Processing trillion-token datasets took months. A native vector engine cut deduplication costs 5x and doubled processing speed.
A leading large language model provider operates a conversational AI platform serving tens of millions of monthly active users while managing petabytes of unstructured training data.
A Redis-based architecture struggled to deliver sub-30ms recommendations during traffic peaks, requiring expensive plugins that increased latency....
Multimodal AI models and applications for text, speech, music, and video generation.
Vector database platform for building and scaling AI applications.
Related implementations across industries and use cases
Search slowed to 1000ms as transcripts hit 30M hours. Migrating the vector engine cut latency to 100ms—10x faster.
Keyword search choked on 500k files. Switching to vector embeddings now scales detection to 250M audio fingerprints in real time.
A separate vector DB failed performance benchmarks. Consolidating on one platform scaled daily processing from 200k to 1 billion tokens.
Sales reps manually sifted 200M contacts. Vector search now creates dynamic "playlists" of high-value leads based on intent.
Trainers manually analyzed scattered sleep and diet logs. AI now unifies the data to trigger instant coaching insights.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Sellers lost hours to manual research. AI agents now prioritize leads and draft briefs, cutting prep time by 80%.
Hundreds of pages per board book slowed director prep. Now, isolated AI securely condenses sensitive materials into actionable briefs.
Custom hardware bottlenecked imaging. A switch to software-defined GPUs now renders photorealistic 3D hearts in real time.
Processing trillion-token datasets took months. A native vector engine cut deduplication costs 5x and doubled processing speed.
A leading large language model provider operates a conversational AI platform serving tens of millions of monthly active users while managing petabytes of unstructured training data.
A Redis-based architecture struggled to deliver sub-30ms recommendations during traffic peaks, requiring expensive plugins that increased latency....
Multimodal AI models and applications for text, speech, music, and video generation.
Vector database platform for building and scaling AI applications.
Related implementations across industries and use cases
Search slowed to 1000ms as transcripts hit 30M hours. Migrating the vector engine cut latency to 100ms—10x faster.
Keyword search choked on 500k files. Switching to vector embeddings now scales detection to 250M audio fingerprints in real time.
A separate vector DB failed performance benchmarks. Consolidating on one platform scaled daily processing from 200k to 1 billion tokens.
Sales reps manually sifted 200M contacts. Vector search now creates dynamic "playlists" of high-value leads based on intent.
Trainers manually analyzed scattered sleep and diet logs. AI now unifies the data to trigger instant coaching insights.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Sellers lost hours to manual research. AI agents now prioritize leads and draft briefs, cutting prep time by 80%.
Hundreds of pages per board book slowed director prep. Now, isolated AI securely condenses sensitive materials into actionable briefs.
Custom hardware bottlenecked imaging. A switch to software-defined GPUs now renders photorealistic 3D hearts in real time.