TDCX Group has acquired SUPA, a data labeling and annotation platform.

This acquisition will integrate SUPA into Chemin, TDCX’s AI enablement subsidiary, to address the growing demand for advanced data services.

SUPA brings expertise in data interpretation, prompt engineering, and synthetic data validation for complex AI systems.

TDCX CEO Laurent Junique said the deal strengthens their curated data offerings, while SUPA CEO Mark Koh highlighted its impact on AI decision-making.

Chemin, launched in 2025, provides services like data annotation and model evaluation, now expanded with SUPA’s tools.

The move positions TDCX to grow in the AI enablement market as demand for AI infrastructure accelerates.

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🔗 Source: TDCX

🧠 Food for thought

1️⃣ Consolidation in CX industry shifts toward AI capability acquisition

TDCX’s acquisition of SUPA reflects a strategic consolidation trend in the customer experience industry, where companies are prioritizing AI capabilities over traditional operational scale.

This contrasts with previous major CX acquisitions like Concentrix’s $2.43 billion purchase of Convergys (2018) and Telus International’s €915 million acquisition of Competence Call Center (2019), which primarily focused on expanding geographic footprint and operational capacity12.

Today’s acquisitions increasingly target specialized AI expertise rather than just service delivery capacity. TDCX’s integration of SUPA into Chemin, their AI enablement subsidiary, rather than their core CX operations, illustrates this shift3.

The industry has moved from acquiring market share to focusing on intellectual property and specialized talent, reflecting how AI is transforming the economics and value proposition of customer experience providers.

McKinsey’s research reveals that while 92% of companies plan to increase AI investments, only 1% consider themselves “mature” in AI deployment. This creates strong incentives for established players to acquire rather than build these capabilities4.

2️⃣ Data labeling evolves from basic tagging to specialized knowledge work

The acquisition highlights how data labeling has transformed from a relatively simple task to highly specialized knowledge work requiring advanced expertise and domain-specific training.

In 2025, the global data labeling market is projected to reach $4.94 billion (up from $3.99 billion in 2024), driven by increasing demand for complex annotations beyond basic tagging5.

This evolution aligns with the broader AI industry shift toward more sophisticated models requiring nuanced human judgment. Companies are now seeking PhD-level reviewers, linguists, and domain experts rather than general data taggers3.

The integration of SUPA into TDCX’s Chemin demonstrates how high-quality human-in-the-loop expertise has become a strategic differentiator in AI development, particularly for addressing edge cases and ensuring AI safety.

As shown in data labeling trend reports, the industry has moved toward AI-assisted labeling, real-time annotation, and industry-specific practices to meet the demands of increasingly complex AI systems6.

3️⃣ AI enablement emerges as critical middle layer in enterprise AI adoption

TDCX’s strategic positioning of Chemin as an “AI enablement” subsidiary highlights the emergence of a crucial middle layer in the AI ecosystem, bridging foundation model creators and enterprise users.

The company’s focus on services like data collection, annotation, model evaluation, and red-teaming addresses a critical gap in the AI implementation chain. McKinsey research shows $4.4 trillion in potential productivity growth from AI, while only 1% of companies consider themselves mature in deployment4.

This AI enablement layer has become essential as enterprises struggle to bridge the gap between rapidly advancing AI models and practical business implementation. There is growing demand for specialized partners who can customize and fine-tune AI systems for specific domains.

The market for these specialized AI enablement services is forecasted to become a multi-billion-dollar opportunity, reflecting how the complexity of AI implementation requires dedicated expertise beyond traditional IT services3.

Labor market analysis shows this trend in hiring patterns, with AI/Machine Learning Engineer roles growing by 41.8% year-over-year, indicating robust demand for professionals who can bridge theoretical AI capabilities with practical business applications7.

Recent TDCX developments

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