Best Alternatives to TELUS International for Data Projects

When businesses begin their AI journey, they often start with well-known data service providers that can handle large-scale projects and offer global reach. For many, TELUS International (formerly Lionbridge AI) has been that partner, offering support in data collection, annotation, and linguistic expertise.

But as AI use cases grow more sophisticated, the requirements for data projects evolve too. What worked well for initial pilots may not always fit when you’re building more advanced models, managing sensitive data, or scaling at speed. Suddenly, businesses find themselves needing more flexibility, more specialised datasets, or stronger compliance frameworks.

That’s when exploring TELUS International alternatives makes sense. The aim isn’t to replace what worked before, but to find a partner that aligns better with today’s needs and tomorrow’s ambitions.

In this blog, we’ll highlight some of the best providers helping enterprises deliver impactful AI data projects.

1. Twine AI

Twine AI works as a hands-on partner rather than just a platform. Twine AI takes a hands-on approach to data projects, working closely with clients to deliver custom datasets across audio, text, image, and video. Rather than relying on open crowds, it curates a trusted global network of contributors and manages every stage of the process, from recruitment and consent through to annotation and delivery. This makes it especially strong in voice AI, computer vision and multilingual datasets, where accuracy and demographic diversity are critical. For companies working in regulated industries, Twine also embeds compliance frameworks like GDPR and CCPA directly into workflows.

• End-to-end service: From recruiting contributors, managing consent, and collecting raw data to annotating and delivering final datasets.
• Global reach: Curated network of contributors spanning multiple countries, languages, and demographics.
• Quality-first: Multi-step review pipelines reduce noise and bias.
• Key advantage: Strong in voice AI, computer vision and multilingual datasets, where accent, tone, and context matter for accuracy.

2. LXT + Clickworker

Following its acquisition of Clickworker, LXT now combines a managed AI data service model with access to over six million contributors worldwide. This merger allows enterprises to launch massive multilingual collection and annotation projects quickly while still benefiting from enterprise-grade quality and security standards. For global organisations looking to scale fast across dozens of languages or regions, this combination is one of the most effective ways to get there.

• Merger synergy: LXT acquired Clickworker in late 2024, combining managed AI data services with a crowd of 6M+ contributors.
• Breadth: Multilingual data collection, annotation, and validation at very large scale.
• Strength: Quick access to scale for enterprises running massive text or voice projects.
• Use case: When you need thousands of contributors rapidly across 45+ languages.

3. Defined.ai

Defined.ai offers a unique mix of a data marketplace and custom collection services. Companies can purchase ready-made datasets, such as speech corpora or chat transcripts, or commission bespoke projects if their needs are highly specific. Its particular strength lies in conversational AI and NLP, making it a popular choice for businesses building chatbots, voice assistants, or multilingual dialogue systems.

• Marketplace model: Buy ready-made datasets (speech corpora, chat transcripts, sentiment data) instantly.
• Custom collection: Tailor datasets to your needs if off-the-shelf isn’t enough.
• Focus: Speech and NLP, especially for conversational AI.
• Unique angle: Combines speed of pre-built assets with flexibility of custom projects.

4. Labelbox

Unlike many providers, Labelbox positions itself as a data infrastructure company. Its platform is designed for enterprises that want to bring annotation in-house, providing the tools, APIs, and automation features needed to build and manage large-scale labelling pipelines. For organisations that prefer to maintain direct control over their data operations — while still having the option to tap into external labellers, Labelbox offers a strong foundation.

• What it is: An enterprise-grade platform for managing annotation pipelines.
• Features: Automation-assisted labelling, data curation, model-assisted workflows, and QA dashboards.
• Deployment: Available cloud-hosted or on-premise for data-sensitive industries.
• Strength: Lets enterprises build their own annotation ecosystem, integrating with existing ML ops.

5. SuperAnnotate

SuperAnnotate is best known in the computer vision space, with a platform built for image and video annotation. Its tools make it easy for distributed teams to collaborate on complex labelling tasks while maintaining high quality through built-in QA features. For companies in areas like autonomous driving, medical imaging, or retail AI, SuperAnnotate provides a workflow-driven solution that balances automation with human judgment.

• Focus: Computer vision, image, and video annotation.
• Features: Model-in-the-loop workflows, project collaboration, annotation QA, and multi-team visibility.
• Strength: Excellent for vision-heavy pipelines like autonomous driving, medical imaging, and retail AI.

6. Cogito Tech

Cogito Tech stands out for its expertise in multilingual transcription, sentiment analysis, and domain-specific datasets. With strong capabilities in industries like healthcare and finance, it’s a valuable partner when projects demand not just raw labelling, but nuanced human interpretation of tone, context, and meaning.

• Services: Covers text, audio, video, and image, but is especially strong in multilingual transcription, NLP, and sentiment analysis.
• Domain expertise: Healthcare and finance datasets where accuracy and compliance are critical.
• Why choose it: If you need specialist human judgment (e.g., analyzing tone or intent) across multiple languages.

7. iMerit

iMerit takes a sector-focused approach, offering trained annotation teams with expertise in areas like agriculture, medical AI, financial services, and geospatial data. By combining domain knowledge with scalable human-in-the-loop processes, iMerit helps enterprises ensure that their datasets meet not only volume requirements but also the specific context of their industry.

• Verticals: Agriculture, medical AI, financial services, and geospatial annotation.
• Strengths: Workforce trained for domain-specific tasks and equipped with advanced QC processes.
• Differentiator: Combines scalable teams with subject expertise, which is rare in generalist providers.

8. Sama

Sama has built its brand on ethical data services, combining computer vision and NLP annotation with a workforce model designed to provide fair wages and career development opportunities. For companies that want a partner aligned with their ESG or social impact goals, Sama provides both technical expertise and transparent sourcing practices.

• Mission: Pioneered the “impact sourcing” model, ensuring fair wages and opportunities in emerging markets.
• Services: Computer vision annotation, text labelling, and GenAI dataset curation.
• Strength: Particularly strong in ethics and transparency, while still serving global enterprise clients.

9. Dataloop

Dataloop is more than an annotation provider; it’s an AI data management platform. It supports everything from annotation and QA to pipeline automation and model fine-tuning. With strengths in handling LiDAR, geospatial, and multimodal datasets, Dataloop is a good fit for enterprises that want a full data ops stack rather than piecemeal annotation services.

• What it is: A data-centric software stack that goes beyond annotation, covering storage, pipelines, QA, and model fine-tuning.
• Specialties: Handles LiDAR, geospatial, and multimodal datasets.
• Strength: Ideal for enterprises that want to own their data pipelines rather than outsource them.

10. Surge AI

As large language models (LLMs) grow in importance, Surge AI has positioned itself as a go-to provider for instruction-tuning, RLHF (reinforcement learning from human feedback), and evaluation sets. With a curated network of expert labelers, it specialises in tasks that require nuanced human judgment, such as ranking model outputs or aligning generative AI with human preferences.

• Focus: Instruction-tuning, RLHF, and evaluation sets for LLMs.
• Contributors: Vetted expert labelers with nuanced judgment.
• Strength: Best-in-class for alignment and safety-critical data in frontier AI.
• Why it matters: Generic crowds can’t deliver the subtle feedback needed for large language models.

11. Prolific

Prolific is widely used by researchers and AI teams that need structured human feedback at scale. Its platform provides access to more than 200,000 vetted participants, allowing companies to collect preference data, run safety evaluations, or test AI outputs against targeted demographics. It’s particularly well-suited to projects where human response data — rather than just raw annotation — is essential.

• What it is: A platform to recruit high-quality participants for research and AI data tasks.
• Strengths: Targeted demographics, transparent worker pay, and strong academic/AI adoption.
• Best for: Collecting human preferences, feedback, and evaluation data quickly and reliably.

Conclusion

TELUS International remains a trusted provider for AI training data projects, but as the industry expands, many organisations are finding value in exploring alternatives that better match their evolving needs.

The right choice will depend on your priorities, whether that’s speed, compliance, modality expertise, or ethical sourcing. What’s clear is that enterprises now have the freedom to choose partners who can provide not just data, but the right data to power the next generation of AI.