Cloudflare deploys three AIs in a relay workflow for claims processing; internal tests show the system is 3.2 times faster and reduces costs by 49%.
1 hours ago
Beating AI News Brief: Cloudflare Releases Preview of Data Extraction Model Clairic Input an invoice or post-sales email, and Clairic can identify order numbers, dates, total amounts, as well as estimate repair costs and predict customer traffic based on available data. Unlike standard LLMs that generate answers token by token, Clairic directly computes and returns results of the specified type, complete with confidence scores. Fields with no supporting data can return null. Clairic follows the System One model paradigm that gained traction after TypeSafe launched Jev. Cloudflare launched decision model Clef on October 1, designed to select from predefined options; Clairic, by contrast, handles extracting, calculating, and predicting specific numerical values. Both models avoid token-by-token answer generation. Clairic costs just $0.10 per million input tokens, with no charges for output tokens. Cloudflare demonstrated the model with a product damage claim use case: Clairic first extracts information and estimates repair costs, Clef decides whether to issue a refund or arrange repairs, and GLM-5.3 drafts the final customer response. The three-model workflow took 19.3 seconds, compared to 61.7 seconds when using GLM-5.3 alone. Per million claims, costs dropped 49% from $11,740 to $6,032. For the information extraction step alone, time fell from 16.1 seconds to 0.46 seconds. That said, while Clairic does not generate output tokens, if fields cannot be processed in a single calculation, the system automatically splits the task into multiple parallel rounds, each re-reading the full input. For high-token inputs like images, this may increase actual call costs. The model is now available in open preview on Workers AI, with weights not yet open-sourced.
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