AI Data Solutions
Multimodal Data Collection Services for AI Modal Training
Collect high-quality text, audio, image, video, speech, sensor, and real-world interaction data for training, fine-tuning, and evaluating advanced AI models.
Trusted by leading AI teams to design, collect, enrich, and deliver model-ready datasets across controlled, remote, lab, and in-the-wild environments.
Multimodal Data Collection Services Across Every Modality
Innodata provides end-to-end AI data collection services for text, speech, audio, image, video, and sensor data — customized to your model’s exact requirements.
Egocentric, UMI, Sensor, and Haptic Glove Data
Real world interaction data, first-person video, AR/VR and wearable capture, body-worn sensors, human-object interaction, household/workplace workflows, and environment metadata.
Sample Datasets
- Egocentric Video
- Depth + RGB Capture
- Spatial Navigation
- IMU Sensor
- Hand-Object Interaction
- Wearable Audio-Video
- UMI Data
- Task Completion Sequences
Speech and Audio Data
Studio, remote, and hybrid speech collection for ASR, TTS, voice cloning, speech-to-speech, conversational AI, and emotion modeling.
Sample Datasets
- Customer Service Calls
- Voice Messages
- Wake Words
- Ambient Soundscapes
- Podcast Transcripts
- Speaker Verification
- Telehealth Recordings
- Lecture Recordings
Image, Video, and Multimodal Data
Image and video capture, visual reasoning data, multimodal Q&A, speaker and scene descriptions, object/action labels, and video transcription.
Sample Datasets
- Selfie Camera
- Sports Videos
- Aerial/Drone
- Facial Data
- Surveillance Footage
- Scene Understanding
- Retail Product Images
- Autonomous Vehicle
Text, Document, and Code Data
Prompt response data, domain-specific Q&A, expert-written content, reasoning datasets, evaluation sets, and structured documents.
Sample Datasets
- Prompt Datasets
- Packing Lists
- Expert Q&A
- Receipts
- Bank Statements
- Reasoning Chains
- Invoices
- Utility Bills
- Code Pairs
* All sensitive data types — including biometric, facial, and healthcare-adjacent data — are collected through consent-managed, privacy-compliant workflows with GDPR, HIPAA, and client-specific data governance requirements.
From Collection Design to Model-Ready Delivery
Every program is scoped around your model requirements, collection environment, contributor needs, quality thresholds, and delivery format.
01
Design the dataset
Define modalities, task taxonomy, collection environments, metadata schema, quality thresholds, and delivery format.
02
Provide the right contributors
Utilize SMEs, voice talent, operators, trained data specialists, or domain experts based on language, geography, demographics, expertise, and task requirements.
03
Execute the collection program
Manage studio, remote, lab, onsite, hybrid, and in-the-wild workflows with moderation, support, and protocol adherence.
04
Enrich and validate the data
Add transcripts, labels, timestamps, metadata, QA scores, preference data, evaluation outputs, and validation reports.
05
Deliver training-ready datasets
Provide structured, secure, ingestion-ready data aligned to your model pipeline.
* All sensitive data types — including biometric, facial, and healthcare-adjacent data — are collected through consent-managed, privacy-compliant workflows with GDPR, HIPAA, and client-specific data governance requirements.
Why Innodata for Complex AI Data Collection Programs?
Vetted, accountable experts across healthcare, finance, legal, scientific, technical, and enterprise domains.
Owned and partner capture environments across North America, South America, the Middle East, Europe, and Asia.
Broadcast-quality isolation for speech, voice, audio, and multimodal recording.
Curated global talent pool for multilingual, multi-accent, and expressive voice datasets.
Language coverage for global AI model training, evaluation, and deployment.
Proven delivery scale for synthetic voice AI, TTS, ASR, and conversational AI programs.
Data Collection Programs for Advanced AI Teams
Specialized collection programs designed for the architectures and data requirements shaping the next generation of AI.
Voice AI and Conversational AI
Speech, audio, dialogue, TTS, ASR, voice cloning, emotion, and speech-to-speech data.
Multimodal Foundation Models
Text-image, video-language, audio-video, visual reasoning, scientific image, and multimodal Q&A datasets.
Physical AI, Robotics, and Embodied Systems
Egocentric video, UMI and sensor data, teacher-follower demonstrations, teleoperation data, human-object interaction, task workflows, and environment metadata for vision-language -action (VLA) models, world, models, and wearable AI.
Domain-Specific AI
Expert-generated and expert-reviewed datasets for regulated, technical, scientific, financial, legal, healthcare, and enterprise use cases.
Need Data Faster? Explore Off-the-Shelf Datasets
Skip the collection timeline. Innodata offers pre-built, fully consented datasets available for immediate licensing, collected to the same quality standards as our custom programs.
Every OTS dataset ships with full provenance documentation, consent records, and metadata schemas. Custom extensions available when off-the-shelf coverage isn’t enough.
Egocentric Video Datasets
First-person capture across Household, Blue Collar, and Hobbyist & Craft task domains, with synchronized IMU sensor data, task annotations, and environment metadata.
Multilingual Speech and Audio
Studio-quality recordings across 120+ languages and dialects for ASR, TTS, and voice AI training.
Text and Document Collections
Structured documents, expert Q&A, and domain-specific corpora.
Need Custom Multimodal Data for Model Training?
Tell us what you are building, what data your model needs, and where your current datasets are falling short. Innodata can help scope a pilot, design the collection workflow, and scale the program into production.
Case Studies
Success Stories
See how top companies are transforming their AI initiatives with Innodata’s comprehensive solutions and platforms. Ready to be our next success story?
Frequently Asked Questions About Multimodal Data Collection
What is multimodal data collection for AI?
Multimodal data collection is the process of gathering training data across multiple input types — or modalities — including text, audio, image, video, speech, sensor readings, and real-world interaction data. AI models such as multimodal foundation models, vision-language models, and voice-enabled assistants require carefully curated datasets that combine these modalities in structured, labeled formats. Innodata designs and manages end-to-end multimodal collection programs tailored to each model’s architecture and task requirements.
What types of multimodal data can Innodata collect?
Innodata collects data across four core modality groups:
- Text, document, and code data including prompt-response pairs, expert Q&A, reasoning chains, and structured documents;
- Speech and audio data including studio recordings for ASR, TTS, voice cloning, and conversational AI;
- Image, video, and multimodal data including visual reasoning datasets, scene descriptions, and object/action labels; and
- Egocentric, sensor, and real-world interaction data including first-person video, wearable capture, IMU sensor data, and human-object interaction sequences.
How does Innodata ensure quality in multimodal datasets?
Every collection program follows a five-stage quality pipeline: dataset design with defined taxonomy and quality thresholds; contributor sourcing and vetting; managed collection workflows with protocol adherence and moderation; data enrichment including transcription, labeling, metadata tagging, and QA scoring; and final validation to ensure the delivered dataset is ingestion-ready. Programs run through professional studios, remote capture platforms, and in-the-wild field operations depending on the use case.
What is egocentric data collection, and why is it important for AI?
Egocentric data collection captures first-person perspective data — typically via body-worn cameras, AR/VR headsets, or wearable sensors — to train AI models that understand the world from a human point of view. This data type is critical for robotics (vision-language-action models), wearable AI devices, spatial computing, and autonomous systems that must interpret human-object interactions, navigation tasks, and real-world environments. Innodata has recruited and moderated 1,000+ participants for egocentric collection programs globally.
Can Innodata collect data for specialized domains or regulated industries?
Yes. Innodata builds domain-specific collection programs for healthcare, financial services, legal, scientific research, enterprise software, and other regulated industries. Programs incorporate expert contributors, subject-matter review, privacy-compliant workflows, and data handling protocols aligned with HIPAA, GDPR, and enterprise security requirements.
How long does a multimodal data collection project typically take?
Project timelines vary based on modality complexity, contributor requirements, geographic scope, and volume. A focused pilot program — such as a speech collection in 5 languages or an egocentric video pilot with 50 participants — can be scoped and launched within weeks. Large-scale production programs spanning 100+ locales or thousands of participants are phased to align with model training roadmaps. Innodata provides dedicated program management and milestone-based delivery schedules for every engagement.Project timelines vary based on modality complexity, contributor requirements, geographic scope, and volume. A focused pilot program — such as a speech collection in 5 languages or an egocentric video pilot with 50 participants — can be scoped and launched within weeks. Large-scale production programs spanning 100+ locales or thousands of participants are phased to align with model training roadmaps. Innodata provides dedicated program management and milestone-based delivery schedules for every engagement.
What deliverables does Innodata provide at the end of a collection program?
Deliverables are structured to be ingestion-ready for the client’s model pipeline. Standard outputs include raw and processed data files, transcripts and annotations, timestamped metadata, QA and validation reports, contributor demographics, preference and evaluation data, and secure transfer via client-approved protocols. All formats and schemas are agreed during the design phase to ensure seamless integration.
Does Innodata offer synthetic data generation in addition to real-world collection?
Yes. Where real-world data is scarce, restricted, or insufficient to cover edge cases, Innodata provides synthetic data generation services that produce statistically accurate, privacy-safe training data across text, image, audio, and video modalities. Synthetic data can augment real-world datasets, accelerate model development timelines, and fill coverage gaps without compromising quality.Yes. Where real-world data is scarce, restricted, or insufficient to cover edge cases, Innodata provides synthetic data generation services that produce statistically accurate, privacy-safe training data across text, image, audio, and video modalities. Synthetic data can augment real-world datasets, accelerate model development timelines, and fill coverage gaps without compromising quality.
What is physical AI training data?
Physical AI training data is real-world interaction data used to train models that perceive and act in physical environments — including robots, wearables, and embodied agents. It typically combines egocentric video, IMU and depth sensor streams, teacher-follower demonstrations, teleoperation logs, and human-object interaction sequences, aligned and annotated for vision-language-action (VLA) and world model training. Innodata collects physical AI data through lab, in-facility, in-home, and in-the-wild programs with synchronized multi-sensor capture.Physical AI training data is real-world interaction data used to train models that perceive and act in physical environments — including robots, wearables, and embodied agents. It typically combines egocentric video, IMU and depth sensor streams, teacher-follower demonstrations, teleoperation logs, and human-object interaction sequences, aligned and annotated for vision-language-action (VLA) and world model training. Innodata collects physical AI data through lab, in-facility, in-home, and in-the-wild programs with synchronized multi-sensor capture.
Should I license an off-the-shelf dataset or commission custom data collection?
Off-the-shelf datasets are the faster, lower-cost option when your model needs broad coverage of common tasks, languages, or environments — they can be licensed and delivered in days. Custom collection is the right choice when your model requires specific devices, demographics, task taxonomies, edge cases, or proprietary formats that existing datasets don’t cover. Many Innodata clients combine both: an OTS dataset to start training immediately, with a custom program scoped in parallel to fill the gaps.
