Manual maintenance stalled the data director. A 9B-vector engine automated stability, freeing the team to scale scanning operations.
A digital identity protection platform safeguards public figures from deepfakes and impersonation by scanning hundreds of millions of images and videos daily across the public internet.
The platform's initial vector database required frequent manual maintenance to ensure stability, forcing the Director of Data Science to divert time...
“Simply put, the more internet we scan, the better we can protect our customers.”
Loti AI's Digital identity protection is part of this use case:
AI platform for digital identity protection and automated likeness monitoring.
Open-source vector database for building AI applications and search experiences.
Related implementations across industries and use cases
Manual teams couldn't catch scams that vanish in an hour. AI now filters noise and classifies threats, cutting response time to minutes.
Isolating data for 50,000 clients was operationally impossible. Multi-tenancy now serves 6.1M queries from a single instance.
Keyword search choked on 500k files. Switching to vector embeddings now scales detection to 250M audio fingerprints in real time.
Pitch prep took days of manual searching. AI retrieves clips from 10M assets in <300ms, cutting proposal timelines from 7 days to 2.
Frame-by-frame analysis bottlenecked R&D. Direct video processing cut development to two weeks, enabling a launch in just one month.
Scattered spreadsheets couldn't catch AI hallucinations. Now, automated LLM judges evaluate every prompt change to block regressions.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Repetitive coding slowed R&D. Now 80% of engineers use agentic tools to automate work, saving up to 2 hours weekly per person.
Manual testing consumed 20% of developer time. Now, 1,500 engineers use AI agents to auto-generate tests and prototype solutions.
Manual maintenance stalled the data director. A 9B-vector engine automated stability, freeing the team to scale scanning operations.
A digital identity protection platform safeguards public figures from deepfakes and impersonation by scanning hundreds of millions of images and videos daily across the public internet.
The platform's initial vector database required frequent manual maintenance to ensure stability, forcing the Director of Data Science to divert time...
“Simply put, the more internet we scan, the better we can protect our customers.”
Loti AI's Digital identity protection is part of this use case:
AI platform for digital identity protection and automated likeness monitoring.
Open-source vector database for building AI applications and search experiences.
Related implementations across industries and use cases
Manual teams couldn't catch scams that vanish in an hour. AI now filters noise and classifies threats, cutting response time to minutes.
Isolating data for 50,000 clients was operationally impossible. Multi-tenancy now serves 6.1M queries from a single instance.
Keyword search choked on 500k files. Switching to vector embeddings now scales detection to 250M audio fingerprints in real time.
Pitch prep took days of manual searching. AI retrieves clips from 10M assets in <300ms, cutting proposal timelines from 7 days to 2.
Frame-by-frame analysis bottlenecked R&D. Direct video processing cut development to two weeks, enabling a launch in just one month.
Scattered spreadsheets couldn't catch AI hallucinations. Now, automated LLM judges evaluate every prompt change to block regressions.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Repetitive coding slowed R&D. Now 80% of engineers use agentic tools to automate work, saving up to 2 hours weekly per person.
Manual testing consumed 20% of developer time. Now, 1,500 engineers use AI agents to auto-generate tests and prototype solutions.