conversations tell you more about what to automate next than any amount of planning would.

Can it look up a customer's actual order?

Yes, and that is where most of the value sits. Reciting policy is easy; answering "where is my order" from live data is what actually reduces volume.

It needs a connection to your commerce or CRM system, and identity verification appropriate to what it is exposing.

What happens outside business hours?

It keeps answering what it can and collects everything it cannot, so the queue on Monday morning arrives triaged and summarised rather than raw.

For many businesses this alone justifies the project — questions get answered at nine in the evening instead of waiting for morning.

Do we need documentation before we start?

Some, and it rarely needs to be as much as people fear. A handful of clear, current policy pages beats a large archive nobody has updated.

Part of the work is identifying which gaps matter, and the transcript analysis usually answers that in the first week.

Which platform do you build on?

It depends on where your team already works. If you have a help desk with a good API, the bot lives inside it. If not, we build the layer and connect it to your channels.

We avoid being locked to one vendor's proprietary builder — the knowledge, the rules and the transcripts should remain yours and portable.