Libera Large Vision Model

AI that sees the shelf — and beyond

Purpose-built for retail, it redefines how brands detect, analyze, and act on product-level insights from granular product recognition to full-store visibility.
Trained on 100M+ verified retail images, the Libera Large Vision Model powers real-time retail visibility — capturing product-level detail directly from stores, shelves, and receipts.
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How it works

A paradigm shift in retail AI

Retail-Specific Vision Intelligence

Unlike generic vision models, LLVM is purpose-built for FMCG and retail in emerging markets — optimized to detect SKUs, analyze product positioning, pricing, and track shelf changes with precision.

Edge-Sourced Data at Scale

LLVM is trained on images captured by real users — not artificial benchmarks. This includes product scans, shelf photos, and receipts gathered via Libera’s merchant and consumer apps.

Continuous Learning Loop

Every new image helps improve the model. This self-learning loop ensures that LLVM stays ahead of packaging updates, local SKUs, and shifting retail formats.

Multimodal, Context-Aware AI

LLVM understands more than just pixels. It combines visual recognition with contextual metadata — location, product category, store type, price, promo — to deliver actionable retail intelligence.

Visual detection

  • Trained on internet images or lab-curated datasets, not real retail environments
  • Unable to detect localized SKUs, regional packaging, or price tags
  • Require manual labeling and constant supervision for retail applications
  • Poor performance in cluttered, low-light, or real-world store conditions
  • Lack contextual understanding of store type, geography, or shelf layout
  • Slow implementation cycles due to the need for custom or proprietary datasets
  • Trained on 100M+ verified images from actual stores, receipts, and product scans
  • Accurately detects global and local products with SKU-level precision, tied to universal store frameworks and transactional context
  • Requires up to 80% less labeled data and manual effort to adapt across retail domains or onboard new clients
  • Autonomously learns from nuanced, authentic data—no need for constant manual inputs
  • Optimized for shelf photos, receipts, packaging and layout changes, and environmental complexity
  • Integrates contextual data like price, product category, promo, store type, and location through Libera’s Knowledge Graph
  • Up to 80% reduction in audit costs through automated shelf tracking
  • Improved product visibility and placement compliance across fragmented markets
  • Real-time data flow from store-level activity and beyond into brand dashboards
  • Automated inventory management and campaign performance validation
  • Enhanced forecasting, planning, and supply chain decisions driven by verified shelf intelligence
  • Comprehensive product performance and behavioural analytics drive up to 55% in sales growth through targeted engagement
Inside the store

Connected retail environment

By linking real-time data from brands, merchants, and consumers, Libera creates a collaborative ecosystem that continuously fuels the Large Vision Model with decentralized, crowdsourced retail data.
This verified stream of domain-specific intelligence enables the model to learn, adapt, and improve—driving smarter insights, better shopping experiences, and increased profitability across the retail chain. Libera’s blockchain-powered reward system ensures transparent, equitable compensation for data contributors, while brands gain access to AI-curated intelligence—delivered at scale and in real time.

Intelligent automation with AI agents

LLVM’s continuous learning cycles enhance AI agents’ ability to interact with visual data and adapt to real-world retail scenarios. By correlating inputs like receipts, shelf photos, and product scans, agents can go beyond routine execution—forecasting issues, detecting anomalies, and delivering contextual feedback.

This enables automated decision-making at scale—from identifying stockouts and validating planogram compliance to tracking promotional performance—unlocking new levels of operational efficiency across the retail chain.

Global scalability

The Libera Large Vision Model adapts seamlessly to regional and cultural differences, offering unmatched flexibility across varied retail environments. Trained on authentic, retail-specific images captured at the edge by real users, it enables tailored solutions for diverse products and markets—reducing time to deployment, cutting reliance on external datasets, and creating a significant competitive advantage.

Real visibility. Real results

LLVM transforms fragmented store-level data into structured retail intelligence — reducing blind spots for brands, eliminating manual audits, and enabling smarter in-store execution across emerging markets.

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