HOST
South Korea
LOCATION
65
ATTENDEES
AI & Data
INDUSTRY
SEOUL, JUNE 25, 2026 — Bright Data brought its ScrapeOps AI Lab to the Grand InterContinental Seoul Parnas for a hands-on workshop on building resilient, cost-efficient web data pipelines for AI agents, LLM training, dynamic pricing, and search optimisation.
THE BRIEF
Turning Web Data Into a Competitive Advantage
Bright Data partnered with The Ortus Club to bring its AI and data engineering community in Seoul together for something more technical than a typical executive dinner: a working session. The ScrapeOps AI Lab Workshop was built for the engineers, product leads, and technical founders who are actually building the pipelines behind Korea’s next wave of AI products, not just approving the budget for them.
The brief was to translate Bright Data’s infrastructure into something the room could use immediately. Rather than a keynote followed by polite applause, the format was built around a live build-versus-buy debate, a real-time demo of data extraction, and a hands-on lab where guests left with something they could apply the same week.
THE SETTING
A Workshop With the Polish of a Product Launch
The Grand InterContinental Seoul Parnas provided the backdrop: a five-star Gangnam property known for hosting Korea’s biggest corporate gatherings, chosen here for a room that could hold a technical crowd without losing the sense of occasion. Registration opened at 9:30 am, giving the room a full morning session structure rather than the usual evening dinner format.
The agenda moved fast and stayed dense: a welcome from The Ortus Club and Bright Data, an introduction to the platform, a look at where the data industry is heading, the build-versus-buy strategy debate, a session on constructing AI agents with real-time web data, a live extraction demo, and a hands-on lab before the room broke for lunch and networking just past lunch.
Interested to find out how the event was shaped and the work that went behind it?
THE GUESTLIST
Korea’s AI and Data Builders, In One Room
Sixty-five guests filled the room with Bright Data, spanning the full stack of Korea’s AI and data ecosystem: CEOs and CTOs of AI startups, principal engineers from the country’s largest conglomerates, and product leaders shipping AI features at scale. The mix skewed technical and senior in equal measure.
Company representation ran from global platform players to founder-led startups, including:
- Samsung SDS, Samsung Electronics, and LG AI Research
- SK Telecom, Hyundai Motor Company, and POSCO E&C
- STRADVISION, seavantage, MODULABS, and morai
- HayanMind, FIS Lab, and Ascent Korea
- Gmarket, Netmarble, KT, and Karrot
That range, from AI research labs to autonomous driving software to e-commerce infrastructure, meant the discussion of build-versus-buy tradeoffs pulled from a genuinely wide set of production experiences rather than a single industry’s playbook.
THE SPEAKERS
A Hands-On Approach to Structured Debate
Bright Data sent a seven-person delegation to run the session, led by KT Prasad, Vice President for APJ, China and MEA, alongside Business Development Manager Jongwoo Park and a supporting team spanning go-to-market, engineering, and marketing. Having that much of the Bright Data bench in the room meant guests weren’t just hearing a sales pitch. They were troubleshooting real scraping challenges with the people who built the platform.
The format leaned hands-on throughout. After the platform introduction and industry trends session, the room moved into a structured debate on advanced scraping strategy: build in-house, buy a vendor solution, or work through an API. That was followed immediately by a live demonstration of data exploration and extraction, then open lab time where guests worked through their own use cases with the Bright Data team circulating the floor.
THE TOPIC
Build, Buy, or API: The Scraping Decision Every AI Team Faces
At the centre of the Bright Data workshop was a question nearly every technical team in the room had wrestled with internally: is it more efficient to build a web scraping stack in-house, license a third-party solution, or route through an API layer like Bright Data’s? The session was structured to let guests stress-test that decision against their own use cases rather than accept a single answer.
Discussion threads explored:
- Where in-house scraping breaks down at scale, and what that costs in engineering time
- How real-time web data feeds AI agents, from search and pricing intelligence to brand monitoring
- What separates a resilient data pipeline from one that breaks every time a target site changes its markup
- Where SEO and answer-engine optimisation intersect with web data infrastructure
THE INSIGHTS
What the Room Actually Builds With Web Data
What key insights did guests learn from the workshop with Bright Data?
The build-it-yourself instinct usually loses to the maintenance bill, not the build cost. Several engineers in the room described the same pattern: the initial scraper is easy, but every anti-bot update, CAPTCHA change, and IP block turns into an ongoing engineering tax that never shows up in the original project estimate.
AI agents are only as good as the freshness of the data feeding them. For teams building agents on top of LLMs, stale or incomplete web data was raised repeatedly as the silent failure mode, one that doesn’t throw an error. It just produces a confidently wrong answer.
Search and answer-engine optimisation are converging into the same infrastructure problem. As more discovery moves through AI-generated answers rather than traditional search results, teams are realising that monitoring how their brand appears in AI outputs requires the same scraping infrastructure as monitoring search rankings did a decade ago.
The API-versus-build debate rarely has a universal winner. What emerged instead was a rough heuristic the room converged on: teams with unpredictable, high-volume targets leaned toward API infrastructure, while teams scraping a small number of stable, known sources were more comfortable maintaining their own stack.
KEY TAKEAWAYS
Lessons in Infrastructure, Resilience, and AI-Readiness
Here are the most important takeaways from the workshop and discussions with Bright Data:
- Scraping resilience is an engineering discipline of its own. Anti-bot measures evolve constantly, and the teams that treat scraping infrastructure as a maintained product outperform those that treat it as a one-time build.
- Data freshness determines AI output quality. Agents built on stale web data don’t fail loudly; they fail convincingly, which makes the problem harder to catch.
- The build-versus-buy decision should be use-case specific, not company-wide policy. Volume, target diversity, and in-house engineering capacity all shift the right answer.
- Answer-engine visibility is becoming a data infrastructure problem. Brands now need to monitor how they appear inside AI-generated answers, not just traditional search results.
- Hands-on demos surface problems slide decks hide. The live extraction demo and lab session drew more detailed technical questions from the room than the presentation portions of the agenda.
THE RESULTS
A Technical Community Built in One Room
- 65 guests across AI, data engineering, and product leadership roles
- 10-session agenda spanning platform introduction, strategy debate, live demo, and hands-on lab
- Company representation spanning global conglomerates, research labs, and founder-led startups
For Bright Data, the workshop delivered something a traditional dinner format doesn’t: a room full of the specific engineers and technical leads who make build-versus-buy infrastructure decisions, in a format built around their actual workflow rather than a pitch deck. The build-versus-buy debate and live demo format gave Bright Data’s team direct visibility into the objections and use cases shaping Korea’s AI data infrastructure market.
For the guests, the value was equally practical. Rather than leaving with a stack of business cards and a vague sense of goodwill, the room left having stress-tested a real technical decision against peers solving the same problems, with Bright Data’s own engineers in the room to answer the follow-up questions that usually go unanswered after a sales call.
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