In the latest extension of Toku's partnership with foodpanda, its AI-powered customer experience platform now supports customer conversations across these new markets.
SINGAPORE, Oct. 29, 2026 /PRNewswire/ -- Toku Ltd. (SGX: TKU) ("Toku" or the "Company", and together with its subsidiaries, the "Group"), a Singapore-headquartered AI-powered customer experience (CX) platform, today announced that foodpanda is using Toku Contact Centre to power its support for customers in Cambodia and Laos, extending its regional partnership with Toku. The platform supports a team of agents managed by foodpanda's agency, handling both inbound and outbound calls. It has been in production since 8 September 2026.
This extension builds on an existing relationship between Toku and foodpanda in other markets, reflecting foodpanda's confidence in the platform's ability to perform reliably in operationally complex, fast-moving environments where connectivity and language demands are anything but standard.
"Every expansion a customer trusts us with is earned, not given," said Thomas Laboulle, Founder and Chief Executive Officer of Toku. "foodpanda runs one of the most demanding customer operations in the region. Its decision to take our partnership into new markets tells us the platform is doing exactly what we built it to do: perform where complexity is the norm. We intend to build on that confidence as the partnership grows."
Customer support in Cambodia and Laos runs on the same Toku platform that powers foodpanda's operations in other markets. This provides foodpanda with consistent reporting and performance metrics across the partnership, allowing results to be compared across deployments. Consolidating voice operations on a common platform also reduces the number of voice-technology relationships foodpanda needs to manage, supporting a more consistent experience for customers and agents, with inbound and outbound calling handled end-to-end through Toku.
"Reliability and speed are non-negotiable for the customers who rely on foodpanda every day," said Ye Myint Aung, Head of Country Marketplace Experience (CLM Cluster) at foodpanda. "Toku's platform has met that standard in our other markets, which made using it for our customers in Cambodia and Laos an easy call."
This deployment reflects Toku's modular CX Platform, built on end-to-end ownership of the technology stack from carrier-grade connectivity through to AI applications. It also adds to Toku's growing base of enterprise customers across the region, reinforcing its position as a trusted partner for complex, multi-market customer operations.
About Toku
Headquartered in Singapore, Toku Ltd. (SGX Catalist: TKU) is a cloud-native, AI-powered customer experience platform purpose-built for enterprises operating in complex, multi-market environments. With deep roots in the APAC region and an expanding global footprint, Toku's modular 360° CX Platform orchestrates customer interactions across voice, chat, email and digital channels while managing regulatory, linguistic and infrastructure complexity at scale.
Built on end-to-end ownership of its technology stack, from carrier-grade connectivity to AI applications, Toku delivers enterprise-grade security, reliability and deployment flexibility across commercial cloud, private data centres and hybrid environments. Its AI capabilities include transcription, summarisation, sentiment analysis, conversation analytics and governed virtual agents, designed to integrate seamlessly with enterprise systems and customer data.
Trusted by leading enterprises and public-sector organisations, Toku helps organisations streamline operations, scale customer engagement and deliver consistent experiences in fragmented markets worldwide.
For more information about Toku, visit toku.co.
Forward-Looking Statements
This press release contains forward-looking statements regarding Toku's expansion plans and business strategy. These statements are based on current expectations and assumptions and are subject to risks and uncertainties that could cause actual results to differ materially. Toku undertakes no obligation to update these statements to reflect subsequent events or circumstances.
Sponsor's Statement
Toku Ltd. (the "Company") was listed on Catalist of the Singapore Exchange Securities Trading Limited (the "Exchange") on 22 January 2026. The initial public offering of the Company was sponsored by PrimePartners Corporate Finance Pte. Ltd. (the "Sponsor"). This press release has been reviewed by the Sponsor. It has not been examined or approved by the Exchange and the Exchange assumes no responsibility for the contents of this press release, including the correctness of any of the statements or opinions made or reports contained in this press release. The contact person for the Sponsor is Ms. Ng Shi Qing, 16 Collyer Quay, #10-00 Collyer Quay Centre, Singapore 049318, sponsorship@ppcf.com.sg.
Investor Relations Contact:
investor.relations@toku.co
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Toku Powers foodpanda's Support for Customers in Cambodia and Laos
A state-of-the-art general-purpose model in its class, built through automated AI research and designed to advance AI and scientific discovery.
BEIJING, Oct. 29, 2026 /PRNewswire/ -- Apex Intelligence has released Apex-35B-V1.0, its first model. Built on Qwen3.6-35B-A3B and further developed through mid-training and post-training, the 35B general-purpose model incorporates extensive proprietary datasets and research trajectories.
According to Apex Intelligence's evaluations, Apex-35B-V1.0 leads the similarly sized models tested in research and development capabilities while maintaining strong general-purpose performance. On selected benchmarks, it is competitive with substantially larger models.
AI participated autonomously in the model's development, including data construction, experiment design, code execution, analysis of training results and training optimization. Apex-35B-V1.0 can perform coding, agent workflows, question answering and deep search. It can also carry out end-to-end computational research workflows in AI research, computational science and robotics simulation.
Benchmark performance
Across 21 benchmarks, Apex-35B-V1.0 outperformed the similarly sized Qwen3.6-35B-A3B on 20 and Recursive Flint (40B) on 18, tying Recursive Flint on one. Beyond its strong performance on research tasks, the model demonstrated broad competitiveness in agentic tasks, coding, and reasoning, and surpassed larger models on selected benchmarks. Further details are available in the technical article.
Tech Blog：https://www.apexin.com/apex-35b/
Building Apex-35B-V1.0 Through Recursive Self-Improvement
Apex Intelligence developed Apex Research, an automated research system designed to enable recursive self-improvement. The system identifies model weaknesses, generates hypotheses, runs experiments and turns findings into measurable improvements. It contributed to the development of Apex-35B-V1.0 through self-evolving training data, automated experimentation, training recipes, GPU kernels and inference optimization.
Apex Research generates targeted training data to address weaknesses revealed by evaluation. When evaluations identified failures in computational fluid dynamics, the system analyzed the reasoning gaps, synthesized and validated new scientific training examples, and incorporated them into subsequent training rounds. Apex Intelligence reports that this process improved performance on its internal CFD-Bench by 32.1 points.
The system also analyzes evaluation results and agent trajectories to identify recurring failure patterns and refine training strategies. After identifying excessively long reasoning contexts and imbalanced data mixtures as factors affecting agent performance, it explored sequence-length distributions, short-to-long trajectory sampling ratios and token-aware batch compositions to improve agent reliability and training efficiency.
Apex Research extends this optimization to training and inference infrastructure. On the training side, its work on GPU load balancing, communication and computation overlap, dynamic batching, and token-aware workload distribution delivered 45–60% higher training throughput, according to Apex Intelligence. On the inference side, optimizations to attention kernels, KV-cache management and prefill/decode execution delivered 1.6–1.8 times faster decoding and 30–35% lower time to first token under comparable workloads.
Together, these improvements across data, training and infrastructure form a feedback loop in which each research iteration supports the next.
Availability
The weights for Apex-35B-V1.0 are available on Hugging Face. Accompanying code, a deployment guide and usage examples are available on GitHub. Developers can download and run the model for AI development, scientific computing and other research tasks. Model versions, hardware requirements, supported inference frameworks and licensing information are detailed on the project pages.
GitHub：https://github.com/apexin-ai/Apex-35B-V1.0
Hugging Face：https://huggingface.co/Apexintelligence-AI/Apex-35B-V1.0
About Apex Intelligence and what comes next
Apex Intelligence was founded by Yongchao Chen, an assistant professor at Tsinghua University's College of AI. The company aims to build the next generation of self-evolving foundation models for research. By "self-evolving," it means enabling AI to participate in research, experimentation and validation, then using the resulting discoveries to improve the model itself.
Apex Intelligence aims to advance AI from an assistant to a research collaborator capable of contributing to new discoveries. Its long-term vision is to develop models whose collective research capability matches and progressively surpasses that of 100,000 leading AI scientists across disciplines.
Following Apex-35B-V1.0, the company plans to release a larger self-evolving foundation model with stronger AI research capabilities and expanded applications in scientific fields including chip design and quantum computing. Apex Intelligence plans to announce further details on the model's capabilities, benchmark results and availability.
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Apex Intelligence Releases Apex-35B-V1.0, a General-Purpose Model Built With Automated AI Research
