Data Collection for AI & ML – Images, Video, Audio, and Text Datasets
We offer customized data collection for ai & ml solutions to fit the needs of your AI program. We can deliver massive amounts of high-quality training data over images, video, speech, audio, and text.
Quality data collection for al & ml and artificial intelligence data collection are essential to build accurate data models. With years of experience in big data and using artificial intelligence, we can provide specific, tailored, balanced datasets, including images, video, speech, audio, and text, for training your algorithms.
We provide professional analytics, ai data collection, and data processing services as a long-term partner, assuming both risk and accountability that align with your strategic vision. By leveraging our extensive industry understanding and advanced capabilities, we turn challenges into practical intelligence via big data and artificial intelligence.
Many firms simply cannot extract value from their data. Our flexible models bridge the gap to increase efficiency, reduce costs, and enable the success of your big data ai projects or programs. Our models also include high-quality image data collection for ai, and we can create customized datasets to ensure the reliable outcomes of machine learning.
Text Data Collection
Our text data collection can be leveraged for idea and product development, branding, shopping research involving patients and experts, and clinical or market studies. Adding value-added depth of audience insight aids in effective AI and ML data solutions to meet the business challenges you face.
Receipt Data Collection
Train AI with receipt data collection to identify invoices, bills, and multilingual variations while using next-generation OCR technology to create all types of structured datasets for AI and ML training.
Encourage machine learning model development with ticket data collection, using young travel tickets containing rich OCR text datasets for AI and ML training.
EHR Data and Notes Transcription
Allow healthcare AI models to read and utilize medical records and note notes to produce clever clinical workflow automations.
Document Data Collection
Create intelligent models that recognize official documents like credit cards, property rights deeds, license, visas, and other proprietary paper forms.
Handwritten Data Transcription
Create AI tools to transcribe or transcribe and understand handwritten notes or historic documents with the fewest possible errors.
Intent Variation Data
Train natural language processing (NLP) systems on intent variation data that captures user intent, emotion, and various language problems.
Chatbot Data for Training
Use context to develop high-quality chatbot training text data for industry-specific and real-time conversational AI models.
OCR Data Collection and Model Training
Support the training of OCR model systems with trusted or reliable datasets for documents that contain image, text, and character recognition.Social Media & Online Data Collection
We collect all the text, audio, video, and image data from Facebook, Twitter, YouTube, and blogs—real user opinions, reviews, and interactions that train AI models with honest, social-driven data.
Social Media Posts
Information from social media posts (Facebook, Twitter, LinkedIn, Instagram) text, hashtags, responses, and user comments.
Online Reviews & Ratings
Feedback received through review sites (Amazon, Yelp, TripAdvisor and vehicle review sites) that contains opinions, sentiment, and satisfaction levels.
Forum & Community Discussions
Thread and response generated from “community” or forum-like sites (Reddit, Quora, and niche forums) that can capture patterns, issues, and public sentiments.
Blog & Article Comments
Responses and comments from users on media sites/blog posts to analyse user reactions, feedback, and engagement.
Visual & Other Media
Videos, images and audio that are shared publicly by users on sites like YouTube can be useful to support the design of multimodal AI systems.
Shopping Behaviour Data
Data from eCommerce platforms provides insight into users shopping behaviour as it relates to their preferences for products.
Chat & Messaging Data
Completes responses from user completion in customer service chats, chatbot, and user inputs from messaging apps to help train conversational AI.
Polls & Survey Responses (open-ended)
User submitted publicly available responses to open-ended questions on social media and surveys.Speech Data Collection
We gather multilingual speech data from participants across the globe, to train voice-enabled AI; all while supporting projects regardless of size but doing so as quickly and accurately as possible.
Typical Conversation Speech
We are collecting natural and ideal real-world recordings of conversations with two or more speakers discussing a daily living topic for conversational AI training.
Call Centre Speech
We are collecting real-world recordings of customer service agents and customers while having calls to create audio data to train AI customer support models.
Wake Word Speech
We are gathering different samples of wake words in different languages and accents to train voice activation systems.
Voice Assistant Commands
We are collecting examples of voice commands in many dialects, languages, and accents to train AI voice assistants.
Scripted Monologue Speech
We are collecting recordings of spoken audio using scripted monologs with single speakers to provide consistent input to voice AI.
Image Description Speech
We are recording speech where speakers are simply describing images to audit into multimodal AI training for future AI models that combine visual and audio inputs.Image Data Collection
We offer high quality, diverse datasets of pictures that includes traffic and warning signs, medical images, and satellite images specifically for effective ML training and accurate AI models.
Facial Image Data Collection
Collecting images of faces from a demographic base to improve accuracy in facial recognition.
Image Data Collection for Documents
Providing quality images of documents for enhanced automated extraction of text.
Human Gesture Data Collection
Collecting motion and gesture data to train AI models for recognizing behaviours.
Healthcare Image Data Collection
Collecting medical images such as CT scans, MRIs, and X-rays – across multi-specialties.
Hand Gesture Data Collection
Collecting images of hand gestures across age, sex, and ethnicity related to gesture recognition.Video-AI Data Collection for Computer Vision
We collect high-quality video and audio data from traffic, biometrics and surveillance for training computer vision and ML models.
Vehicle video collection
HD videos of vehicles in different scenarios–roadways, intersections, parking lots–to improve AI detection and tracking capabilities.
Image Data Collection for Objects
Collecting various images of objects to enhance object detection in AI/ML.
Drone Video Collection
Favourite videos of unique aerial and surveillance videos depicting a variety of scenery for enhanced security and real-time AI evaluation.
Human Face Video Collection
Video of human faces videos displaying various facial movements and activities to improve recognition and analysis models.
Object Video Collection
Video of multiple types of moving objects in various environments to help improve AI models detection, auditing and tracking capabilities.Our Industries
At Statswork we work with organizations to reduce costs, increase efficiencies, and enhance customer service using advanced artificial intelligence and machine learning algorithm development.
Statswork provides professional quality customized data collection solutions specifically for AI and machine learning purposes. Here are the reasons to trust us to solve that for you:
Domain Exposure
Our decades of experience collecting big data and working with AI means we will collect data relevant to your ML models with considerable accuracy.
Customized Solutions:
Custom data collection within text, speech, images etc. to fulfil your AI requirements.
Global Access
Our brand awareness and reputation allows us to grow huge and diverse datasets, collected in all parts of the world for increased model reliability and accuracy.
Quality Control
We validate and clean your data so you, and your team, can have the best quality data to improve the training of any algorithms.
Scalability
Our services are flexible enough to help with any size project. Whether it’s quick turnaround work or large amounts of data, we have you covered.
Our process delivers precise, high-quality data tailored for AI and ML, ensuring maximum impact and support at every step.
1. Requirements Analysis
We will work closely with you to analyse your project needs for AI and ML data in a collaborative effort to establish specific goals for what data we should be collecting.
2. Data Sourcing & Collection
Based on your stated need for data we will collect rich raw data from a variety of sources to provide a diverse collection of high-quality data that meets the requirements of your project.
3. Cleaning and Preprocessing Data
We will clean and preprocess your dataset and make sure it is complete, reliable, consistent, and suitable for training models for AI and ML.
4. Quality Assurance
The data is validated and quality checked as part of a rigorous process to ensure the integrity and relevance of the data.
5. Delivery & Ongoing Support
The processed data will be delivered to you according to the agreed timescales and we will continue to support you in your efforts to get value from it (and therefore your AI & ML solutions).
Data Collection | Article
The integration of big data and AI is making a significant difference in the…
Data Entry | Article
Bioinformatics plays a significant role to analyze the data sets by using AI…
Data Abstraction | Article
Statistical techniques like Time Series Analysis allow the examination of an…
FAQs – AI & ML Data Collection
1. What type of data collection is used in AI?
AI uses both primary and secondary data collection methods, including sensors, surveys, databases, web scraping, transaction records, and IoT devices. The collected data may be structured, semi-structured, or unstructured depending on the application. High-quality data collection is essential for training accurate and reliable AI models.
2. Why is data collection important for AI and ML?
Data collection provides the foundation for training, validating, and testing artificial intelligence and machine learning models. High-quality, diverse, and representative datasets improve model accuracy, reduce bias, and enhance prediction performance. Without reliable data, AI and ML systems cannot produce meaningful or trustworthy results.
3. What is data collection in ML?
Data collection in machine learning is the process of gathering relevant data that will be used to train and evaluate machine learning algorithms. This data may come from databases, sensors, user interactions, or publicly available datasets. Proper data collection ensures that ML models learn patterns accurately and perform effectively.
4. Which database is best for AI and ML?
The best database for AI and ML depends on the project requirements and data type. Popular options include PostgreSQL, MongoDB, MySQL, Apache Cassandra, and cloud databases such as Google BigQuery and Amazon Redshift. The ideal database should support scalability, fast data processing, and seamless integration with AI and ML tools.
5. What are the 7 types of AI?
The seven commonly recognized types of AI include Reactive Machines, Limited Memory AI, Theory of Mind AI, Self-Aware AI, Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI). These categories represent different levels of intelligence and capabilities, ranging from task-specific systems to highly advanced theoretical models. Most AI applications used today fall under Artificial Narrow Intelligence.
6. What are the 7 types of data?
The seven common types of data include qualitative, quantitative, categorical, numerical, ordinal, nominal, and binary data. Each type has unique characteristics and is analyzed using different statistical or analytical methods. Selecting the appropriate data type is essential for accurate analysis and reliable research outcomes.