Webinars
Webinar: AI Classification Behind the Scenes
40 minutes with the Papyrus ML team — how classification works, where it fails, how we handle Swahili, and the retraining loop.
Webinar: AI Classification Behind the Scenes
Recorded 28 March 2026 · 40 minutes · Hosted by the Papyrus ML team
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Who this is for
Technical audiences — engineering leads, data scientists, CIOs who want to understand the AI under the hood (not the marketing version).
What we cover
- Pipeline overview: from upload to classification to extraction in 90 seconds
- Architecture: which model for which step (Tier 1 edge ONNX, Tier 2 server ML.NET, Tier 3 cloud LLM)
- Swahili and code-switching: what we did to get from 68% to 94% accuracy
- Confidence scoring and the review queue: where humans come in
- The retraining loop: how user corrections feed quarterly retraining
- Q&A — including: “Can we run our own private model?”
Transcript highlights
"We don't try to use the biggest model for every step. Most operations run on a small classifier we trained ourselves. The big cloud LLM only kicks in when the question genuinely needs reasoning."
"The Swahili improvement wasn't one trick. It was four: more data, custom tokenizer, language-aware routing, hard-negative mining."