Why does post-training data matter for LLMs?

Post-training data shapes model behavior through supervised fine-tuning, reinforcement learning from human feedback and alignment techniques that make outputs more accurate, safe and useful. Without it, even well-architected base models produce inconsistent or unsafe outputs as raw capability without behavioral alignment cannot meet production standards. For most leading labs, post-training data quality is now the primary differentiating variable between models with similar architectures.

What kind of data is needed to train agentic AI?

Agentic AI systems require fundamentally different training data than standard language models. Because agents must plan, use tools, self-correct and execute multistep workflows, they need richly annotated data capturing reasoning processes, not just input-output pairs. This includes verified reasoning traces, chain-of-thought annotations and decision trees for branching scenarios. The quality of this data directly determines whether an agent generalizes reliably or fails unpredictably in production.

How can I ensure AI training data is ethically sourced and copyright safe?

Responsible training data collection requires explicit contributor consent, fair compensation, transparent provenance and clear intellectual property frameworks, particularly as copyright litigation around AI training data intensifies. Enterprises should expect vendors to demonstrate documented consent workflows and a clear chain of custody for every dataset. For proprietary or domain-specific data, workflows should produce defensible records that can withstand legal and regulatory scrutiny.

How is proprietary AI research protected during data partnerships?

Proprietary AI research demands more than standard data security, it requires strict data isolation guarantees that ensure training data is never shared with other model builders or competing labs. TELUS Digital operates under a zero-trust security architecture with this commitment built in, ensuring that novel model architectures, competitive R&D and proprietary research remain fully protected throughout every phase of the data partnership.

How do you benchmark and evaluate LLMs?

Effective benchmarking requires structured human evaluation pipelines designed for multiparameter model assessment. TELUS Digital delivers evaluation datasets, side-by-side comparisons, preference ratings and safety benchmarks through configurable pipelines that support competitive model analysis, performance tracking across capability dimensions and compliance with evolving alignment standards, giving AI labs the human signal needed to continuously improve model outputs.

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Data for AGI & GenAI

Building agentic and frontier intelligence

Frontier data creates frontier intelligence, solving novel use cases from emergent behavior to complex agentic workflows. We address this need with bespoke data creation driven by advanced intelligence and expertise. With consistent quality, we move the needle from post-training to production.

Trusted partner for the world’s top AI labs

Quality-first platforms and processes
Get proven, mission-critical training data through continuous quality validation, expert-in-the-loop reviews and agentic QC pipelines. Your datasets meet the highest standards required for state-of-the-art research and model development.
Vetted and curated expert cohorts
Access our managed, verified community of on-demand experts, filtered through intensive skill qualifications and project-based evaluations, as well as forward-deployed engineering expertise. Every task reflects genuine expertise as your models train from the best.
Proven data neutrality and trust
Our mission is to solve for your research needs. Your data is yours alone, not for other model builders and competing labs. Our zero-trust security architecture and commitment to independence safeguard your proprietary R&D across any frontier initiative.

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Experts across diverse subject matter areas
100+
Language groups and countries supported

Data for every model type and vision

Our 20+ years of data solutions experience, from NLP to multimodal AI, has shaped our capabilities to address every post-training data need. From unlocking access to advanced experts to building intuitive projects and quality workflows, we think through every step.

Multidomain, multimodal, multilingual training data for any R&D need

From enterprise deployments to sovereign infrastructure, we deliver human ingenuity and domain expertise. This intelligence is backed by technical solutioning and quality assurance driven by our state-of-the-art platforms and processes.

Agentic trajectories and RL environments
Train and evaluate agents to excel at long-horizon, professional workflows with verified reasoning traces, chain-of-thought annotations and multi-step planning. We architect complex, multi-modal RL environments with live model interactivity that challenge models to plan, use external tools and self-correct through expert-generated decision trees.
Advanced SFT and deep reasoning
Train your models on high-fidelity human instructions and reasoning from our network of PhDs, clinicians, software architects and SMEs, who generate highly contextual multimodal annotations with dense class attributes and granular logic chains necessary to "think" through complex, multi-step problems rather than brute-force a solution.
Red teaming and adversarial evaluation
We combine automation with expert manual testing to stress-test your AI models. Our multidisciplinary teams of red teaming experts uncover latent biases, logical fallacies and safety vulnerabilities, delivering 100% harm taxonomy coverage to guard your models before they reach the public.
Multimodal, multilingual post-training datasets
Intelligence sees, hears and speaks. We curate high-fidelity datasets across 100+ languages and multiple modalities, ensuring your AGI captures the subtle cultural nuances, technical grammar and diverse worldviews of a global audience.
Human reinforcement, evaluations and benchmarking
Assess model performance, adaptability and safety through high-quality preference datasets. Our pipelines are configurable for multi-parameter ratings and multi-model comparisons, ensuring outputs are explainable and aligned with evolving standards.
Sovereign AI and context localization
Maintain data sovereignty and regional relevance, without sacrificing quality or scale, with locally sourced training datasets, in-country annotation and compliant pipelines to build culturally-nuanced models that are relevant locally and not just globally.

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Every model has different needs. Every deployment has different constraints. Contact us to build custom data pipelines that solve your unique problem.

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Strengthening multilingual AI safety guardrails with expert-validated adversarial data
Discover how native-speaking red-team specialists generated and validated a multilingual adversarial dataset that helps strengthen AI safety guardrails across multiple languages.
- 40KMultilingual adversarial red-team prompts delivered
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Improving trust and safety policy-enforcement accuracy with chain-of-thought reasoning data
Discover how expert safety analysts at TELUS Digital built a structured reasoning framework that taught a content-moderation LLM to detect nuanced, evasive policy violations.
- 850+Complex chain-of-thought evaluations created
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Evaluating affective safety guardrails for a leading foundational model
Discover how TELUS Digital combined multi-turn conversation data, turn-level evaluation and licensed clinical expertise to help a prominent foundation-model provider benchmark and secure its AI model during high-stakes, emotionally sensitive interactions.
- 12,500+Turn-level safety judgements across two models
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Overcoming the challenges of AI agent creation, training and evaluation

Join us for an enlightening discussion with two pioneers in the field of AI: Tommy Guy, principal applied researcher at Microsoft Copilot Studio and Steve Nemzer, senior director of AI growth and innovation at TELUS Digital.

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Frequently asked questions

Post-training data shapes model behavior through supervised fine-tuning, reinforcement learning from human feedback and alignment techniques that make outputs more accurate, safe and useful. Without it, even well-architected base models produce inconsistent or unsafe outputs as raw capability without behavioral alignment cannot meet production standards. For most leading labs, post-training data quality is now the primary differentiating variable between models with similar architectures.
Agentic AI systems require fundamentally different training data than standard language models. Because agents must plan, use tools, self-correct and execute multistep workflows, they need richly annotated data capturing reasoning processes, not just input-output pairs. This includes verified reasoning traces, chain-of-thought annotations and decision trees for branching scenarios. The quality of this data directly determines whether an agent generalizes reliably or fails unpredictably in production.
Responsible training data collection requires explicit contributor consent, fair compensation, transparent provenance and clear intellectual property frameworks, particularly as copyright litigation around AI training data intensifies. Enterprises should expect vendors to demonstrate documented consent workflows and a clear chain of custody for every dataset. For proprietary or domain-specific data, workflows should produce defensible records that can withstand legal and regulatory scrutiny.
Proprietary AI research demands more than standard data security, it requires strict data isolation guarantees that ensure training data is never shared with other model builders or competing labs. TELUS Digital operates under a zero-trust security architecture with this commitment built in, ensuring that novel model architectures, competitive R&D and proprietary research remain fully protected throughout every phase of the data partnership.
Effective benchmarking requires structured human evaluation pipelines designed for multiparameter model assessment. TELUS Digital delivers evaluation datasets, side-by-side comparisons, preference ratings and safety benchmarks through configurable pipelines that support competitive model analysis, performance tracking across capability dimensions and compliance with evolving alignment standards, giving AI labs the human signal needed to continuously improve model outputs.

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Data solutions that power AGI and GenAI

Data for AGI & GenAI

Trusted partner for the world’s top AI labs

Quality-first platforms and processes

Vetted and curated expert cohorts

Proven data neutrality and trust

Data for every model type and vision

Multidomain, multimodal, multilingual training data for any R&D need

Agentic trajectories and RL environments

Advanced SFT and deep reasoning

Red teaming and adversarial evaluation

Multimodal, multilingual post-training datasets

Human reinforcement, evaluations and benchmarking

Sovereign AI and context localization

Build your custom data pipeline today

Explore our success stories

Strengthening multilingual AI safety guardrails with expert-validated adversarial data

Improving trust and safety policy-enforcement accuracy with chain-of-thought reasoning data

Evaluating affective safety guardrails for a leading foundational model