Gorgias vs Lasio AI: Which Helpdesk Fits Your Shopify Store in 2026?

Gorgias added an AI Agent layer on top of its ticketing core. The implementation is competent — it can answer routine questions and draft replies — but the product was never built AI-first, and that shows when you put it next to a tool that was. Most workflows still assume a human agent is doing the resolving; the AI helps that agent move faster.

Where it works: drafting replies on common ticket types, surfacing order context for agents, routing low-confidence cases back to the human queue. Where it struggles: pre-sale conversations on product pages, identity-grounded answers that reference what the visitor was just looking at, and any decision that needs to happen before a ticket exists.

Lasio AI is purpose-built for ecommerce conversations. The agent runtime is shared across chat, email, and external helpdesk automations, and it sits on top of a multi-LLM stack (OpenAI + Anthropic + Cohere reranking, with Gemini in the queue) plus role-based model routing and a tiered context-compression system that protects per-conversation cost on long, tool-heavy sessions. That last detail is what unlocks identity-aware replies without margin collapse.

On documented stores, Lasio resolves the majority of pre-sale conversations without a human handoff and the replies are grounded in real visitor history — pages viewed, products considered, prior conversations, past orders. That number drops on stores without written policies and clean catalog data; the AI is grounded in your real data, not invented, which is exactly the constraint you want. Plan the documentation work before the install.

Gorgias is the strongest pure-ticketing surface in this comparison. Macros support variables and conditional logic, SLA tracking is built-in with breach alerts, and the rule engine handles assignment, prioritization, and escalation without external automation. For teams that have spent years tuning this layer, that institutional knowledge is real.

Multi-channel coverage is the harder differentiator. Email, chat, SMS, voice, social — all in one inbox with the same order context. For stores already operating across phone and DMs, consolidating to a chat + email surface isn't an option today. That's the cleanest reason to keep Gorgias.

Lasio ships a real Phoenix-backed inbox with email and on-site chat — not a chat widget bolted onto a ticketing fantasy. Structured AI dispositions (resolved, pending, escalate), draft refinement for humans, SLA policies, assignment rules, attachments with text extraction and image vision, refunds and cancellations from the sidebar. The workflow scaffolding is there; it's just younger and less ornate than Gorgias's.

The shape difference matters more than the feature checklist. Lasio assumes most routine conversations resolve in chat and only a residual stream becomes ticket-shaped; Gorgias assumes everything is a ticket and the goal is to close it faster. If the second assumption is your reality, Gorgias wins. If the first one is, Lasio's surface is the right shape and the unused macro depth on Gorgias is paying for itself.

Gorgias is largely a post-purchase surface. Conversion impact comes from making support fast enough that buyers trust the brand — not from intervening before checkout. There are chat campaigns and trigger-based messages, but the platform was not engineered around pre-purchase conversion lift and the attribution story reflects that.

Lasio is engineered for the pre-purchase conversation. Proactive AI-initiated chats fire on cart idle, product views, and exit intent. Add-to-cart product cards render inside the chat. The AI generates suggestion chips per page. And revenue attribution — including 'influenced' conversions from proactive opens — is built in, not bolted on through tagging spaghetti in GA.

Combined with Bayesian A/B testing on agent behavior, this turns support from a cost line into a measurable revenue lever — a posture Gorgias's category isn't really built for. The lift is uneven across verticals (apparel, supplements, and broad-catalog stores see the biggest swings), so track against a holdout cohort rather than trusting vendor benchmarks.

Gorgias setup is more about workflow design than software install. The platform is well-documented and the Shopify integration is one-click, but the macros, rules, SLA targets, and assignment logic that make Gorgias worth its price take a few days of design work upfront — and another few weeks of tuning before the steady state shows up.

Lasio's auto-configurator crawls the store, ingests policies and catalog, and pre-fills the agent's purpose, brand profile, trust signals, promotions, sales permissions, and boundaries through guided forms. That's not prompt engineering. It's a structured agent that an operator can configure in an afternoon and ship the same day.

The honest caveat: if the policies aren't written, catalog descriptions are thin, or shipping logic isn't documented, the AI's answers degrade. Plan a 2–3 week calibration window during launch — monitor low-confidence conversations, fill documentation gaps, tighten escalation rules. Most of the payoff lands in week three, not week one.

Gorgias pricing has three moving parts: ticket-volume tier, agent seats, and a per-resolution AI Agent charge that's separate from the base. The real-world bill for a brand doing 1,500 monthly tickets with the AI Agent active typically lands in the $400–800/mo range — before voice add-ons and before the seats needed to actually run the workflow.

Lasio's pricing model is intentionally different: AI conversations are included in the base, not metered separately, and the platform is positioned to replace several line items rather than slot in alongside them. A typical D2C brand on Lasio compresses roughly $1,800–3,150/mo of helpdesk, marketing tooling, and freelance line items into one subscription.

The pricing comparison isn't really line-for-line against Gorgias. It's Lasio vs the stack the merchant is running today — and on that comparison the answer is usually obvious within one billing cycle.