Team
AI automation for operations teams that turns emailed PDFs, spreadsheets and supplier feeds into clean data in your systems, and flags the exceptions that need a person.
Sound familiar?
Operations teams are usually the glue between systems that don't talk to each other. Orders arrive by email, stock levels live in one tool, pricing in another, and someone spends the morning moving data between them by hand. The work is important, error-prone and rarely anyone's favorite part of the job.
AI automation for operations is a good fit here because much of the input is semi-structured: PDFs, emails, spreadsheets and supplier files that follow a pattern but never quite the same one. Traditional rules-based automation breaks on that variety. Language models handle it well, especially when paired with strict validation and a review queue for anything that doesn't check out.
A common first project is document processing: reading purchase orders, delivery notes or invoices from an inbox and creating clean records in NetSuite, Odoo or your inventory system. Another is a small internal tool that gives the team one screen for exceptions instead of a dozen tabs. If your pain is supplier stock and price files for a Shopify store, our own FeedGuard app may already cover it without custom work.
We start by mapping the process as it actually runs, including the workarounds. Then we automate the steps that are high volume and low judgment, and route everything else to a person with the context they need to decide quickly.
The problem
Ask an operations lead what their team does all day and you'll hear about shipping, purchasing and keeping customers happy. Watch them work for a morning and you'll see a lot of copying.
Take a distributor that receives 60 purchase orders a day as PDF attachments, each laid out differently by the customer's own system. Someone opens each one, finds the SKUs, quantities and delivery address, and types them into the ERP. It's slow, and a single transposed digit becomes a wrong shipment.
Stock in the warehouse system doesn't match the web store. A supplier's price file has changed format again. The team exports, pastes into Google Sheets, writes a VLOOKUP and hopes nothing shifted.
Late shipments, short deliveries, address problems and backorders all need a decision, but they arrive scattered across email, Slack and portal notifications. Without a single queue, things fall through the gaps until a customer complains.
Where's this order? What's our lead time on that part? Which carrier do we use for pallets to Ireland? The answers exist, but finding them interrupts whoever knows.
Someone pulls numbers from several systems each morning to show what shipped, what's late and what's at risk. It's useful, and it's an hour that a scheduled job could save.
Most of this is high volume and low judgment, which is where automation pays off. The judgment calls stay with your team, but they arrive faster and with the facts already gathered.
Not every operations problem needs a language model. If your supplier sends the same CSV layout every week, a plain import script is cheaper, faster and easier to maintain. If two systems already have good APIs, a direct integration beats an AI step in the middle. We use AI where inputs vary and a person would otherwise need to read and interpret them, and we'll tell you when ordinary automation does the job better and costs less to run.
Where to start
PO attachments are read from a shared inbox, matched to customers and SKUs, checked against price lists and stock, then created as draft sales orders in your ERP. Anything with a mismatch or low confidence lands in a review queue instead of going straight through.
Price lists and stock files arriving by email, FTP or Google Sheets are mapped to your SKUs and units, checked for odd changes such as a price dropping 90 percent, and loaded into your inventory system or store on a schedule.
Late shipments, short deliveries, failed payments and address problems from different systems are pulled into a single list with a summary and suggested action for each, so someone owns every issue and nothing waits in an inbox.
A daily check compares stock, open orders and recent sales velocity and posts a short reorder suggestion to Slack or Teams. A buyer approves or adjusts it; the automation drafts the purchase order.
An internal assistant answers questions like 'how do we ship hazardous goods to Canada?' from your SOPs, carrier docs and past tickets, with links to the source, so new staff stop interrupting the one person who knows.
Shipped, late, at risk and blocked orders are summarized each morning from your ERP, 3PL and store data, with the numbers pulled directly from the systems and a short written commentary on what changed.
Integrations
NetSuite, Odoo, Cin7, Katana, Fishbowl and similar systems through their APIs, or through imports where an API isn't available.
Shopify and WooCommerce for orders, products and stock, including B2B and wholesale setups.
Google Sheets, Excel, Airtable and Postgres, often as a staging layer while you move away from spreadsheet-driven processes.
Gmail, Outlook, SharePoint and Google Drive as the source for PDFs, attachments and supplier files.
ShipStation, carrier APIs and 3PL portals or file drops for tracking, fulfillment status and delivery exceptions.
n8n, Make or Zapier for lighter workflows, and custom services on Next.js and Postgres when volume or reliability needs more control.
FAQ
On clean, typed documents it's usually very accurate, but no extraction is perfect. We add checks that don't rely on the model, such as totals adding up, SKUs existing and prices matching your list, and send anything that fails to a person. You decide the confidence level at which records go through automatically.
Not entirely, but we will map it with you first. Automating a process nobody understands just produces mistakes faster. The mapping often surfaces a few simple fixes that are worth making before or alongside the build.
There's usually another way in: scheduled exports, CSV imports, email-based workflows or a database connection. Screen automation is a last resort because it breaks easily, and we'll tell you if that's the only option.
Data moves between your systems and the model provider only for the steps that need it, under business API terms that exclude training. Automations can run in your own cloud account if you prefer, and we document every connection and what it can access.
Suppliers change formats and systems get updated, so automations need some upkeep. We build monitoring and alerts so failures are visible, document how everything works, and can either support it ongoing or hand it over to your team.
Show us the process your team complains about most. We'll map it, point out where AI automation for operations would help and where a simpler fix will do, and give you a realistic plan.
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