Stop the Leaks. Scale the Machine.
Ecommerce operations are full of revenue leaks, slow returns, weak listings, undetected exceptions. Eagle Eye seals them autonomously.
Catalogue work scales linearly. Your team does not.
Every SKU needs a description, images, and attributes. Every return needs tracking, triage, and a refund that reconciles. Every stock discrepancy needs somebody to notice it before a customer does.
The margin in e-commerce sits inside operations, and operations is exactly where the manual work concentrates. A catalogue that grows faster than the team maintaining it turns into a catalogue with thin descriptions, missing photos, and listings nobody has looked at since launch.
The leaks are individually small and collectively expensive, which is precisely the profile of work worth automating first.
Agents built for ecommerce.
DEPLOYED AGENTS
AI Draft CoPilot
Autonomous content generation for high-velocity product listings.
Inventory Description Bot
Transforms raw inventory data into conversion-optimized narratives.
Return Tracking System
Eliminates revenue leakage through autonomous return management.
Operations Exception Engine
Autonomous exception detection across the full ecommerce stack.
Photo Request Workflow
Demand-triggered visual content pipeline for inventory.
Already deployed here.
DEPLOYED FOR
After deployment.
Product copy generated from the data you already have
Attributes, fitment, and specifications become conversion-oriented descriptions at whatever rate your catalogue grows.
Returns stop leaking revenue
Return events tracked from the payment platform through to resolution, so refunds that were never processed and items that never came back surface automatically.
Exceptions surface before customers find them
Stock mismatches, pricing errors, and broken listings detected across the stack and routed to whoever can fix them.
Photo gaps get chased automatically
Demand-triggered requests for the listings that are actually getting traffic, rather than a photo backlog worked front to back.
The systems you already run.
Shopify and Stripe cover most of the operational surface. Where you are on a bespoke platform, n8n bridges it. Most of these deployments touch at least one system nobody else supports.
Two weeks, start to live.
Friction audit
We map where the hours actually go, not where you think they go. For e-commerce that usually means whoever owns the catalogue and whoever gets the angry emails about returns. The output is a ranked list of targets scored on time cost and reversibility. We start with high-time, easy-to-undo work.
Systems connected
Store platform and payment processor connect first, because returns and exceptions are the fastest measurable win. Nothing is published yet. The first agent runs in shadow mode, producing output that only your team sees, so you can judge the quality against work you already trust.
Approval queues live
Listing generation moves to auto-publish quickly for new SKUs; anything touching price or refund amounts stays gated. Anything sensitive routes to a human queue before it goes out. You decide what auto-publishes and what waits for a person, and you can move that line at any point.
Handover
Every agent gets a named owner on your side, a documented rollback, and a metric agreed before the build started. If the number does not move, we said what it was in advance and there is nowhere to hide.
What operators ask first.
How long before anything is actually live?
Two weeks to a working deployment. The first three days are a friction audit, the next four connect your systems and run the first agent in shadow mode, and the second week moves it into production behind approval queues. You see real output in week one.
What happens when the AI gets something wrong?
A generated description gets a specification wrong, or a return is flagged that was already resolved. Every agent has a defined approval path, and anything sensitive routes to a person before it goes out. Every deployment ships with a documented rollback, so switching an agent off is a decision, not an engineering project.
Do we need a technical team to run this?
No. We build, deploy, and operate the agents. Your side needs one named owner per agent, someone who reviews output and owns the result. That is a role change from generating work to reviewing it, not a new hire.
Will it work with Shopify, WooCommerce, or a custom platform?
Almost certainly. Orchestration runs on n8n, which connects to effectively anything with an API, and we have built directly against the systems listed above. If a platform has no API, we will tell you that in the audit rather than after the invoice.
Who owns what you build?
You own the output and the data. The agents run on your accounts and your systems. If we stopped working together tomorrow, the workflows and everything they have produced stay with you.
How is this different from just using ChatGPT?
A chat window needs a person to open it, paste context, and copy the answer somewhere useful. That is still manual work with an AI-shaped middle. These agents are triggered by events in your systems, run without being asked, and write their output back where the work actually lives.
Our catalogue is 40,000 SKUs. Is that a problem?
It is the reason to do this. Volume is where generated copy pays for itself, the break-even against a person writing descriptions arrives somewhere in the low thousands. Backfilling an existing catalogue is usually a separate, one-time run alongside the live pipeline.
How do you handle fitment or compatibility data?
It comes from your source of truth, not from the model. The agent formats and writes; it does not decide what fits what. Where the underlying data is wrong, the description will be confidently wrong too, which is why the audit looks at data quality before anything gets generated.
Ready to seal the leaks?
Live in two weeks. No experiments. Measurable ROI from day one.
Book a Call