Retail & hospitality operations

You're making million-dollar decisions with spreadsheets and intuition.

Custom AI for multi-location operations: Computer vision turns footage into data, Smart GM links inventory to margin, labor scheduling matched to demand.

AWS
Artsy
Ashley FDE
Charlotte Hornets
Flywheel
Rappi
Swissgear

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Use cases

Where are you bleeding margin?

These are the workflows we deploy most often for consumer brands. Fix the one hitting your P&L hardest.

Demand forecasting

Stop buying based on last year's sales and gut instinct.

Forecast what will sell, where, and when before you commit inventory.

Impact
Higher sell-through rates
Increased full-price sales
Better seasonal buying decisions

Assortment optimization

Same SKUs carried everywhere. Local customer behavior varies, product selection doesn't.

Optimize which products to carry by location, tailored to local demand.

Impact
Higher sell-through rates
Increased full-price sales
Improved sales per square foot

Smart GM

Store execution varies. Process consistency breaks down. Operational standards slip.

Proactive coaching for store managers on SOP adherence, operational rigor, and maintenance timing.

Impact
Customer retention gains
Higher CSAT
Reduced staff turnover

Computer vision insights

Service gaps are invisible until customer complaints surface.

Turn physical operations structured data. Track execution, timing, and adherence in real-time.

Impact
Consistency across locations
Faster service times
Improved compliance scores

Supply chain optimization

Manual vendor orders. Emergency procurement. Late deliveries.

Automate ordering based on real demand and optimize fulfillment routing.

Impact
Forecast error reduction
Fewer markdowns
Better seasonal buying

Operations efficiency

Excess inventory draining cash. Stockouts on best sellers.

Optimize allocation by location and SKU.

Impact
Revenue capture through availability
Improved gross margin per location
Store-level performance gains

Our approach

We embed with your team, observe how work flows, and build systems that align with what’s already in motion.

Trusted by

Invisible helped Nasdaq streamline a data integration process, reducing customer onboarding time and saving 10,000 developer hours.

Invisible improved Cohere’s data quality and scalability, enhancing multilingual, coding, and reasoning capabilities to strengthen its enterprise-ready AI performance.

Invisible automated processes like invoice reconciliation, W9 processing, claim approval letters, and compliance support, resulting in significant cost and time savings.

Invisible's team worked within the client's platform to review model conversations from the research team and assess the level of accuracy of the model responses.

Invisible's team stepped in to review partially completed conversations between different models. We analyzed the search results, reference material, and model responses.

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