Top Multimodal Annotation Companies: Tools, Services, and Compliance

What to Look for in Multimodal Annotation Companies

For multimodal AI projects, you need data annotation vendors that can label links across them: text tied to an image region, transcripts synced to video, 3D fused with camera frames. That cross-modal alignment is what large multimodal models (LMMs) learn from.

Start by picking the right operating model for your team: 

• Platform-first (you run labeling)
• Managed service (they run it)
• Hybrid (tool + workforce)

Map this choice to your DataOps maturity, control needs, and timelines. After that, you can use this handy checklist to compare top multimodal annotation companies:

• Modalities & links: Support for image, video, audio, text, docs, LiDAR/3D, DICOM, and cross-modal annotation (e.g. image+text, video+audio, sensor fusion).
• Workforce model: Tool-only, service-led, or hybrid. Prioritize burst scale, domain expertise, and transparency into who does the work.
• QA process: Gold sets, layered review, IAA scores, model-in-the-loop checks. Ask for QA samples from similar use cases.
• Security & compliance: SOC 2/ISO 27001 minimum; HIPAA, GDPR, CCPA if relevant; VPC/on-prem for sensitive data.
• Pricing clarity: Per-label/hour/unit rates, hidden fees, rework costs. Prefer vendors with clear data annotation pricing and free pilots.

These criteria keep focus on what matters to ML teams: reliable cross-modal labels, predictable costs, and compliance you can defend. The best vendors can help support diverse use cases, whether you're training foundation models or niche types of LLMs for domain-specific tasks.

Comparing Top Multimodal Annotation Companies

Choosing the best data annotation company for multimodal tasks in AI often starts with a side-by-side look. With models like Gemini and GPT-4o leading the next wave of LMMs, accurate multimodal annotation is more essential than ever. 

In a Gemini vs ChatGPT comparison, both rely on tightly aligned text, image, and audio labels, making the quality of your upstream data a real differentiator.

This table breaks down 10 top-rated multimodal annotation companies by their operational model, strengths, and best-fit use cases, helping you shortlist based on flexibility, compliance, scalability, or domain expertise.

Hybrid vendors like Label Your Data and Scale AI strike a balance between tooling and workforce. Tool-first companies like Labelbox or Encord cater to in-house ML teams with more control. Workforce-first providers like Appen, TELUS Intl., and iMerit deliver scale or domain expertise with less technical overhead. 

Your best-fit partner depends on your team’s technical maturity, compliance needs, and budget sensitivity. Use this snapshot to narrow down who to pilot with.

Top 10 Multimodal Annotation Companies for ML Teams

We’ve profiled each multimodal data annotation vendor based on real-world strengths, service models, quality controls, and compliance maturity. 

Whether you’re looking to label radiology scans, LiDAR+video sequences, or transcript-aligned interviews, this guide helps you compare vendors for multimodal machine learning tasks.

Label Your Data

Label Your Data is a hybrid vendor combining managed data annotation services with a flexible data annotation platform. Unlike many tool-first companies, it’s designed for teams that need multimodal coverage without sacrificing accuracy, security, or support. The company supports projects across computer vision, NLP, audio, document annotation, and 3D point clouds, including complex use cases like video-text alignment and OCR extraction with QA loops.

It’s consistently ranked as one of the top-rated multimodal annotation companies: 4.9 on G2 and 5.0 on Clutch, with clients frequently citing transparent pricing, free pilot projects, and highly responsive support. The team works across regulated domains like healthcare, finance, and autonomous systems, offering ISO 27001, HIPAA, and GDPR compliance by default.

Label Your Data pros

• Transparent per-object pricing with no lock-in
• Free pilot and high accuracy (98%+) guarantees
• SOC 2, ISO 27001, HIPAA, and GDPR compliance
• Multimodal and tool-agnostic workflows
• G2: 4.9 (15 reviews); Clutch: 5.0 (26 reviews)

Label Your Data cons

• No self-serve sandbox for rapid experimentation
• Better suited for teams who want hands-on collaboration, not hands-off automation

Label Your Data stands out as the best multimodal annotation company for ML teams that need a secure, high-quality partner who can adapt to shifting scopes, support multiple modalities, and maintain production-level QA. 

Check the in-depth Label Your Data company review to learn more about the vendor.

Scale AI

Scale AI is a hybrid provider known for its unmatched scale, massive human-in-the-loop infrastructure, and specialization in high-complexity modalities like LiDAR, video, and synthetic data generation. Its core platform supports advanced workflow customization, and its marketplace approach lets customers combine tools, QA logic, and human review at scale. It's frequently chosen for autonomous driving, defense, and government AI projects that require extreme volume and precision.

However, Scale’s premium pricing and lack of transparent cost structure can be a challenge for smaller teams. Many enterprise clients note a steep onboarding process and sales cycle. That said, if your team works in high-stakes, sensor-rich environments and has the budget to match, Scale remains one of the strongest options for complex multimodal labeling pipelines.

Scale AI pros

• Exceptional support for LiDAR, 3D, synthetic, and sensor fusion data
• Advanced QA logic and customizable workflows
• Strong fit for defense, AV, and government AI projects

Scale AI cons

• Opaque pricing, long procurement timelines
• Not suitable for smaller, cost-conscious teams
• Enterprise-only access to key features
• Potential data concerns after Meta deal

Read the full Scale AI review for an in-depth look at the vendor. You can also explore the verified list of Scale AI competitors to compare all options.

iMerit

iMerit is a managed service provider focused on delivering high-quality human-labeled data with deep domain expertise in healthcare, autonomous vehicles, and geospatial AI. The company is known for rigorous annotation workflows, in-house annotator training programs, and strong compliance with standards like ISO 27001 and HIPAA. It’s a preferred partner for regulated industries that require precision, auditability, and full annotation pipeline ownership.

While iMerit offers strong quality and domain specialization, it’s slower to onboard and generally more expensive than other providers. Its fully managed model gives less tooling control to ML teams and may not suit teams that want to iterate labeling workflows rapidly in-house.

iMerit pros

• Domain-trained workforce for medical, AV, and geospatial AI
• Full-service workflows with human-in-the-loop QA
• Strong security, compliance, and project management support

iMerit cons

• Premium pricing and slower ramp-up
• No self-serve platform or fine-grained workflow control
• Less transparency into real-time labeler performance

Explore our in-depth iMerit review to see if it's the right fit for your AI projects.

Labelbox

Labelbox is a platform-first vendor offering a powerful suite of annotation tools for image, video, text, and geospatial data. It’s designed for in-house teams that want to build and manage labeling workflows themselves, with automation features like model-assisted labeling, SDK/API integrations, and analytics dashboards. Its usage-based pricing via Labelbox Units (LBU) makes it scalable for many enterprise use cases, if managed carefully.

That said, Labelbox has a learning curve, and the LBU model can become costly for large, long-running projects. Compared to other Labelbox competitors, this company is best suited for teams with strong internal DataOps who want tooling flexibility but don’t need vendor-managed labor or QA.

Labelbox pros

• Robust platform with automation and active learning features
• Integrates with cloud storage and ML stacks
• Custom ontologies, SDK, and model-in-the-loop support

Labelbox cons

• Steep learning curve for complex workflows
• Usage-based pricing can spike unexpectedly
• No managed annotation workforce or domain-trained QA

SuperAnnotate

SuperAnnotate is a hybrid annotation company combining an advanced platform with access to a vetted workforce. It supports a wide range of modalities (image, video, text, LiDAR, and more) with features like auto-segmentation (via SAM), GPT-4-assisted labeling, and QA workflows. The platform is frequently ranked among the top in G2, praised for its intuitive UI and velocity on computer vision tasks.

While SuperAnnotate offers strong tooling, its more advanced features have a learning curve, and buyers have flagged pricing transparency as an issue. It's a strong fit for ML teams that want platform flexibility without giving up access to on-demand annotation support.

SuperAnnotate pros

• Top-rated platform on G2 for CV annotation
• GPT/SAM-powered automation and human-in-the-loop QA
• Hybrid model: powerful tool plus optional workforce

SuperAnnotate cons

• Advanced features can be hard to configure for new users
• Pricing and usage terms may be unclear upfront
• Support for niche data types (e.g., DICOM) may require customization

Read the full SuperAnnotate review for pricing, strengths, and ideal use cases. 

TELUS International

TELUS International is a fully managed data annotation provider with a global workforce and deep enterprise roots. Known for handling large-scale, multilingual projects, it offers multimodal annotation for text, images, audio, video, and more. The company brings strong compliance (SOC 2, ISO 27001, GDPR) and has extensive experience across sectors like healthcare, e-commerce, and financial services. Its dedicated delivery teams, SLAs, and quality controls make it a go-to for regulated industries.

However, TELUS's size comes with trade-offs. Some buyers report slower onboarding due to corporate layers, and quality can vary across distributed annotation teams. TELUS is best for enterprise AI teams that need a reliable, compliant partner with global reach, and are willing to navigate a more formal engagement process.

TELUS International pros

• Global workforce with 500+ supported languages
• Strong compliance posture and industry experience
• Scalable managed service with quality oversight

TELUS International cons

• Bureaucratic onboarding and procurement
• Less transparency into individual annotator performance
• Slower ramp-up for smaller, fast-moving projects

Appen

Appen is one of the longest-standing players in the data annotation space, offering a mix of crowdsourced and managed services. Its multimodal support covers text, image, audio, video, and sensor data, with particular strength in linguistic diversity – spanning 170+ countries and 500+ languages. Appen is often chosen for global projects needing multilingual coverage, and it offers a hybrid model with platform tooling and human workforce at scale.

Despite its reach, Appen has faced criticism for inconsistent annotation quality and lack of transparency. Some teams report challenges with annotator engagement, quality control, and communication. Appen is best suited for teams prioritizing scale and language breadth over granular control or advanced workflow customization.

Appen pros

• Unmatched global reach and language coverage
• Long-standing experience in multimodal AI projects
• Hybrid model supports platform + workforce

Appen cons

• Inconsistent quality across projects
• Limited transparency and QA visibility
• Worker-side grievances and retention issues

Dive into our Appen company review for insights into services and scale.

Encord

Encord is a platform-first multimodal annotation company with strong adoption among healthcare, biotech, and robotics teams. Its platform supports video, image, DICOM (medical imaging), LiDAR, and 3D data, making it a good fit for high-complexity CV pipelines. Encord emphasizes AI-assisted labeling, automated QA, and native support for collaborative workflows. It’s also known for its advanced support of ontologies and robust API integrations.

However, Encord’s advanced tooling can be overwhelming for smaller teams or first-time users. While it does offer services, its pricing and onboarding are geared toward enterprise use cases. For teams that can fully leverage its platform capabilities, the vendor delivers powerful tools for building high-quality, domain-specific datasets.

Encord pros

• Strong in healthcare (DICOM), robotics, and 3D
• Advanced automation and QA tools
• Ontology support and API integrations

Encord cons

• Steep learning curve
• Enterprise-focused pricing
• Limited service-first support for smaller teams

CloudFactory

CloudFactory is a managed workforce provider focused on human-in-the-loop (HITL) labeling and ethical sourcing. Known for its global team model and strong presence in developing markets, CloudFactory supports multimodal annotation across image, text, video, and audio. Its strength lies in projects that benefit from consistent, trained annotator teams—particularly when ethical or mission-driven sourcing matters. Tool-agnostic by design, CloudFactory integrates with customer platforms or can use partner tools.

It’s not a self-serve platform, and onboarding can take time. While it delivers high-quality work, project management consistency and communication can vary depending on team size and geography. For teams that value ethical labor practices and need to scale without building internal teams, CloudFactory is a dependable partner.

CloudFactory pros

• Human-in-the-loop with dedicated teams
• Tool-agnostic and highly flexible
• Ethical labor sourcing and social impact mission

CloudFactory cons

• Not a self-serve platform
• Onboarding may be slower
• Varies in project management quality

Check this CloudFactory review for pros, cons, and key features.

Cogito Tech

Cogito Tech is a specialized managed service provider with deep expertise in complex annotation tasks for healthcare, insurance, finance, and government. Its focus is on precision, domain-trained teams, and full-service delivery. Cogito supports multimodal projects involving text, audio, image, video, and DICOM formats, offering QA processes tailored to high-risk applications. Clients often cite its high accuracy, responsiveness, and willingness to work within tight compliance constraints.

However, Cogito does not offer a public-facing platform or self-serve tools, which may limit transparency and direct workflow control. Scalability for large volumes may also require coordination with partner tools or third-party platforms. Still, for buyers seeking a white-glove service with consistent output and industry-specific expertise, Cogito remains a solid contender.

Cogito Tech pros

• High accuracy for regulated domains
• Custom QA workflows and flexible onboarding
• Strong compliance posture (ISO, HIPAA, etc.)

Cogito Tech cons

• No client-facing platform
• Less transparent pricing
• Requires external tooling for scaling

How to Choose the Right Partner for Multimodal Projects

There’s no one-size-fits-all annotation vendor. The best choice depends on your team’s resources, project scope, data types, and need for control. Use this breakdown to match vendor types to your workflow maturity:

Startups & research teams often benefit from platform-first or hybrid vendors like Labelbox, Encord, or SuperAnnotate. These options allow technical teams to stay hands-on while automating repetitive work and scaling cost-effectively. Look for tools with active communities, SDKs, and API-first design.

Mid-sized ML teams typically seek balance. Hybrid vendors such as Label Your Data, Scale AI, or SuperAnnotate offer both tool access and managed workforces, letting you scale up quickly without overcommitting internal resources. QA, burst capacity, and pilot options matter here.

Large enterprises or regulated industries need proven security, accuracy guarantees, and compliance certifications. Vendors like Label Your Data, iMerit, TELUS International, or Cogito Tech offer managed services with industry-trained annotators and multi-layered QA. They’re ideal when stakes are high and scale is non-negotiable.

No matter your choice, always run a pilot first (risk-free if you choose Label Your Data). Testing vendor performance on a 1-5% slice of machine learning datasets gives you real signal on label quality, collaboration, and turnaround times before you scale.

Expert Tips on Handling Multimodal Annotation Challenges

Your machine learning algorithm is only as strong as the training data it learns from. Inconsistent cross-modal labeling can quietly erode performance. Even the most advanced annotation companies face the same core hurdles when scaling multimodal projects.

We asked industry leaders to share what goes wrong and how they fix it in their projects.

Catch errors with cross-modal validation

In multimodal projects, the biggest hurdle I see is keeping annotations consistent across text, audio, and images. One mismatched label can cascade into compliance risks. I’ve found the best way forward is to build secure, role-based workflows with automated validation checks to catch errors early without jeopardizing privacy.

Tom Terronez CEO, Medix Dental IT

Reduce bias with diverse teams

Bias can slip into multimodal data because text, images, and audio each invite different interpretations. Teams can reduce this risk by building diversity into the annotator pool and using clear, standardized guidelines. That combination leads to richer, fairer datasets and builds stronger trust in the AI systems built on them.

Andy Wang Marketing Manager, Skywork.ai

Fix sync and QA with unified tools

Multimodal annotation introduces three major challenges: alignment across modalities, inconsistent quality control, and fragmented tool chains. In one project, 34% of annotations had sync errors and had to be redone. Teams should invest in integrated tools, cross-modal guidelines, and annotators trained to work across modalities, not just one.

Raj Baruah Co Founder, VoiceAIWrapper

About Label Your Data

If you choose to delegate multimodal annotation, run a free data pilot with Label Your Data. Our outsourcing strategy has helped many companies scale their ML projects. Here’s why:

No Commitment

Check our performance based on a free trial

Flexible Pricing

Pay per labeled object or per annotation hour

Tool-Agnostic

Working with every annotation tool, even your custom tools

Data Compliance

Work with a data-certified vendor: PCI DSS Level 1, ISO:2700, GDPR, CCPA

FAQ

What is multimodal annotation?

Multimodal annotation refers to labeling not just multiple data types, like text, images, audio, and video, but also the relationships across them. For example, linking a transcript segment to a video timestamp or annotating objects in LiDAR data aligned with camera views. It’s essential for training LMMs (Large Multimodal Models) like GPT-4o or Gemini.

What is the difference between multimodal and OCR?

OCR (Optical Character Recognition) extracts text from images or scanned documents and is a single-modality task. Multimodal annotation involves labeling multiple data types together, often including OCR outputs as one layer among others (e.g., bounding boxes, transcript alignment, visual labels).

How to choose a multimodal data annotation company?

• Match the vendor to your team size and control needs.

• Check if they support all required data modalities.

• Review their QA process for accuracy and reliability.

• Look for transparent, predictable pricing.

• Run a pilot to test fit and quality

What industries need multimodal annotation the most?

Sectors like autonomous vehicles, healthcare, surveillance, and retail rely heavily on multimodal data (e.g., syncing LiDAR and video, linking medical imaging and reports, or combining product images with reviews for AI models).

What’s the difference between hybrid and platform-first vendors?

Hybrid vendors (e.g., Label Your Data, Scale AI) combine platform tooling with access to a managed labeling workforce. Platform-first vendors (e.g., Labelbox, Encord) provide the tools, while bringing the team. Hybrid models offer more support; platform-first models give more control.