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Automation and AI agents for ecommerce brands whose orders move through a real warehouse.

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© 2026 ZavrAIBuilt with ☕ and ❤️ by Bilal
ZavrAI

Keep orders moving.
Cut the manual work.order backlogs.inventory errors.support queues.

We build automations and AI agents that connect your store, warehouse and support. Then keep them running.

Book a 30 minute callSee the work

An order. A payment. A question.

Your everyday tools send the signals.

The next step, handled.

Orders routed. Stock updated.
Replies prepared.

Your tools in. The next step handled.

Your tools become working automationsStore, payment, spreadsheet and support tools flow into ZavrAI. Three inputs enter individually, the processor checks the batch, and one completed workflow emerges. Example outcomes include orders ready to fulfill, updated stock, prepared replies and routed returns.Order + paymentReady to fulfillReturns workflowReturn routedSupport agentReply preparedInventory syncStock updatedZavrAI

Built with these teams.

Sheraton InteriorsARC Intelligence

Automation for the work between systems.

Keep store and warehouse records aligned, answer routine support requests, and flag exceptions for your team. We build and maintain all three.

Ops truth layer

+

Your store and your warehouse stop agreeing, and nobody finds out until a customer does. We build the pipelines that compare what Shopify believes against what the warehouse actually did. Then we fix the gap or stop the line.

Built with
Shopify, WMS, n8n
Timing
Live in 2 to 4 weeks
Explore the service
Workflow study · illustrative records
Store / warehouse reconciliationCatch the gap before the customer.
SHOPIFYFulfilledOrder · example
WAREHOUSENot shippedSame order · example
Mismatch foundFix the gap or stop the line ↗

Support agents

+

Routine tickets answered from real order data, not from a scraped help page. Order status, returns eligibility, exchange rules. Money decisions and sensitive cases go to a person, every time.

Built with
Claude API, tool calling, Shopify
Timing
Live in 3 to 5 weeks
Explore the service
Workflow study · illustrative records
Support / bounded decisionsContext before an answer.
Where is my order?Example customer request
CONTEXT CHECKEDOrder lookup ✓Store policy ✓
Human handoff ↗

Sensitive requests go to the team.

Exception handling

+

The work that only appears once something is already wrong. Over-ship audits, stuck order sweeps, cost variance checks. It runs on a schedule, it escalates to a person, and it never passes silently.

Built with
Audits, sweeps, alerting
Timing
Live in 2 to 3 weeks
Explore the service
Workflow study · illustrative records
Scheduled checks / human decisionsThe problem gets an owner.
Over-ship auditReview ↗
Stuck order sweepEscalate ↗
Cost varianceCheck ↗
Team notifiedEvery exception is surfaced

Systems in daily use.

Built alongside the people who depend on them. Numbers below are real. Where a number is an estimate, it says so.

Workflow study · illustrative records
Zoho / job progressionOne change. Work moves.
DEAL · EXAMPLEStage changedZoho CRM
CHECK EXISTING WORKProceed once ↗
TasksClickUp↗
InvoiceZoho↗
Project boardZoho Projects↗
Repeated event ↳ existing work retained
Zoho CRM pipeline / Sheraton Interiorsn8n, Zoho CRM, Zoho Projects

A pipeline that moves the job forward on its own.

Twenty-seven stages across leads and deals. Each stage change creates the right tasks, invoices and project boards across Zoho, ClickUp and Sheets. Repeated webhooks do not create duplicates, because every branch checks whether the work already exists before it runs.

27 pipeline stages. 24 workflows live.

Open a teardown
Workflow study · illustrative records
Warehouse / dispatchThe batch is ready.
PICK SHEET EXAMPLE
BINORDERQTY
A–0110412
A–0310421
B–0210433
Aisle-ordered · bins allocated
DISPATCH03orders in this example
Labels ready ↗
Plan boxes → labels → pick → sync
Bulk shippingNext.js, PostgreSQL, BullMQ

The app the warehouse runs on.

Label-first multi-order picking. Box planning, label purchase, aisle-ordered pick sheets, shipment sync. It allocates live warehouse bins at label time and redirects pickers around empty or reserved locations mid-pick.

3,000+ orders a day. 5,000 at peak. Las Vegas and Mexico.

Open a teardown
Workflow study · illustrative records
Returns / chain of custodyEvery handoff accounted for.
01Return labelIssued
02Warehouse scanReceived
03Customer updateSent
04Refund preparationReady for review
One tracked lifecycle across teams
Returns and RMAn8n, Python, PostgreSQL

Returns that do not get stuck between teams.

Return labels, warehouse scans, customer updates and refund preparation in one tracked lifecycle. A custom async engine tracks hundreds of shipments at once with rate limits and retries.

60 to 70 percent less manual returns work. Estimated.

Open a teardown
Workflow study · illustrative records
Support / bounded decisionsContext before an answer.
Where is my order?Example customer request
CONTEXT CHECKEDOrder lookup ✓Store policy ✓
Human handoff ↗

Sensitive requests go to the team.

Support agentClaude API, tool calling, Shopify

An agent that handles the easy stuff.

Live on the storefront. It combines order lookup, policy context and internal tools. Low-risk requests close on their own. Sensitive ones go to the team.

Fewer tickets a human ever has to touch.

Open a teardown
Workflow study · illustrative records
Operations / shared viewThe floor. Finance. One record.
FulfillmentReturnsInvoices
WORKSTREAMSHARED RECORD
BackordersInventory ↗
ContainersSupply chain ↗
ReturnsWarehouse ↗
InvoicesFinance ↗
SyncedOne source of truth
Ops platformNext.js, PostgreSQL, WMS

One screen the floor trusts.

Replaces disconnected spreadsheets with a shared view of backorders, fulfillment, containers, returns and invoices. One source of truth, synced automatically.

Used every day by warehouse, finance and supply chain. Inventory synced across six countries.

Open a teardown
Workflow study · illustrative records
Enquiries / durable captureSaved before anything sends.
WEBSITE ENQUIRYNew project enquiry
✓ Saved to database
↓
Team notification ↗Customer confirmation ↗
If sending fails the enquiry stays safe
Lead capture / ARC Intelligencen8n, Supabase, Gmail

The lead is safe before the automation runs.

The website writes the enquiry to the database first. Only then does the pipeline pick it up, notify the team and send the confirmation. If an email step fails, the lead is still there. Database events are filtered to inserts only, so editing a record later does not email the customer twice.

Zero leads lost to a failed send.

Open a teardown

Three steps. No deck.

Weeks, not quarters.

LookManual stepsHours per weekWhat to automate
BuildWorking versionWeekly reviewLive handoff
RunMonitoringCost checksOngoing support
1. Look

A call, then a map

30 minutes on your stack. Then a written map of the manual steps, what each one costs in hours, and which are worth automating first. Free. Nothing starts until you say so.

2. Build

You see it every week

A working piece every week from week one. We build against synthetic data first and touch your live Shopify and WMS only once the behaviour holds.

3. Run

We keep it alive

Live in 2 to 6 weeks with monitoring and a cost dashboard. When it breaks at 2am it pages us, not you.

Your first agent should be boring.

Start with the task your team repeats every single day. Make it dependable. Then earn the complexity.

Anything that moves orders, inventory or money stays deterministic. The agent gets a bounded job. That split is the whole design.

Meet the team
ORDERS · INVENTORY · MONEYDefined rules
ROUTINE SUPPORTBounded agent
SENSITIVE DECISIONSYour team

Everything here runs in production.

Nothing on this list is aspirational.

Claude API
n8n
Shopify
WMS
Zoho
ClickUp
Next.js
PostgreSQL
Python
TypeScript
BullMQ
Redis
Carrier APIs
QuickBooks
Slack

The things you are actually worried about.

We tried this with Zapier and it broke constantly. Why is this different?

Zapier retries and hopes. We build explicit failure states, so a broken step stops and alerts instead of passing a bad order through. Every run is logged and replayable. When something fails you are told what failed, on which order, and why.

Does an AI agent get to touch my orders?

No. Agents answer tickets. Anything that moves orders, inventory or money is deterministic code with hard rules. That split is deliberate. Anything that cannot fail does not go near a language model.

What access do you need?

A Shopify custom app with scoped permissions, a WMS API key, and read access to your helpdesk. We ask for the narrowest scopes that do the job and document every one before you approve it.

How long until something is live?

Two to six weeks depending on the work. You see a working piece every week from week one, so you know by week two whether this is going anywhere.

Who owns the workflows?

You do. Code, workflows and documentation sit in your repo from day one. If you stop working with us, nothing turns off.

What does it cost?

The first call and the map are free. Build work is fixed scope, quoted before anything starts. Running it afterwards is a monthly retainer based on volume. You see all three numbers in writing first.

What happens after it goes live?

We run it. Monitoring, alerting and fixes are part of the retainer. If you would rather run it yourself, we hand over the runbook and step back.

Find out what your manual ops actually cost.

MapBuildRun ↗

30 minutes on your stack. Then a written map of every manual step, what it costs you in hours per week, and which ones are worth fixing first. Free, and yours whether or not we build anything.

Book a 30 minute call

If automation is the wrong answer for your stack, we will say so on the call.

3,000+

orders a day through the bulk shipping app.

See the work