AI That Runs Your Floor.
Physical retail runs on reputation, foot traffic, and repeat customers. Eagle Eye automates the operations that keep all three moving.
Your floor produces signal all day. Almost none of it survives the shift.
A customer walks in, browses, asks a question, and leaves without buying. A review goes up on Sunday and gets answered on Thursday. A campaign underperforms for nine days before anyone opens the dashboard that would have said so on day two.
None of this is neglect. It is arithmetic. Retail teams are sized for the floor, not for the back office, and the back office is where the signals pile up. The work that would catch them is real work, it just never wins against a customer standing in front of you.
Automation earns its place here by handling the things that only need doing consistently, not cleverly.
Agents built for retail.
DEPLOYED AGENTS
Review Response Engine
Intercepts and neutralizes reputation threats in real-time.
Marketing Exception Monitor
Detects anomalies in campaign performance before humans notice.
Customer Capture Layer
Passive intelligence gathering from physical location traffic.
Review Tracker
Real-time reputation surveillance across all locations.
After deployment.
Reviews answered across every location
One intelligence layer watching every platform for every store, drafting responses in a consistent voice, and escalating the ones that need a manager.
Campaigns flag themselves when they break
Anomaly detection on spend and performance, so a campaign that stops converting surfaces in hours rather than at the end of the month.
Walk-in traffic becomes a follow-up list
Passive capture at the location turns anonymous foot traffic into a re-engagement audience you can actually market to.
Lapsed customers get contacted on a schedule
Not when somebody remembers. On a cadence, with a message that reflects what they bought and when.
The systems you already run.
Most retail stacks are a POS, a CRM, and a pile of spreadsheets. We connect the first two and retire as much of the third as we can.
Two weeks, start to live.
Friction audit
We map where the hours actually go, not where you think they go. For multi-location retail that usually means the marketing lead and whoever fields the escalations from store managers. 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
Review platforms and the CRM come online first, since reputation is the fastest thing to visibly improve. 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
Four- and five-star review responses tend to auto-publish early; anything under that stays with a human until you say otherwise. 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 review response lands flat, or an anomaly alert fires on a campaign that was fine. 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, Square, or Lightspeed?
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.
We have twelve locations with different voices. Does this flatten them?
Only if you want it to. Tone is configured per location, and most groups run a shared baseline with store-level overrides. The consistency you get is in response time, not in making every store sound identical.
What counts as an anomaly worth alerting on?
You set the thresholds during the audit. The default is a meaningful deviation from that campaign's own recent baseline rather than a fixed number, so a small account does not get ignored and a large one does not alert constantly.
Ready to automate your retail operation?
Live in two weeks. No experiments. Measurable ROI from day one.
Book a Call