Headquarter.ai Pioneers Expert-in-the-Loop LLM Reasoning to Boost Public- and Private-Sector Efficiency; Wins AWS “Rising Star Partner of the Year – Technology”

Taipei, Taiwan — September 17, 2025 

In 2025, the most common use of Generative AI remains the chatbot. Yet many government agencies and enterprises report a counterintuitive trend: the more the AI answers, the more cases flood in—often with added workload explaining away AI “hallucinations.” Even with knowledge bases attached, systems can surface conflicting documents for the same query, forcing caseworkers to reconcile contradictions and driving frontline pressure higher.

Headquarter.ai, a Taiwan-born Agentic AI team, helps customers shift LLMs from an outward-facing Q&A tool to an execution layer inside core operations. By bringing Expert-in-the-Loop reasoning into Agentic Workflows, AI collaborates with human experts to parse large volumes of data, handle repetitive operations, and perform structured reasoning—augmenting (not replacing) expert judgment. In 2025 deployments, one government project achieved 95%+ precision while reducing LLM token usage by 85%. Leveraging Amazon Web Services (AWS) AI services, Headquarter.ai has delivered measurable value across multiple organizations—earning the AWS 2025 “Rising Star Partner of the Year – Technology” award.

AWS Taiwan & Hong Kong General Manager, Ting-Kai Wang, said: “We are committed to bringing Amazon’s global best practices to Taiwan—partnering to accelerate AI adoption and help customers expand globally. This year, we’re excited to see Headquarter.ai driving transformation across industries. With the new AWS Asia Pacific (Taipei) Region, we look forward to even closer collaboration with local partners to harness cloud and AI, helping more customers seize digital transformation opportunities and advance toward a smarter future.”

Headquarter.ai was also invited to speak at the 2025 AWS Partner Summit (Taiwan) alongside Deloitte Taiwan, co-leading an industry conversation on “Generative AI Opportunities: From Demand Insights to Revenue.”

Moving Beyond the “Fully Autonomous AI” Myth

“Many agencies assumed deploying AI meant wiring up the ‘strongest’ LLM plus a PDF FAQ. But once citizens asked case-specific questions, the AI couldn’t answer—and staff had to spend time explaining what the AI got wrong, making the job even harder,” said Chien-Chang Huang, CEO of Headquarter.ai. “That’s the consequence of relying on a single LLM without task-oriented workflow design.”

Headquarter.ai’s Agentic Workflow architecture starts from the task, defining the AI’s role and accountability boundaries, then selecting the right mix of RAG strategies and model types—rather than blindly chasing the latest model trend. In a recent deployment of the “AI Tax Assistant” at a local agency, the AI is responsible for three things (and not “answering everything”):

1. Normalize citizen narratives into precise, formal tax questions.

2. Retrieve applicable statutes and tax rules for the specific case.

3. Rank documents by legal hierarchy and use that ordering as a reasoning guardrail—rather than “trusting” the AI outright.

Crucially, frontline officers can see each LLM reasoning node, correct or enrich it in-flow, and adjust retrieval scope and depth. With this division of labor, AI becomes the best teammate for case analysis and evidence gathering—not a source of decision risk. The same platform is now live in Taiwan across risk evaluation, administrative audits, and supply-chain decisioning. “We make LLM reasoning controllable—that’s how you earn enterprise trust,” Huang added.

Zero-Error Ambition, One-Tenth the Cost?

As Generative AI moves from PoCs to scale, leaders ask a shared question: How do we maintain accuracy while driving LLM cost down—despite growing data and usage?

Headquarter.ai’s field experience shows it’s achievable by decomposing a hard problem into dozens of sub-tasks and assigning the best tool to each node:

• Simple & repetitive → classical ML models

• Routine operations → AI Agents operating existing enterprise systems

• High-complexity reasoning → LLMs collaborating with domain experts

Headquarter.ai provides the platform for this approach. Deployed in BYOA (Bring-Your-Own-Account) mode, compute and data remain entirely within the customer’s own AWS account. Paired with AI advisory, customers start from hundreds of workflow templates (Agent Hub) and then tailor knowledge bases and flows to their needs. “AI isn’t omnipotent. It should act like a team-first collaborator that works with experts to get the job done.”