Taiwan-Aware, Validated by Public Benchmarks
AIEC validates FFM's understanding of Taiwan's language, culture, and local contexts.
AIEC validates FFM's understanding of Taiwan's language, culture, and local contexts.
In-house model training further enhances reasoning, tool use, and multi-step task execution.
Lower deployment barriers while balancing model capabilities, data security, inference efficiency, and long-term costs.
Source: AIEC official language model evaluation results (September 2026); agentic AI benchmarks based on PinchBench / OpenClaw Agent Benchmark. Data compiled as of September 30, 2026. Rankings may change as subsequent benchmark results are published.
Integrates FAQs, product documentation, internal policies, and knowledge bases to support real-time queries, summarization, and internal enterprise Q&A.
It connects APIs, tools, and existing systems to perform data retrieval, task breakdown, cross-system processing, and workflow.
Understand forms, charts, scanned documents, and mixed text-and-image content to reduce the time spent on manual reading, organization, and information extraction.
Integrate supervision standards, internal operations, and credit information to help frontline personnel quickly find evidence, organize cases, and produce reports.
Integrates equipment and process knowledge to help engineering teams quickly identify root causes, find maintenance solutions, and access historical troubleshooting records.
Provides a centralized access point for querying clinical documents and institutional knowledge, helping healthcare and administrative staff find the information they need more quickly.
Integrates regulations, official interpretations, and administrative procedures to help public sector staff quickly find relevant legal references, organize documents, and address day-to-day operational inquiries.
For highly regulated industries, addressing data retention, access controls, auditability, and deployment requirements only after implementation can lead to architectural redesign, data migration, and redeployment. Compliance should be built into the AI architecture from the design stage.
Plan for in-country cloud or on-premises deployment based on project requirements and compliance needs, reducing governance and regulatory risks associated with cross-border data transfers.
Run models in environments controlled by the enterprise, based on data sensitivity and industry requirements, minimizing unnecessary exposure of core business data to external parties.
Control access to models, data, and knowledge bases based on user roles and use cases, giving enterprises greater control over how their data is accessed and used.
Maintain records required for model usage and governance to support the management needs of highly regulated sectors, including finance, healthcare, and government.
Enterprise Q&A, Long-Context Processing, Reasoning, and Tool Calling
Image, OCR, and Document Understanding
Enterprise Knowledge Bases and Semantic Search
Sending sensitive data directly to external public AI services may increase cross-border data transfer, outsourcing, and data governance risks. Highly regulated use cases should prioritize private cloud, in-country cloud, hybrid, or on-premises architectures.
Taiwan AI Cloud's Model Foundry integrates AI computing and orchestration, data and security governance, model training and management, and AFS Suite and Agent applications to deliver end-to-end AI capabilities spanning compute, data, models, and applications.
AI Infrastructure and Orchestration
Data and Security Governance
Training, Management, and Deployment
AFS Suite and Agent