How to Migrate From Gorgias to an AI Support Agent Without Losing History

Why merchants move off Gorgias

Merchants who migrate from Gorgias to AI usually do it for one of two reasons: the bill stopped being predictable, or the automation stopped keeping up with ticket volume. Gorgias is a capable help desk built for ecommerce, with strong Shopify ticket data and a deep macro system. But its AI Agent is priced per resolution, which means your support cost rises every time the AI does its job well. For a store growing into peak season, that pricing model turns success into a bigger invoice.

The second reason is structural. Gorgias started as a help-desk-first product and bolted AI on top. Macros and rules still depend on a human picking the right canned reply at the right moment. An AI agent flips that: it reasons over your knowledge base and live store data, drafts or sends the reply itself, and takes the action behind it, such as looking up an order, starting a return, or applying a refund within your rules. You are not replacing a chat window. You are replacing the manual work between the question and the resolution.

Before you switch anything, get clear on what you are trying to fix. If your team is drowning in repetitive WISMO questions, you want an agent with deep order-tracking actions. If your Gorgias overage line keeps surprising you, you want flat, message-credit pricing instead of per-resolution fees. Naming the problem keeps the migration focused and gives you a number to measure against later.

What to export before you switch

Export everything that holds institutional knowledge before you touch the new tool. That means ticket history, macros, help center articles, tags, customer notes, and any saved views or rules. Gorgias lets you pull most of this through its data export and its API, and you want it captured while your account is still active and fully paid. The worst time to discover a missing export is after you have downgraded.

Ticket history matters more than people expect. It is the single best training source for an AI agent, because it shows the exact questions your customers ask and the exact answers your team gave. A clean CSV or JSON export of past conversations becomes both an archive for compliance and a corpus you can feed into the new agent's knowledge base.

Do the export while your plan is at full access. Some help desks throttle or strip export features on downgraded accounts, and large stores with hundreds of thousands of tickets should use the API with pagination rather than the dashboard, which can time out. Pull the data in batches, confirm the row counts match what the dashboard reports, and only then consider any plan change. A migration that starts with an incomplete export is a migration that ends with a compliance gap.

1. 1Export ticket and conversation history (Gorgias Settings to Data export, or via the API for large volumes) as CSV or JSON, including timestamps, customer email, tags, and the full message thread.
2. 2Export your macros. Copy each macro's title, body text, and any variables or actions it triggers into a spreadsheet so you can map them to AI actions later.
3. 3Export help center articles. Pull every published article's title, URL, and body, since these become the agent's primary knowledge source.
4. 4Export your tag list and any tagging rules, plus saved views and assignment rules, so you can recreate routing logic.
5. 5Export customer data and internal notes attached to contacts, especially anything that affects how a VIP or wholesale customer should be handled.
6. 6Save a copy of integration settings: which Shopify store, which channels (email, chat, social), and any third-party apps connected to Gorgias.

Mapping Gorgias macros to AI actions

Macros and AI actions solve the same problem in opposite ways. A Gorgias macro is a saved reply, sometimes with a small automation attached, that a human selects. An AI action is something the agent decides to do on its own when the conversation calls for it. Migrating well means translating each macro into either knowledge the agent can answer from or an action the agent can take, then deleting the macros that exist only to paper over a missing integration.

Start by sorting your macros into three buckets. Informational macros (shipping times, return windows, sizing help) become knowledge base content. Action macros (start a return, resend a confirmation, apply a discount) become agent actions with rules attached. Routing macros (tag and assign to the wholesale queue) become escalation and handoff logic. Most stores find that half their macros collapse into a handful of well-configured actions once the agent can read live order data.

The audit itself is valuable. Teams accumulate macros over years, and a typical Gorgias account has dozens of near-duplicates, dead promos, and replies referencing policies that changed two seasons ago. Migrating forces a clean-out: you keep the logic worth keeping, you let the agent generate the wording dynamically instead of maintaining frozen canned text, and you stop maintaining a library that quietly contradicts itself. Fewer, sharper rules beat a sprawling macro shelf every time.

Migrating help docs and your knowledge base

Your knowledge base is the fuel for the new agent, so migrate it deliberately rather than dumping every article in and hoping. An AI agent answers from what you give it. Feed it stale shipping times or a contradicting return window and it will repeat the mistake confidently. Migration is a chance to audit: keep what is accurate, rewrite what is vague, and delete what is dead.

Most agents can ingest your help center several ways: import the article URLs directly, crawl your storefront and policy pages, upload PDFs and docs, or sync a help center export. Bookbag can deep-crawl a website from its sitemap and import help docs, then re-embed everything on a schedule so the knowledge stays current. The goal is a single source of truth the agent reads from, not five overlapping copies.

• Import help center articles by URL so updates on your site flow back into the agent on the next retrain.
• Add your policy pages explicitly: returns, refunds, shipping, warranty, and privacy. These drive the agent's decisions, not just its answers.
• Rewrite anything written for a human reader to skim into clear, declarative statements the agent can quote ('Returns accepted within 30 days of delivery on unworn items').
• Use your exported ticket history to find gaps. If customers keep asking something your docs never answer, write the article now.
• Set a retrain schedule so price changes, new products, and policy updates re-embed automatically instead of drifting out of date.

Recreating tags, views, and routing

Tags, views, and routing rules are the operating logic of your support team, and they migrate as a structured map rather than a copy-paste. In Gorgias, tags drive views, views drive assignment, and assignment drives who answers what. In an AI-first setup, the agent handles the front line, so your routing logic mostly governs the exceptions: which tickets the agent should not touch, and where the ones it escalates should land.

Rebuild routing from the customer's intent, not from your old queue names. Decide which intents the agent resolves autonomously, which it handles but flags for review, and which it must hand straight to a human. A damaged-item complaint with photos, a chargeback, or a wholesale pricing negotiation usually belongs with a person, and the agent's job there is to gather context and route cleanly, not to improvise.

Running both tools in parallel during cutover

Run Gorgias and the new agent side by side for one to three weeks before you cut over. This is the single most important step for avoiding downtime, and it is also how you build confidence with a skeptical team. During parallel running, the AI agent answers in a controlled way while Gorgias remains your safety net, so a bad answer never reaches a customer unsupervised.

There are two common parallel modes. In suggest mode, the agent drafts replies that a human reviews and sends, which lets you grade its answers against what your team would have written. In live-on-a-slice mode, you point one channel or a percentage of chat traffic at the agent and watch real resolutions while everything else stays in Gorgias. Most merchants start in suggest mode for a few days, then move to a traffic slice once the draft quality looks right.

Parallel running is also where you catch the unglamorous integration bugs: an email address that routes to the wrong inbox, a Shopify scope that was not granted, a channel that did not reconnect. Finding those while Gorgias is still answering costs you nothing. Finding them after you have cancelled costs you a day of silent, unanswered tickets. The overlap is cheap insurance, and the only mistake is rushing through it to save a few weeks of double subscription.

1. 1Week 1: run the agent in suggest mode. It drafts replies, your team edits and sends, and you log where it was wrong or thin.
2. 2Fix the gaps you find by adding knowledge or tightening action rules, then retrain.
3. 3Week 2: route a slice of live traffic (one channel, or 20 to 30 percent of chat) to the agent for autonomous resolution, with handoff on low confidence.
4. 4Compare resolution rate, CSAT, and escalation rate on the slice against your Gorgias baseline.
5. 5Week 3: widen the slice as the numbers hold, keeping Gorgias as the handoff destination for anything the agent escalates.
6. 6Cut over the front line to the agent once it clears your resolution-rate threshold, and keep Gorgias read-only until your archive is verified.

Connecting Shopify order data to the new agent

The order integration is what turns the agent from a FAQ reader into something that resolves tickets, so connect it early and test it hard. Gorgias gives agents a sidebar of Shopify order data to read. An AI agent goes further: it queries the order itself, decides what the customer needs, and acts. That is the difference between 'I can see your order shipped' and 'your order shipped yesterday, here is the tracking, and it is due Thursday.'

Bookbag connects natively to Shopify through the app store, and also to WooCommerce and BigCommerce, plus an API and SDK for custom and headless stores. Once connected, the agent can run order-tracking and WISMO lookups, start returns and exchanges, process refunds within your caps, answer subscription and account questions, and recommend products from your live catalog. Set the rules carefully: refund caps, return eligibility windows, and which actions require a human sign-off.

• Connect the same store Gorgias used, and confirm the agent reads live order status, fulfillment, and tracking.
• Define refund and return rules explicitly, including dollar caps and eligibility windows, before enabling those actions.
• Decide which actions are fully autonomous and which need human approval, then encode that as rules, not hope.
• Test edge cases: split shipments, partial refunds, subscription orders, and international orders with customs delays.
• Confirm personalization works for logged-in customers so the agent greets and serves returning buyers with context.

Testing resolution rate before full switch

Do not cut over on vibes. Set a resolution-rate threshold before you start, then hold the migration to it. Resolution rate, the share of tickets the agent fully handles without a human, is the number that decides whether the switch pays off. Industry benchmarks for 2026 put well-configured ecommerce AI in the range of 40 to 65 percent deflection, with purpose-built, action-taking agents reaching higher resolution on order-related queries. Bookbag's own framing is that an agent can deflect up to around 70 percent of tickets autonomously, depending on your mix.

Measure on your real ticket stream, not a sandbox. During the parallel slice, track how many conversations the agent closed without handoff, how many it escalated, and how customers rated the outcome. Watch escalation reasons closely. If it keeps handing off the same question, that is a knowledge or rules gap you can close, and each fix raises the resolution rate before full switch.

Training your team on the new inbox

Your agents are not being replaced, their job is changing, and a clear story prevents a quiet revolt. In a Gorgias world, reps spend their day answering repetitive tickets. After migration, the AI handles the repetitive volume and your team handles the exceptions: angry customers, complex returns, VIP relationships, and the judgment calls software should never make alone. That is a better job, but only if you frame it that way and train for it.

Practically, your team needs to learn three things: how to read a handoff the agent sends them, how to flag a bad AI answer so it gets fixed, and how to teach the agent by improving the knowledge base instead of writing one-off replies. The best support people become editors of the agent, not just responders. That shift is where the lasting quality gains come from.

• Show each rep how handoffs arrive with full conversation context, so they never ask the customer to repeat themselves.
• Give everyone a one-click way to flag a wrong or weak AI answer, and a fast loop to fix the underlying doc or rule.
• Reassign senior reps to the high-empathy, high-stakes tickets the agent escalates on purpose.
• Train one person as the agent's owner: they watch resolution rate, retrain on a schedule, and tune action rules.
• Celebrate the volume the agent absorbs, so the team sees it as relief, not threat.

The teams that migrate well stop measuring agents by tickets closed and start measuring them by how good the AI gets under their supervision.

Avoiding data loss and downtime

Two failures sink a migration: losing history and going dark. Both are avoidable with sequencing. The rule is simple: never cancel or downgrade Gorgias until your exports are verified and the new agent has carried the front line cleanly for at least a full week. Downtime in support is not a minor inconvenience, it is unanswered customers during your busiest hours, so the overlap period is non-negotiable.

Verify your archive before you let go. Open the export, spot-check that ticket threads, timestamps, tags, and customer emails are all present and readable. Confirm that any open disputes or pending returns in Gorgias are either closed or carried into the new tool. Only when the data is provably safe and the agent is provably handling volume should you downgrade Gorgias, and even then, keep it read-only for a billing cycle as insurance.

1. 1Verify exports: open the files and confirm threads, tags, timestamps, and contacts are complete and readable.
2. 2Resolve or migrate every open ticket in Gorgias so nothing is stranded mid-conversation at cutover.
3. 3Keep both inboxes monitored during the overlap so no channel silently stops being answered.
4. 4Point the public widget and email routing to the new agent only after it clears your resolution threshold.
5. 5Downgrade Gorgias to read-only, not deleted, for one billing cycle in case you need to reference a closed ticket.
6. 6Cancel only after a full cycle with no gaps, with your archive stored in cloud storage you control.

How Bookbag compares to Gorgias on pricing and actions

Here is the honest comparison. Gorgias is a strong, mature help desk with deep ecommerce roots and a large app ecosystem, and if you want a human-led help desk with AI as an assist, it is a reasonable choice. Bookbag is built the other way around: an AI agent that resolves and acts is the product, and the shared inbox supports it. The clearest differences are pricing and what the AI actually does.

On pricing, Gorgias charges for its AI Agent per resolution, so your cost scales with success. Bookbag uses flat monthly plans with message-credit allowances and a spend cap you set, where one credit equals one AI reply and a typical conversation runs about four replies. No per-resolution fee, no success penalty, no surprise overage bill, since overages are top-up packs. For a growing store, that turns a variable, volume-linked cost into a budget line you can plan around.

On capability, both connect to Shopify, but Bookbag's agent takes the action rather than handing your rep a sidebar to act from. It tracks orders, processes returns and refunds within your rules, recommends products, and works across website chat, email, WhatsApp, Instagram, Messenger, and Slack from day one, with most Shopify stores live in under a day. Gorgias is genuinely strong on multi-store help-desk operations and its app marketplace, so weigh what you actually need.