Last week, I spent a few days at AI Expo Korea to check out the latest wave of AI products. And upon entering the room bustling with 500+ booths, I found myself in the thick of 2025’s most dominant AI playbook: vertical agents.
Vertical agents are AI systems trained with deep, domain-specific knowledge to excel at narrow, often industry-specific tasks. While it was interesting to see the variety of use cases, the sheer saturation of companies offering slightly different spins on similar concepts raised a real question for me: are these products truly differentiated enough to survive if foundational model giants like OpenAI decide to directly enter the vertical space?
And before digging into that, here are two vertical AI companies from the Expo that stood out for me.
NEWEN AI: Cracking the Code of Colloquial Language
If you’ve tried prompting models like ChatGPT or Claude in languages other than English, you may have noticed a difference in how well they understand the finer layers of the conversation. While still impressively capable, they tend to miss nuance and colloquial subtleties more often in other languages than they do in English. As a bilingual Korean speaker, I’ve seen firsthand how much better the models are at capturing intent and making smoother interactions when prompted in English vs. Korean.
Enter NEWEN AI, which stood out with its Quetta LLMs: small language models explicitly trained on informal and colloquial Korean (alongside 29 other languages). Instead of serving one industry, they’ve built an extensive, multi-sector platform with domain-specific models tailored for FMCG, telecom, automotives, K-beauty, and more. Leveraging its vast corpus of labeled colloquial language data, its offerings help businesses understand global consumer sentiment for their products through real-time trend spotting, influencer monitoring, consumer experience analysis via social media commentary, and keyword trend analytics.
While they layer in GPT for summarization and chat functionality, their real moat lies in the thousands of finely-tuned, industry-specific analysis models, the high volume of daily data collection, and the extensive labeling (over 30 million data points) of unstructured, multilingual language data. It's the kind of informal nuance general-purpose models still struggle to capture. This specialization, rather than any singular technological breakthrough, defines their market position.
Samil PwC: When Compliance Is the Product
Samil PwC, the Korean member firm of the global PwC network, showcased a suite of vertical AI agents built to tackle finance and accounting regulatory complexities in Korea. While they also build on GPT, their real edge comes from each of their vertical agents being fine-tuned through backend coding and narrow AI training to ensure outputs meet Korean compliance requirements.
Their suite of agents includes tools for automating tasks across accounting, contract analysis, lease processing, internal controls, document review, and investor relations. Each product is built with a specific workflow in mind, like parsing lease terms, classifying financial disclosures, or extracting audit-relevant details from contracts, and generates outputs that align with local regulatory standards like K-SOX and compliance expectations from Korea’s Financial Supervisory Service (FSS).
Like NEWEN AI, Samil PwC’s strength isn’t novel AI tech. It’s their ability to embed deep domain expertise and compliance readiness directly into their AI products, backed by the institutional credibility of the PwC brand. This level of fine-tuning requires years of professional experience and nuanced understanding of local regulatory expectations - something that’s difficult to replicate.
When Giants Enter the Chat: OpenAI’s Strategic Shift
The widespread emergence of vertical agents becomes even more interesting against the backdrop of OpenAI's recent strategic decisions: hiring Fidji Simo as CEO of Applications and partnering with Jony Ive for AI hardware initiatives.
These developments signal a major strategic shift for OpenAI. OpenAI’s early focus was foundational model development. But now, it’s clear that productization is just as much a priority. Through software and hardware, consumer and enterprise, OpenAI wants to own the end-user experience.
If they roll out verticalized, workflow-integrated AI products, the pressure is on. Vertical agents that are simply wrapping GPT with a fancy UI and little else won’t stand a chance. OpenAI already commands unmatched scale, proprietary data, and mature feedback loops. Its move into first-party products will only deepen its advantage, widening the gap for vertical agent startups still racing to build out their infrastructure.
Sink or Swim: Building a Real Moat in Vertical AI
So where does this leave everyone else? If you’re building a vertical AI product, you’ve got to get sharper about defensibility. Here’s where I think true differentiation will come from:
1. Specialized Niches: Target complex, local problems that require deep domain expertise.
o Ask yourself: Is the niche you’re targeting too specific or nuanced for major platforms to pursue?
2. Proprietary & Actionable Data: Curate exclusive, high-quality datasets that are useable and continuously updated.
o Ask yourself: How easily can competitors replicate your data? Is your data structured in a genuinely usable manner? How frequently is your data refreshed?
3. Seamless Workflow Integration: Embed your AI solution deeply into existing client workflows, making your product difficult to replace.
o Ask yourself: Does your product integrate into existing enterprise systems without much need for infrastructural changes? Is it effective enough to create a sticky customer base? Is the integration deep enough that it creates high switching costs?
4. Compliance & Audit Readiness: Ensure your AI outputs satisfy regulatory requirements with transparency and auditability.
o Ask yourself: Can your solutions withstand immediate regulatory scrutiny?
5. Real-Time Product Alignment: Continuously refine your AI products based on active user feedback to maintain strong product-market fit.
o Ask yourself: What kind of evaluation metrics do you have set up to measure effectiveness of your tools? Are you actively capturing user feedback? How quickly are your products iterated based on this feedback to enhance model effectiveness?
Bottom Line
The AI Expo Korea captured both the excitement and risk around the current boom of vertical AI agents. It’s an exciting wave, but not everyone will make it to shore. The real winners will be those investing deeply in specialized datasets, robust workflow integrations, stringent regulatory compliance, and continuous feedback loops. Have you seen a vertical AI product that checks all those boxes? I’d love to hear about it.
And finally, on a more personal note: here’s a snapshot of my parents at AI Expo Korea! Including your family in your AI learning journey can be a powerful way to normalize these tools and spark curiosity across generations. Real adoption happens when everyone’s brought along, not just the early few.
Thanks for sharing - sounds super interesting!
Where do you think Korea stands in terms of AI adoption and development? I think Korea is such an interesting case because the power of the Korean language, in terms of the number of speakers isn’t as strong compared to other Asian countries, but the country's influence in business standards is strong. Would be keen to know if you’re also open to discussing this!
I want to know how good Newen was at making small talk in Koran with you