Why Real-Time Context is a Critical Bottleneck for AI – PYLER's Take from SingleStore Now 2025

Attending SingleStore Now 2025 felt less like visiting an event and more like a conversation about the future we’re actively building. This event matters to us because its central theme—that AI's success hinges on a highly responsive, real-time context layer—is not just a future trend, but the core technical problem PYLER has been solving at petabyte scale.

The industry is now waking up to a problem we've long understood: AI models are "context-blind." Statistics from the opening keynote by SingleStore CEO Raj Verma (like the fact that fewer than 26% of AI projects achieve ROI) prove that legacy architectures are reaching their limits. They cannot deliver the ultra-low latency, high concurrency, and complex query support needed to provide real-time context.

This challenge is exponentially harder for unstructured video data—the most complex, data-heavy, and high-velocity medium. The keynote discussion resonated deeply with our obsessive focus on solving this high-latency data problem. Real-time context is the difference between preventing brand risk and merely reporting it after the damage is done.

Key Innovations Intersecting with Our Architectural Approach

PYLER team members join SingleStore CEO Raj Verma (front, center) and C-level executives to celebrate the SingleStore Nasdaq closing ceremony in New York City.

Each new capability introduced at the event reflected the same principles we’ve built our platform on — a shared belief in real-time, context-rich data as the foundation of AI. This alignment was clear across three key announcements, which together form a powerful toolkit for the next generation of AI applications.

1. AI/ML Functions: Bringing AI to the Data

SingleStore is embedding AI capabilities (like AI_SENTIMENT(), AI_SUMMARIZE(), AI_CLASSIFY()) directly into the database, accessible via simple SQL. This eliminates the slow, costly ETL pipelines traditionally required to move data to an external AI model and back. For PYLER, this directly aligns with our philosophy of 'bringing compute to the data.' Moving petabytes of video data for external inference is architecturally unfeasible and slow. In-database functions allow us to enrich our contextual analysis in-place, drastically reducing latency. It’s a move from complex, costly ETL pipelines to instant, on-the-fly intelligence—a core principle of our 'Pride-worthy Engineering.'

2. Zero Copy Attach: Agile Development on Live Data

SingleStore's new “Zero Copy Attach” feature allows developers to instantly attach smaller, isolated compute clusters to a production database without copying any data. This provides complete workload isolation and independent scaling. For PYLER's R&D, this is a massive enabler, as it allows our engineers to experiment fearlessly. We can now test new validation models or AI agents on live, production-scale data without risking performance isolation or incurring massive data duplication costs. This accelerates our innovation cycle from weeks to days, allowing us to test new trust models without compromising our core service.

3. Aura Analyst: The Natural Language Data Agent

The speed is only half the battle. The other half is accessibility. Aura Analyst is a new "context-aware" natural language agent system that allows business users to query data using plain English, with all queries being auditable and governable. While we may not embed this in customer-facing dashboards immediately, we see immense value in Aura Analyst for our internal operations, precisely because it aligns with our core philosophy of data democratization. We believe that access to data and the opportunity for analysis must be democratically guaranteed to everyone, not just developers. This isn't just an ideal; it's a core value for our high-speed growth. Tools like Aura Analyst can dramatically boost operational efficiency by empowering our non-developer teams to get insights without a SQL bottleneck.

Our Perspective: How We Architected for Trust

This vision of a real-time, context-aware AI future is not just theoretical for us at PYLER—it is the challenge we solve daily. We provide real-time brand safety for global brands like Samsung Electronics and LVMH.

Our previous architecture (on PostgreSQL) faced the exact problems the industry is now discussing. Our service architecture is inherently complex, mixing high-throughput transactional (OLTP) queries from real-time ad metrics with heavy analytical (OLAP) queries from our massive video analysis database. Our previous PostgreSQL-based system could not guarantee latency for this mixed workload, which directly impacted user experience. Our engineering challenge was that traditional OLAP-optimized systems are less efficient when handling high-concurrency ingestion, and OLTP-optimized systems are not designed for complex analytical joins. We were forced into a brittle system of materialized views and nightly batch jobs, which is the very definition of 'stale context.'

This is how we solved the problem: Our engineering decision was to move to an HTAP architecture. By migrating, we eliminated the core bottleneck: the join latency between live ad-metrics data and petabyte-scale video analytics. This new design allows us to serve both query patterns concurrently with radically improved performance, which has been a critical factor in dramatically improving our user experience.

This isn't just a theoretical number. In our internal benchmarks, SingleStore delivered query speeds up to 100x faster for complex OLAP-style analytical queries compared to our previous partitioned PostgreSQL setup. This performance leap isn't just a vanity metric. For PYLER, this translates directly into two critical outcomes: a vastly improved, real-time experience for our user-facing analytical dashboards, and the engineering delta that separates 'brand safety' from 'brand risk.' It’s the difference between catching harmful video content before an ad impression or after the damage is done. This focus on how we architect for trust is what defines us.

Beyond the Sessions: A Meeting of Minds

Leaders from PYLER and partner companies exchange insights during a networking cruise at SingleStore Now 2025. From left to right: Jaeho Lee, Posco, Jaenyun Shin, A Platform, Hyeongjun Park, PYLER, DongChan Park, PYLER, Rahul Rastogi, SingleStore, Ranjit Panigrahi, Apple, Hannim Kim, APlatform, and Jaeun Kim, PYLER.

Perhaps just as valuable as the technical sessions was the opportunity to connect with leaders from companies like Apple, Posco, Goldman Sachs, K-Bank, IBM, Kakao, and Adobe. This wasn't just networking; it was an opportunity to exchange ideas with leaders from other data-critical industries—from manufacturing and finance to platform and SaaS.

We found that when we described our unique challenge—processing petabytes of unstructured video data in real-time to ensure trust and safety—it resonated deeply with other industry leaders, partners, and data executives at the conference.. Whether in finance, logistics, or tech, everyone is facing their own version of the 'real-time context' problem. The conversations confirmed that PYLER is not just solving a niche problem; we are solving a universal, cutting-edge data problem at an extreme scale.

The Future: Trust Through Understanding

PYLER engineers Hyeongjun Park and DongChan Park (center and right) connect with an infrastructure engineer at SingleStore Now 2025. The engineer on the left, an attendee from a quantitative firm, discussed their work in infrastructure engineering.

We left SingleStore Now 2025 with a reinforced conviction. The industry is waking up to a problem we've been solving for years: that AI without real-time context is a liability, not an asset.

The event confirmed that our architectural foundation is sound, but our mission is what truly sets us apart. The era of AI agents is here, but it requires a new standard of validation and trust. We don't just build AI; we engineer the understanding that makes it trustworthy. We’re inspired to see partners like SingleStore building the infrastructure that helps make that possible. The future of AI will not be defined by the models themselves, but by our collective ability to safely and verifiably connect them to the real world.

This piece was written by Hyeongjun Park, Backend Engineer and X-Ops Squad Lead at PYLER.