Turns chats into completed orders

What you lose in the chat thread

A customer who messages your chat is almost ready to buy — but there's too much friction between their message and the order:

• in the evening, on weekends and during peak hours a message sits unanswered — and while they wait, they move on to whoever replied first;
• the customer describes what they want in their own words ("something inexpensive as a gift") and gets told to scroll the catalogue themselves;
• the same questions over and over — in stock?, how much?, what fits?, how do I pay? — burn through a manager's time;
• the cart gets built, but nobody suggests the matching item or the worthwhile add-on — the average order stays lower than it could be.

It's not a bot — it's a team of agents

The market is flooded with "AI bolted onto a chat window": one bot doing everything at once — getting confused, inventing stock, forgetting what was said earlier. We build differently — an orchestrator plus a team of specialized phase agents, each owning one step of the sale. Like an orchestra: every player handles their own part, and the conductor brings it together into one result — a placed order.

• Request agent — Works out what the customer actually wants, even when they phrase it vaguely and in their own words.
• Catalogue agent — Picks products to match the request and budget, checking stock — suggests what's real, not what's invented.
• Cart and upsell agent — Manages the order and suggests matching items in the right place — lifts the order value without nagging — this is usually where the average order grows.
• Copywriter agent — Phrases replies like a human and in the brand's voice, not in dry "robot" templates.
• Checkout and handoff agent — Closes to a placed order, picks the payment method, and passes a tricky case to a live operator with full context.

You get as many agents as your business needs — three or thirty. We add a new one for every new job: a progressive discount on the next item, complaint handling, special requests. Together they cover the manager's work: understand, pick, build, upsell, close.

Where it works

One engine — different scenarios for each niche with a large catalogue and guided choice:

• Online shops — picking products from the customer's request, building the cart, upselling matching items, checking out right in the chat.
• Showrooms and retail stores — advice and holds across a large range, answers to "is it in stock?", "what fits?", "how much?".
• Services with guided choice — helping pick the package or configuration for the task and budget, moving a warm customer to checkout.
• B2B and wholesale catalogues — selecting items to a spec, calculating the order composition, handing a complex enquiry to a manager with context.

People burn out — the system doesn't

A good manager is irreplaceable. But a team of agents covers the weak points every live chat has:

• it won't leave a message unanswered in the evening, on a weekend or during a surge;
• it won't forget to suggest the matching item or quietly skip a small order;
• it won't burn out or reply curtly at the end of a shift;
• it won't get stock wrong or promise what isn't there — it says exactly what's in the catalogue;
• it won't quit and walk to a competitor, taking the database, scripts and hard-won upsell combinations with it;
• it scales instantly — ten chats or a thousand, no hiring.

People stay where empathy and judgement matter — the stream of routine chats and the peaks go to the system.

Case context

The system was built for an online shop with a large catalogue — the flow of incoming messages is uneven and surges are impossible to predict: in the evening, on weekends and during promotions, managers physically can't carry every chat through to an order. Customers often describe what they want in their own words rather than by SKU, so understanding the request and picking within budget are critical here.

Why it's hard to copy

A demo takes an evening. Production is a different thing: holding the context of a long thread, never inventing stock, upselling in the right place rather than pushing, and catching the exact moment to hand off to a person. It's not about one model — it's about a coordinated team of phase agents under an orchestrator, and getting that interaction right is harder than hooking up a single bot. Tuned on real chats, not on a demo bench.

What we measure on the pilot

We look at what shows up in revenue, not "wow demos":

We don't publish other people's numbers or promise a result before the pilot: we track these from day one of your project and show them to you directly.