Our data specialists use cutting-edge tools to transform raw data into finely tuned training datasets while maintaining ethical best practices. The result: AI models with exceptional accuracy, rapid development cycles, and high performance in real-world scenarios.
Aya Data can acquire top-quality data to fuel your AI models. Our diverse industry network enables us to gather valuable, hard-to-find information through field collection, web scraping, surveys, and other targeted methods.
From generative AI to computer vision, 3D modelling, speech recognition, and predictive analytics, Aya Data delivers bespoke AI solutions to propel your projects forward. We offer end-to-end support, guiding you from initial concept to deployment and ongoing model optimisation.
"We struggled with sales data visualisation using our existing CRM, Aya built a bespoke dashboard to geolocate our sales and display them on a map with a host of other metrics, in incredible detail. This has informed the revision of our entire sales strategy, it has been an invaluable asset."
Rocco Falconer,
CEO,
Demeter Holdings
"Aya Data has performed complex 3D data labelling tasks with our machine learning team at Cydar Medical and helped us accelerate our research and development. We especially value their diligence, attention to detail, focus on high quality, excellent teamwork and communication, and record of delivering projects on time and on budget."
Tom Carrell,
Founder and Chief Medical Officer, Cydar Medical
"We worked with Ayadata to build and label huge datasets; their team was responsive and did their job as expected. They were flexible and accommodated our changing schedule, we appreciate working with their team."
DP WORLD
"Aya's value is in consistently delivering very high quality of work over a long period at a reasonable price, without dropping standards. They deal well with complex use cases requiring pixel perfect precision, fast communication means very little rework. This helps us to bring our models to production faster. They have become an invaluable part of our annotation process."
Thomas Perry,
Annotations Manager, Dogtooth
"We had a fantastic experience working with Aya Data. Their professionalism, responsiveness, and dedication to our business needs was truly impressive. They delivered the request data project accurately, on time, and within the quoted budget. We highly recommend their services."
TIDAL
"We’re pleased to have a positive relationship with the whole Aya Data team. They are diligent and committed to continuous improvement and our teams enjoy working together. Utilising V7’s leading platform and Aya’s dedicated annotator workforce, we're pleased to partner with this team, and are one of a few companies that have actively put themselves forward to become V7 accredited."
Lauren Hale,
Partnerships Director, V7 Labs
"We approached Aya to build a bespoke computer vision solution to monitor seedling germination rates using drones. They developed, trained and deployed the models extremely quickly and with excellent results. I now have a single source of truth dashboard to monitor. The success of our reforestation project."
Chris Rothera,
CEO, Oko Environmental
"We had many photos to label in a short period of time, so we decided to outsource the task. Aya Data did not only meet our time constraints, but also our complicated annotation requirements.
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Richard Parcell,
CTO, Earth Rover
"Aya Data have been a reliable partner, covering a range of different use cases and sectors with us. We know we can trust them with complexity, meeting tough deadlines and most importantly to communicate clearly and effectively at all times."
Marley Jones,
Senior Project Manager, Labelbox
eCommerce is one of the world’s largest industries, forecast to account for 22% of all global retail sales by the end of 2023. The infrastructure required for smooth and efficient eCommerce has evolved to support this growth.Online shops are now extremely intelligent, with recommendation systems, personalization, and marketing automation that targets customers at precisely the right time to drive sales.
Supervised machine learning projects require training data. By learning from training data, a supervised algorithm aims to be able to accurately predict outcomes when exposed to real data.Training data is required for all types of supervised machine learning projects Images, video, LiDAR, and other visual media are annotated for the purposes of computer vision (CV). Audio is annotated and labeled for the purposes of training conversational AIs and technologies with audio sensors.
Data annotation is a basic step,the foundation of all artificial intelligence and machine learning projects. You simply can’t have a functioning ML model that was created without processing data. And there are many types of data annotation, depending on the needs of given projects.That is what will be discussed in this article - the different types of data annotation, from image annotation to text annotation, exploring their processes and significance.
Artificial intelligence (AI) has risen from a fringe concept in sci-fi to one of the most influential technologies conceived. Building systems that can understand visual information has been a cornerstone of AI research and development. This allows machines to ‘see’ and respond to the world around them. The domain of AI related to vision and visual data is called computer vision (CV). Computer vision equips computers with the ability to process, interpret, analyze, and understand visual data.
Audio transcription is the process of converting unstructured audio data, such as recordings of human speech, into structured data. Artificial intelligence (AI) and machine learning (ML) algorithms require structured data to perform various tasks involving human speech, including speech recognition, sentiment analysis, and speaker identification. In short, audio transcription is fundamental for teaching computers to understand spoken language.
Objects are labeled with polygon annotations to create a dataset, which is fed into a supervised CV model. The model learns from the annotations, enabling it to predict and classify objects when exposed to new, unseen data. The physical environment primarily consists of complex shapes with non-linear edges – polygon annotation is considerably more effective at labeling them when compared to bounding boxes, which include a lot of useless information.
As technology continues to advance, the demand for image recognition and object detection has skyrocketed. From self-driving cars to medical imaging, accurate and reliable data annotation is crucial for these AI systems to function effectively. This comprehensive guide covers everything you'll need to understand this component of machine learning.
The large-scale oil palm plantation and the orderly rows of banana trees might look serene, but their management presents complex challenges. From overseeing the hectares of plantation to keeping an eye on the dying plants and restoring the health of wilted ones to manually keeping a count of the trees, numerous problems can arise. Manual labor and traditional methods not only waste resources but also require more time. Plus, there is always room for inaccuracy.
Drones or UAVs (Unmanned Aerial Vehicles) are propelling a revolution in farming and agriculture. In 2019, the FAO published E-Agriculture in Action: Drones for Agriculture, a detailed guide and roadmap of how drones are being used in agriculture, and the best practices for the future. The agricultural drone market is growing and is expected to surpass $4.8 billion by 2024.
In June 2022, a cross-discipline team of researchers at the University of Chicago created an AI model that could predict the location and rate of crime in the city with 90% accuracy. The team utilized citywide crime open data between the years of 2014 and 2016, dividing the city into squares of around 1000ft (300m). The resulting model could predict the square where crime was most likely to occur 1-week in advance.