AI Chatbots

An assistant that actually understands your customers — not just their keywords

A mohi.to team member at their desk, mid-thought

What it solves

Your support inbox gets the same five questions, every single day.

A question outside business hours means a customer just waits — or leaves.

A traffic spike means longer queues, not more staff to handle them.

Your own team re-asks each other the same product questions your customers do.

Rule-based chatbots follow a script. Ours actually understand what's being asked.

Built on a large language model instead of a fixed set of rules, it reads a real, unscripted question the way a person would — then answers from your own product data, docs, and policies instead of guessing or falling back on "I didn't understand that."

What that changes day to day:

24/7

no queue, no closing time — someone gets an answer the moment they ask.

∞

conversations handled at once — as fast on a Black Friday spike as on a quiet Tuesday.

What it can actually do

The first three are for your customers. The next three are for your own team.

Most teams start with one side of this and end up wanting both — it's the same assistant either way, just answering a different kind of question depending on who's asking.

Product & catalog knowledge

Ingredients, specs, compatibility, availability — the questions your support inbox already gets every day, answered instantly instead of queued.

Recommendations that fit the question

Points someone to the right product for what they actually asked, instead of a generic best-sellers list.

Support that resolves, not just deflects

Handles the repeat questions on its own and hands off what genuinely needs a person — with the context already attached.

Internal product knowledge

Anyone on a customer-facing team gets the same accurate answer instantly, instead of pinging a colleague or digging through a shared drive.

Sales & field support

Stock, pricing, and current promotions on the spot, without stepping away from a call or a client visit to go check.

Ticket & ops triage

Routes a technical or facilities issue to the right place immediately, instead of sitting in a shared inbox until someone notices it.

Where teams are already using this

This isn't experimental anymore

A few examples already running at real scale — cited here as market context, not as mohito's own work.

Starbucks

Its Deep Brew system learns your usual order, when you get it, and what you add to it — then suggests it back to you as you near any store in the chain.

Nestlé

A public "cookie coach" walks home bakers through a recipe, while a separate internal ChatGPT-based tool is used by more than 7,000 employees to save real time every week.

Carrefour

Tell its Hopla assistant what's already in your fridge and it builds a recipe and a shopping list around it — within your budget, diet, and allergies.

Klarna

Its OpenAI-built assistant now handles the workload of hundreds of full-time support agents, cutting average resolution time from 11 minutes to about 2.

Zalando

A ChatGPT-powered stylist, live across all 25 of its markets, has helped more than 2 million shoppers find something they'd actually wear.

IKEA

Its Billie chatbot took over routine queries, freeing thousands of staff to become paid design consultants — now IKEA's fastest-growing sales channel.

How we build it

Built to work with what you already have

No new platform for your team to babysit — the assistant plugs into the stack and workflow you're already running.

Deploy anywhere

One assistant, reachable from a standalone web app, a widget on your existing site or Shopify storefront, or inside your mobile app.

Grounded in your data

Product catalogs, help docs, and policies connected through a vector database, so answers stay accurate instead of invented.

Guardrails from day one

Boundaries on what the assistant will and won't answer are part of the build, not something bolted on after launch.

Natural, not scripted

It follows a real conversation instead of matching keywords to a decision tree, so a rephrased question still gets answered.

Live data when it matters

An MCP server lets the assistant check real-time information — stock levels, order status, availability — instead of guessing.

Connects to real actions when you're ready

Order status, account details, a booking — once it's proven itself on the basics, we connect it to the systems that need a login.

FAQ

Questions before you commit to a build

How is this different from the chat widget we already have?

Most existing widgets match keywords to a fixed set of scripted replies, so anything phrased differently than expected gets a dead end. This is built on a large language model, so it follows the actual question — including follow-ups and rephrasing — and answers from your real content instead of a script.

What data can the assistant actually use?

Anything text-based — product catalogs, help docs, policies, FAQs — connected through a vector database so answers stay grounded in what you actually publish, not general internet knowledge. Live systems (stock, orders, bookings) connect through an MCP server for information that changes by the minute.

How do you stop it from saying the wrong thing?

Guardrails are part of the initial build, not a fix we add after something goes wrong — we define upfront what the assistant should decline to answer and test that boundary before launch, the same way we scope what it should answer.

Where can we actually put this?

It's built as its own application with a small integration layer, so the same assistant can sit on a standalone page, embed as a widget on your existing site or Shopify storefront, or live inside your mobile app — one assistant, wherever your customers already are.

Is this only for customers, or can it help internally too?

Both, usually. The same assistant can answer a product question for a shopper and answer the identical question for your own sales or support team — most clients end up running it on both sides once they see the internal use, not because it was pitched that way upfront.

What does a first version cost, and how long does it take?

It depends on how much data you have and which channels you want it on, so we scope that on a short discovery call rather than quoting a price off a list. What we can promise upfront: a real, working first version — not a long exploratory phase before you see anything.

Can it take real actions, like checking an order or an account?

Yes, once it's connected to a system that requires a login. We treat that as a later step — prove the assistant on public information first, then add authenticated actions once there's a reason to.

What happens after we launch?

Real usage shows what people actually ask, which is a better guide than guessing upfront — that's what we use to decide whether the next step is more data sources, a new channel, or extending it to your own team.

One assistant, four stages

We start scoped and prove value fast, then expand as real usage shows what's worth adding next.

Process

Our process

01 – Discovery & guardrails

We map your data sources and agree on what the assistant should — and shouldn't — answer.

02 – Prototype

A working assistant on your public knowledge, ready to test with real questions in days.

03 – Build & connect

We wire up the data sources, deployment channels, and any systems that need a login.

04 – Ship & iterate

Launch on the channel you chose, then expand scope as real usage shows what to add next.

Some of the clients we work with

Ready to give your customers — and your team — a smarter first answer?

Tell us what you'd want an assistant to handle first — we'll scope a working first version around it, not a generic template.

Get in touch

A mohi.to team member working at their desk