Automation is not new in ecommerce. Zapier, Make and Shopify Flow have moved data between systems for years, and they are good at it.
What they cannot do is handle the exception: the order that needs an address change after the cut-off, the SKU whose supplier slipped, the promo code quietly eating margin. Somebody still watches for those.
An AI employee is the layer for that work. Deterministic handoffs stay on your existing automation. The judgement calls come here, with your rules and your approval limits.
Zapier and Flow are deterministic. Trigger, condition, action. When the case fits the rule, they are cheaper and more predictable than any agent. Keep them.
Agents are for the cases the rules miss. The order that broke, the supplier who slipped, the refund that needs a decision, the catalogue field that is wrong in three places.
The split that works: automation for volume you can define, an agent for the volume you cannot. Both log what they did; only one can explain why.
Both are useful. They fail differently.
| Zapier, Make, Flow | AI employee | |
|---|---|---|
| What it does | Moves data between apps on a trigger | Reads, decides within a policy, and acts across systems |
| Handles exceptions | No. The run fails and someone gets an email | Yes, up to the limits you set, then escalates to a person |
| Setup | You build the steps | You write the rulebook in plain language, or we do |
| Maintenance | Breaks silently when an API changes | Connector maintained by us, failures reported with context |
| Cost model | Per task, scales with volume | Per agent and credits, seats unlimited |
| Best for | Deterministic handoffs, notifications, sync | Judgement, exceptions, multi-step work across systems |
Running both is normal: Flow for the clean trigger, the agent for what the trigger cannot resolve.
If a process has no exceptions and never needs judgement, do not put an agent on it.
Ranked by how much they annoy a real operations team.
Failed payments, address problems, split shipments and stock-outs caught and fixed before the customer writes.
Sales velocity against stock, reorder suggestions by SKU, supplier lead times checked against the calendar.
Titles, attributes and metafields corrected across the store and the marketplace feed, with a before and after log.
Discounts checked for stacking, margin loss and expiry, with the numbers attached.
Missing ASNs, delayed inbound and short shipments chased with the warehouse instead of by email.
Requests timed after delivery, reviews triaged, questions answered from catalogue data.
Eligibility checked, labels issued, exchanges reserved, refunds queued for approval.
One page per week: revenue, exceptions, stock risk, ad spend, support volume, with the change explained.
The rulebook you write is also the training document for whoever joins later.
Steps, exceptions and who approves what. If it cannot be written down, it is not ready.
Order status, catalogue fixes, review requests. Not refunds on day one.
Leave the clean trigger in Flow or Zapier. Route only the exceptions to the agent.
Two weeks of logs tell you which limits to raise and which to keep.
Automation projects die when nobody can show the saving.
| Metric | What it tells you | What good looks like |
|---|---|---|
| Exceptions caught before contact | How often you fixed it before the customer wrote | Rising, week over week |
| Manual touches per order | Count of human steps in the order lifecycle | Falling |
| Failed runs | Automation that broke and was not noticed | Near zero, with alerts |
| Hours returned | Time the team no longer spends on the queue | The number you use to justify the next step |
| Error rate | Wrong edits, wrong refunds, wrong data | Lower than the manual baseline, and visible in the log |
| Time to fix a break | From failure to resolution | Minutes, because the log shows the failed step |
Baseline first: two weeks of counting before you automate anything.
Anything you cannot measure, do not automate.
Store, helpdesk, warehouse, ads and books.
Connect what you already run. Where a portal has no API, the agent works through an isolated browser session with every action logged. See the catalogue.
Native connectors and scoped API connections for the store, helpdesk and warehouse.
Marketplace and carrier portals reached by browser automation, credentials in a vault.
Enterprise builds the connector and keeps it working.
Automation is cheap until it needs a human to babysit it.
| Option | What it costs | What you get |
|---|---|---|
| Zapier or Make | $20 to $100 a month plus tasks | Deterministic handoffs. Fine until a run fails silently. |
| AI employee, entry tier | $99 a month | One agent, exception handling, day-one setup. |
| AI employee, AI Go | $249 a month | Managed onboarding, 200K credits, all 50+ integrations. |
| Operations hire | $45,000 to $65,000 a year loaded | The person currently watching for exceptions. |
Most stores keep Zapier and add one agent. The agent is not a replacement for the plumbing.
Credits are usage, not seats. Peak months are a top-up.
Including where Zapier is the better answer.
No. Keep Zapier for deterministic handoffs. Use the agent where a step needs judgement or an exception path.
It can catch the consequence, for example an order that never reached the warehouse, and correct or escalate it. It does not manage your Zaps.
Order status and catalogue fixes. High volume, low financial risk, visible in a week.
Limits per role, approval thresholds, and a log of every change with a before and after. Start in draft mode.
Yes, through the marketplace APIs where available, or browser automation where not.
Per agent and credits, not per task. Seats are unlimited, which matters when the whole ops team wants to watch.
Day one for one workflow on the entry tier, two to four weeks managed for a multi-system rollout.
Then the first job is writing them down. We do that with you; it becomes the rulebook.
Yes, from your attributes and brand rules, with the source data attached and a human approving before publish.
Order status, returns and address fixes in the chat channel. Read the page.
Working the Shopify admin across apps and channels. Read the page.
The whole operation in one page. AI for ecommerce.
Every connector and how it is reached. See integrations.
One monthly fee per agent, with hosting, AI usage and support included. Every plan begins with a free month, and the first month is free even on self-onboarding.
One agent, one job, 40K credits, all 50+ integrations, and guided self-setup. You start on day one. There is no migration and no custom workflow build at this tier - if you need either, take AI Go and we deliver it for you.
Unlimited seats for your team
On every plan. Add people, not licences - your Brahma bill does not move when your team grows.
One agent, one job, live in weeks, with migration and onboarding done for you.
A working platform with your first agents live, fast.
Heavier operations: more agents, priority delivery.
Your own instance, on your own domain or subdomain, with your governance - run by us, reviewed by your team.
Every plan includes hosting, AI usage and support. Each step up gives you more credits at a lower cost per credit: 40K on Solo, 200K on AI Go, 500K on AI Foundation, 2M on AI Foundation Plus. Credits are shown live per agent in your portal, and one credit is a standard unit of work: a quick task runs 50-150 credits, a standard workflow across two or three systems 250-750, and a deep task such as a report or a document set 1,500-5,000. Unused credits roll over for as long as your plan is active. Top-ups are $50 per 100K credits when you need a spike, and extra agents add on at $249 per agent per month. No credit card to start.
Every plan includes all 50+ integrations, with no per-connector fee. See every integration we connect.
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