About Retail AI

Shelf Intelligence,
Engineered at Scale

We are a multi-disciplinary team of AI researchers, distributed systems engineers, and retail specialists building the infrastructure that powers modern shelf auditing for the world's largest brands.

The Challenge

Retail Shelf Auditing
Is Broken

Consumer packaged goods companies spend $40 billion annually on field merchandising and shelf auditing. Yet the process remains painfully manual: representatives with clipboards counting products one by one, taking blurry photos, and transcribing data into spreadsheets.

The result? Incomplete data, inconsistent coverage, and weeks of delay before insights reach decision-makers. By then, the shelf has already changed.

Existing computer vision solutions are either too slow for real-time use, too expensive for mid-market brands, or too generic to handle the visual complexity of real-world retail environments.

Out-of-stock events missed40%
Data entry errors18%
Time to insight2-4 weeks
Cost per store visit$45-120
Planogram compliance~60%
Our Solution

AI That Sees the Shelf

Retail AI replaces manual shelf audits with a fully automated computer vision pipeline that detects, classifies, and counts every SKU in seconds.

Detect Everything

Our proprietary YOLO-based model detects individual products, shelf facings, and price tags in cluttered retail environments with 97.4% accuracy.

Process Anywhere

Upload images via API, dashboard, or mobile SDK. Process in the cloud on auto-scaling GPU clusters or deploy edge inference for real-time use.

Act on Insights

Get structured JSON results with bounding boxes, confidence scores, and SKU mappings. Integrate directly into your BI tools, ERP, or field force apps.

Numbers That Speak

12M+
Images Processed
97.4%
Detection Accuracy
<1s
Avg. Latency
12
Countries Served
40+
Enterprise Clients
99.9%
Uptime SLA
Our Journey

From Research to Global Scale

2024

Research Phase

Started as a research initiative with a team of computer vision PhDs. Trained first prototype on 50K shelf images from partner supermarkets.

2025 Q1

Platform Architecture

Built the inference pipeline with YOLO11, TensorRT, and distributed GPU workers. Designed the credit-based billing system from scratch.

2025 Q2

Enterprise Pilot

Launched private beta with 3 major CPG brands across LATAM. Achieved 97.4% detection accuracy on real-world store shelves.

2025 Q3

Public Platform

Released the self-serve dashboard, REST API, and SDK. Scaled infrastructure to handle 10K+ images per day with sub-second latency.

2026

Global Scale

Now processing millions of images monthly across 12 countries. Continuous model retraining with federated data from enterprise clients.

Technology

Built on Modern Foundations

Every layer of our stack is chosen for performance, reliability, and developer experience.

AI / ML

  • YOLO11 (Detection)
  • TensorRT (Optimization)
  • PyTorch (Training)
  • ONNX Runtime

Backend

  • Django 5 + DRF
  • PostgreSQL 16
  • Redis 7 (Cache/Queue)
  • Celery Workers

Infrastructure

  • Kubernetes
  • Docker
  • GitHub Actions
  • Terraform

Frontend

  • Next.js 14 (App Router)
  • TypeScript
  • Tailwind CSS
  • shadcn/ui
The People

Six Specialized Teams, One Mission

Retail AI is the product of dozens of engineers, researchers, and designers working across disciplines to solve one hard problem.

AI Research

8 researchers

Computer vision PhDs and ML engineers building proprietary detection models trained on millions of real shelf images.

YOLO11
TensorRT
PyTorch
ONNX

Platform Engineering

12 engineers

Distributed systems engineers designing the async inference pipeline, Celery workers, and GPU orchestration.

Django
DRF
Celery
Redis
Kubernetes

Frontend & Design

6 designers & devs

Product designers and frontend engineers crafting the dashboard experience with pixel-perfect UI and real-time visualizations.

Next.js
Tailwind
shadcn/ui
Recharts

Data Infrastructure

4 engineers

Data engineers managing the ingestion, annotation, and storage pipeline. S3-compatible object storage with 30-day lifecycle.

PostgreSQL
MinIO/S3
Pandas
Airflow

DevOps & Security

5 engineers

Infrastructure and security team managing multi-cluster Kubernetes, CI/CD pipelines, and enterprise-grade compliance.

K8s
GitHub Actions
Terraform
Vault

Customer Success

3 specialists

Dedicated team for onboarding enterprise clients, providing API integration support, and building custom inference solutions.

Slack
Zendesk
Notion
Figma
Principles

Values We Live By

Accuracy First

Every pixel matters. Our models are trained and validated against ground-truth datasets with sub-percent error margins.

Privacy by Design

No image is retained beyond the processing window. End-to-end encryption. SOC 2 Type II compliance roadmap.

Performance at Scale

Sub-second inference on standard resolutions. Auto-scaling GPU workers. 99.9% uptime SLA for Enterprise.

Developer Experience

Clean REST API, comprehensive docs, SDKs, and responsive support. We obsess over the integration experience.

Transparent Pricing

No hidden fees. Pay for what you use. Real-time credit balance tracking with detailed usage breakdowns.

Continuous Improvement

Models retrain weekly on new data. Your feedback directly improves detection quality for all customers.

Trusted By

Powering Global Brands

Procter & Gamble
Unilever LATAM
Coca-Cola FEMSA
Nestlé Andina
PepsiCo
AB InBev

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