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Formula E

Enterprise Knowledge Search · Real-time Analytics

Formula E wanted fan experiences that feel immediate, immersive, personalized, and participatory, but its broadcast and event experience was limited by inconsistent onboard video quality, information spread across multiple sources, and race data that viewers could not easily follow through the live broadcast alone.

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Delivered responses in under one second.
Developed in under 30 days.
Less than one second between live vehicle data and the information appearing on screen.
More than 10 real-time data points directly within the in-car broadcast view.

Reported impact

Business outcome

Formula E validated near-real-time AI video enhancement, launched a fan guide with high answer accuracy and sub-second responses, and created an AR broadcast concept that can strengthen fan engagement, reduce routine support pressure, improve broadcast value, and open new sponsorship and revenue opportunities.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth
  • Scalability
  • Speed to Market

Solution

Formula E worked with Lenovo on three AI-enabled fan-experience implementations spanning broadcast quality, conversational discovery, and augmented race storytelling. For AI Video Optimization, actual onboard footage from Jeddah night racing and Monaco daytime racing is ingested from in-car cameras and processed by AI that dynamically corrects glare, haze, exposure, and color shifts, enabling near-real-time enhancement for live broadcast workflows. For the AI Fan Experience Guide, Formula E content about drivers, cars, circuits, rules, history, and venue guidance is prepared, structured, and grounded in a connected knowledge base; Lenovo xIQ performs retrieval and response generation behind a lifelike digital human that serves fast answers to fan questions. For AR broadcast graphics, Formula E live vehicle and race data is processed and integrated into an in-car heads-up display that overlays telemetry such as speed, energy, ATTACK MODE, laps, G-force, position, gaps, and trajectory without obstructing sponsor content.

Data flow

Onboard camera feeds from Formula E races are sent to AI Video Optimization for near-real-time image correction before broadcast use. Formula E content from multiple sources is prepared and structured into a connected knowledge base, then Lenovo xIQ retrieves and grounds answers for the digital-human interface. Separately, live vehicle and race data flows from Formula E broadcast workflows into Lenovo processing and HUD integration, then appears as AR overlays in the in-car broadcast view.

Solution architecture

4 components · 2 layers
  1. Compute
    • AI Video Optimization Processes onboard race footage to correct glare, haze, exposure, and color inconsistencies for near-real-time broadcast enhancement.
    • Lenovo xIQ Handles Formula E knowledge retrieval and response generation behind the digital-human fan experience.
  2. Serving
    • AI Fan Experience Guide Provides a conversational digital-human interface that lets fans ask about Formula E knowledge and venue guidance.
    • AR broadcast graphics Adds live race and vehicle telemetry into the in-car broadcast view as readable augmented overlays.

Architecture clues

  • AR HUD design was centered on a selected car and key race moment while avoiding obstruction of sponsor content.
  • Image enhancement was tested and validated by Formula E for near-real-time broadcast correction.
  • The approach was designed to extend across different circuits, weather conditions, racing environments, races, and venues.
  • The fan-guide system used a connected knowledge base with grounded retrieval and response generation behind a lifelike digital human.
  • Video-enhancement pilot used actual onboard footage from night racing in Jeddah and daytime racing in Monaco.

Evidence from the source

"Developed in under 30 days, the experience achieved more than 95% answer accuracy across the connected knowledge base and delivered responses in under one second."
"In a pilot tested and validated by Formula E, the system completed image enhancement in under three seconds, supporting near-real-time correction without disrupting the at-home viewing experience."
"Lenovo xIQ handled Formula E knowledge retrieval and response generation behind the digital-human experience."
"The heads-up display is designed to surface more than 10 real-time data points directly within the in-car broadcast view, with less than one second between live vehicle data and the information appearing on screen."
"They want experiences that feel immediate, immersive, personalized, and participatory—bringing them closer to the competition and giving them more ways to engage with it."
Transparency Evidence & methodology 100/100 reference value · 93% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence93%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency10/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Lenovo

    Formula E and Lenovo put AI to the test across the future of motorsport - Lenovo StoryHub

    First-party
    https://news.lenovo.com/formula-e-ai-to-the-test-future-of-motorsport/
    Source type
    Vendor-published customer story
    Published
    Oct 13, 2026
    Supports
    • Customer
    • Region
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • Publication date
    • evidence quotes

Date evidence

  • Published on source page Oct 13, 2026 · Formula E · First-party
    Open date source
Not separately sourced Industry, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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