The AI agent behind the fastest ecommerce support teams

A chatbot follows a script. It can only handle situations it's been explicitly programmed for — when it hits something outside that, it routes to a human or fails. An AI agent reads the situation, reasons through your policies, and acts. A chatbot tells a customer how to request a refund. An AI agent processes it. That difference in what actually happens at the end of the conversation is what makes the customer experience feel either resolved or frustrating.

Software that handles customer inquiries end-to-end without a human — not by deflecting to articles, but by completing the actual task. Checking order status, processing a return, updating a shipping address, cancelling an order. Mim does this across chat, email, and messaging. When something genuinely needs a human, it escalates with the full conversation and customer context already loaded.

It reads the message, checks your knowledge base and policies, pulls relevant order or account data, decides what should happen, and does it. No keyword matching, no decision trees. If a customer says "my order hasn't arrived and I need it by Friday" — Mim checks the order status, understands the urgency, and responds with what's actually true and what options exist. If it can resolve it, it does. If it can't, it says so and hands off.

Most AI tools in support are built to deflect — suggest an article, reduce ticket volume, hope the customer goes away. Mim is built to resolve — connect to your systems, complete the transaction, confirm it's done. It also lives inside Dixa's platform natively, so customer history, routing, and agent workspace are all the same whether Mim is handling the conversation or a human is. No seam between AI and human interactions.

Complex complaints that need discretion, situations where a customer is visibly upset and needs to feel heard, exceptions to policy that need authorisation, anything ambiguous enough that a wrong answer would cause real damage. Mim is designed to recognise these and escalate rather than attempt them. The brands that get the best results are the ones that think carefully about where the line is — not trying to automate everything, but automating the right things.

Both, but the mechanism matters. Speed of resolution is the biggest driver of CSAT for routine inquiries — a customer who gets their refund confirmed in 30 seconds doesn't feel like they had a worse experience because it was AI. The CSAT risk comes from wrong answers, dead ends, or an AI that tries to handle something it shouldn't. Rapha saw +14% CSAT after moving to Dixa. Oliver Bonas reached 90% CSAT while doubling agent productivity per hour.

Mim connects to Shopify and Magento to pull live order data and act on it. A customer asks where their order is — Mim checks the actual current status and responds. A customer wants a return — Mim checks your policy, initiates it, confirms. These query types make up the majority of ecommerce contact volume. Dott reduced response time by 70% and handling time by 40% using Mim across exactly these scenarios. Hobbii achieved an 81% self-service rate.

Volume spikes don't affect Mim the way they affect a human team. There's no queue limit on AI resolution, no seasonal hiring, no onboarding lag. Your human team absorbs only the contacts that actually need them — complex, exception-based, high-stakes. The brands that feel the biggest relief at peak are the ones who deployed before it, not during.

Most Dixa customers come from Zendesk, Freshdesk, Gorgias, or Intercom. Implementation takes two to four weeks with dedicated onboarding support — knowledge base migration, integration setup, agent training. The transition is structured to maintain continuity. You're live before the old contract ends.

Days to two weeks for most customers. Mim learns from your existing knowledge base and website — no training data to build from scratch. Standard Shopify and Magento integrations are configured through guided setup. The main time investment is making sure your knowledge base is accurate before Mim starts handling live conversations. Dedicated onboarding is included.

Self-service rate — what share of inquiries Mim resolves without escalating — is the primary signal. You can also track CSAT on Mim-handled conversations versus human-handled, and time-to-resolution. Where Mim escalates most frequently tells you where your knowledge base needs work. A healthy deployment shows a rising self-service rate over the first few weeks as Mim encounters more of your real inquiry patterns.

Depends on your contact mix more than your headcount. If a significant share of your volume is routine — order tracking, returns, FAQs — there's a strong case regardless of team size. If most of your contacts are complex or high-judgment, the case is weaker. Dixa works best for mid-market ecommerce brands where volume is growing faster than the team can absorb it without AI.