Models & capabilities, read from an operations desk

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Cloudflare previews Clairic, an extraction model on Workers AI

Cloudflare has opened a preview of Clairic, an extraction model on Workers AI that reads text or images and returns typed values with confidence scores in one pass. It bills input only and answers with null when a field is missing. Open weights are planned but not yet released.

What changes for operators — If your support or finance team reads order numbers, dates, amounts and damage descriptions out of inbound emails, receipts or invoices, this is a cheaper way to fill those fields than prompting a general LLM for JSON. The confidence score and null probability give you a rule for routing: auto-process high-confidence values, send low-confidence ones to a person. The estimate and forecast fields could also feed lead scoring or volume planning. These are Cloudflare's own examples, so run it on a sample of your real documents before wiring it into anything that moves money.

Cloudflare's Clef-omni adds audio, video decisions

For a support or ops team using classification models to triage tickets, screen uploads for PII, or flag suspicious content, Clef-omni removes the need to stitch together separate speech-to-text and video-frame extraction steps before scoring — a customer complaint with a voice memo or video attachment can now go through one call. The Clef-flash price cut (now below Jev's pricing) and the 2x speed gain on Clef make it cheaper and faster to run these decision checks at the volume a 10-200 person company's ticket or document queue actually generates, though teams with inputs over 24k tokens per request will need to use the pricier Clef model instead of Clef-flash.

Cloudflare Ships Open-Source Clef Models for Support Triage

The example Cloudflare publishes is the exact workflow most support teams already run by hand or with brittle rules: a message comes in, someone decides if it's urgent, which team owns it, and how severe it is. Clef does all three in one API call with typed, probability-scored outputs your existing code can act on directly, no LLM prompt engineering or free-text parsing required. For a 10-200 person company this means ticket routing, escalation flags, and severity scoring can run as a deterministic step inside an existing support stack, with humans only pulled in when the model defers. The new RL fine-tuning option (via Cloudflare's hands-on team first, self-serve later) also means a company with a backlog of labeled support tickets or sales qualification outcomes can tune the model on its own data rather than relying on a generic classifier.