Aura Vision GTM brain

category

market profile

• text: Physical retailers use traffic, dwell, demographics and queue measurements to understand store performance and shopper behavior.
• text: Aura positions its offering around using existing camera networks and a local processing unit, with natural-language access to store metrics.
• text: The company has worked with retailers including O2 Retail and Frasers Group, according to a company database; validate current deployments directly.

personas

• title: Head of retail operations
  pains:

  • Staffing schedules do not consistently match store traffic and service demand.
  • Teams struggle to spot queue and conversion problems across locations quickly.
• title: Retail analytics or business transformation leader
  pains:

  • Footfall can be inflated by employees, distorting conversion.
  • Store teams need to know whether displays and operational changes actually improve performance.
• title: Store technology and innovation lead
  pains:

  • Deploying dedicated sensors and wiring across an estate is expensive and slow.
  • Camera analytics must pass privacy, security and infrastructure compatibility reviews.

icp

• text: Multi-location brick-and-mortar retailers with existing security cameras and teams responsible for store performance; published annual terms start at five locations.
• text: Larger chains seeking an estate-wide rollout of footfall, staffing or conversion analytics; pricing offers a 36-month tier for 20 or more locations.
• text: Specialty retail chains looking to understand journeys and store presentation are a plausible fit, supported by the founder's historical comments.

message pillars

• text: Use existing cameras for store-wide insights.
• text: Deploy without adding costly sensor infrastructure.
• text: Help teams ask questions and act faster.

objections

• objection: Will the system work with our existing cameras?
  response: Check network access and camera compatibility in a representative store pilot before committing to estate deployment.
• objection: What about customer and employee privacy?
  response: Aura describes anonymous analytics and no personal data; review the processing agreement, local requirements and data handling with the retailer's privacy team.
• objection: How do we prove the return across stores?
  response: Use the advertised three-month proof of concept to compare a baseline and a defined outcome such as conversion or staffing coverage.

competitors

• name: RetailNext
  why: Offers in-store retail traffic and shopper analytics.
• name: mAy-I
  why: Uses computer vision for in-store retail analytics.
• name: Dayta AI
  why: Uses AI and video analytics to measure retail activity.

influencers

• name: Daniel Martinho-Corbishley
  url: https://corp.asics.com/en/ventures/article/2019-06-03-2
  why: Cofounder and CEO with domain expertise in computer vision and retail.

keywords

• retail footfall analytics
• camera based people counting
• retail conversion analytics
• store staffing optimization
• in-store queue analytics
• shopper journey heatmaps
• retail CCTV analytics

voice

• direct
• retail operations focused
• outcome oriented

signals

• text: A chain is auditing shopper conversion or discovers employee traffic contaminates footfall counts.
• text: A retailer is planning an estate-wide store analytics rollout and already has camera infrastructure.
• text: Operations leadership is reviewing staffing allocation or queue abandonment across stores.

stack

• CCTV
• computer vision
• edge processing unit

pricing

motion

channels

• SEO
• case studies
• events
• partners

confidence

Founders

• name: Jonathon Blok
  title: Founder
  role: Founder
• name: Jaime Lomeli-Rodriguez
  title: Founder/CTO
  role: CTO
• name: Daniel Martinho-Corbishley
  title: Founder
  role: Founder