Text Classification
One fine-tuned 350M model, three enterprise scenarios. See why sub-50ms classification unlocks use cases that cloud LLMs physically cannot serve.
The Problem
Content moderation at gaming and AdTech speed requires sub-50ms classification, but keyword filters miss sarcasm and context, and custom BERT models take months to build and a dedicated MLOps team to maintain.
How LFM Compares
Keyword filters are fast but miss sarcasm and context. Custom classifiers need ML teams and months to train. LFM delivers semantic classification at 40ms with new categories via LEAP in minutes.
What LFM Unlocks
Custom content categories deployed in 10 minutes via LEAP at 40ms, with no ML team. New toxicity patterns or brand safety rules go from request to production same-day.
Semantic Classification Engine
Low-latency semantic intelligence as middleware — replacing brittle regex rules with a fine-tuned 350M model that understands meaning. Sub-50ms inference makes it a drop-in for real-time pipelines that can't tolerate cloud API round-trips.
Throughput Stress Test
52 inputs classified in real-time — LFM inference vs simulated Cloud LLM.
Classification Progress
LFM Metrics
Cloud LLM Metrics
Sample message pool (52 messages)
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- Thanks for the quick response on my support ticket
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- The API documentation needs updating for v2 endpoints
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- The server is returning 500 errors on the /users endpoint
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- Great job on the presentation yesterday, team!
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- The deployment pipeline is failing on the staging branch
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- The new feature rollout is scheduled for next sprint
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- How do I integrate with the webhook API?
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- The CI build is green, ready for code review
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This demo is fine-tuned on sample data. Results improve with your data.