70% of your support tickets have the same 20 answers. AI handles them instantly.

Basic chatbots match keywords to canned responses. AI support agents understand context, remember conversation history, process actions (refunds, order changes, bookings), and handle multi-step problems. They don't just answer questions — they resolve issues end-to-end.

It escalates to your human team with the full conversation context, customer history, and a suggested resolution. The handoff is seamless — the customer doesn't have to repeat anything. You define exactly which scenarios trigger escalation.

Yes. We train the AI on your existing support responses, style guides, and brand voice. If your brand is casual and friendly, the AI is casual and friendly. If it's professional and formal, so is the AI. It's indistinguishable from your best human agents.

Most deployments go live within 2-3 weeks. Week 1: training on your data and configuring integrations. Week 2: testing and refinement with your team. Week 3: gradual rollout starting with easy ticket categories and expanding from there.

Chat (website, app, WhatsApp, Facebook Messenger), email, and phone. All channels share context — if a customer starts on chat and follows up by email, the AI knows the full history.

Deflection and upsell are not in tension if you wire them as one flow. The agent handles the three deflection layers — WISMO from carrier webhook (Shippo, ShipStation, EasyPost, AfterShip), returns within policy via the returns portal (Loop, Returnly, Narvar), and exchanges through the size/fit grounding pack — so that 65–75% of post-purchase tickets close without a human. The upsell layer sits inside the same conversation. When the agent processes a size swap, it offers the same-fit complementary SKU (the way a clienteling associate would) and adds it as a one-click add to the exchange order. When it confirms an order, it triggers the Klaviyo post-purchase flow opt-in. When a return reason indicates fit dissatisfaction rather than product defect, it routes to 'exchange with concession' instead of 'refund' — that single rule alone tends to recover 18–28% of would-be refunds. The pattern that breaks is silo'd: a CX team uses Gorgias for tickets and Klaviyo for retention with no shared context, so the agent never sees the customer's lifetime value at the moment of decision. Wire the CDP, and the upsell line shows up in the same dashboard as the deflection line.

Five backend layers must speak to the agent or the agent fails on the second turn. Order state from the OMS (Shopify, Manhattan Active Omni, NetSuite, SAP OMS) — read and write, because the agent must read fulfillment status and write order modifications. POS for in-store transactions and per-location inventory (Shopify POS, Lightspeed X-Series, NCR Counterpoint) — read-only on inventory, but the read must be real-time or the agent gives wrong stock answers. Carrier APIs for live tracking events (Shippo, ShipStation, EasyPost, AfterShip) — without these the WISMO answer is 24 hours stale and worse than no answer. Returns portal (Loop, Returnly, Narvar) — the agent must initiate, track, and label-print through the portal API rather than emailing the customer a link. Customer profile/CDP (Klaviyo, Bloomreach, Segment) — for VIP routing, lifetime value, and segment-aware tone. Where it breaks: SKU normalization between POS and web catalog (in-store often uses retail SKU, web uses parent-variant), inconsistent return reasons across channels, and time-zone drift between the carrier webhook and the OMS timestamp. Budget half the integration spend on the normalization layer, not the connectors themselves.

Plan for three phases, not a single number. Days 1–14: deflection lands at 20–32% on WISMO-only flows. The agent only knows what the carrier API tells it, so first-touch resolution looks good on order-status questions and bad on everything else. Days 14–45: deflection moves to 48–62% as the returns portal integration and sizing/fit grounding pack come online, and the agent starts handling exchanges end-to-end rather than escalating them. Days 45–90: plateau at 68–78% across the full post-purchase ticket mix for a brand with a clean returns policy and a fully wired catalog. Two factors decide where you plateau in the band. First, returns policy edge cases — final-sale categories, holiday extensions, multi-region windows — push you down 4–8 points if not encoded explicitly. Second, catalog size and variant complexity: a brand with 200 SKUs plateaus 5–7 points higher than one with 8,000 SKUs because the agent's catalog grounding stays current with less drift. Brands that miss the 65% floor by day 90 almost always have one missing integration (usually the returns portal or the CDP) rather than a model-quality problem.