As automakers look to reign in manufacturing costs and meet soaring consumer expectations, our data services fuel cutting-edge solutions to the industry’s most pressing challenges.
The rate at which consumers report issues/malfunctions when first using voice-enabled assistants in automobiles.
Use case 1
Enhance in-car infotainment
The cost of just 30 minutes of downtime for the average automotive manufacturer.
Use case 2
Reduce maintenance unpredictability
Use case 1
Enhance in-car infotainment with speech
Our quality-focused approach to speech collection focuses on proper distribution to defeat algorithmic bias so that every user is heard.
Customers are willing to spend up to 15% more on a car for a state-of-the-art infotainment system.
Nearly ¾ of millennials consider enhanced infotainment features as ‘must-haves’ when purchasing new vehicles.
Spontaneous speech collection
Train in-car virtual assistants to understand real human speech.
Build the foundation of speech recognition models with highly-accurate transcriptions.
Collect valuable insight on the naturalness of a virtual assistant’s utterances.
Use case 2
Develop predictive maintenance
We teach models to process a wide array of input types so they can monitor critical assets and flag small issues before they snowball into critical failures.
Automakers with predictive maintenance programs in place experience 160 fewer hours of equipment downtime per year.
Market analysts expect predictive maintenance will save manufacturers between $240-$630 billion per year in the next decade.
Audio collection & validation
Train models to listen for mechanical anomalies.
Multimodal data collection
Enable models to process a large range of datatypes to understand overall asset health.
Image collection & validation
Teach models to watch for small signs of minor malfunctions.
Use case 3
Build autonomous vehicles
Our computer vision and multimodal workflows build the foundations for automated driver assistance.
Experts project a market value of $1 trillion for autonomous vehicles in supply-chain logistics.
Autonomous vehicles stand to lower freight-shipping costs by as much as 40% per kilometer.
Train models to recognize and follow moving objects.
Enable models to identify certain objects present in images.
Teach models to sift through large swaths of images/videos.
Mastercard’s R&D Labs needed unique, multi-lingual text data that covered 20 designated payment scenarios in English and Spanish, and they needed it fast.
Keeping a nation’s lights on means constantly inspecting electricity poles for damage. EDP partnered with DefinedCrowd to improve Asset Performance Management processes.
A global electronics maker came to DefinedCrowd with the goal of building more inclusive facial recognition models, requiring accurately annotated images with highly specific criteria.
A Fortune 500 Tech company needed comprehensive speech training data in French that accounted for a wide range of dialects, requiring diverse data in terms of age, gender and regional dialects.
A visionary Fortune 500 Tech company leveraged sentiment analysis models to dig beyond surface-level understandings to extract granular-level insights.
Smart companies see the pile of unstructured text floating through the digital realm as a strategic goldmine of consumer insights.