A small decision model served over a REST API that returns typed labels, scores, spans, and JSON fields from text or images in milliseconds.

Milliseconds.ai works by combining Typed Decision Endpoints: Eight purpose-built routes — yes-no, classify, classify-tree, rate, answer, extract, entities, and verify — each returning structured JSON rather than free text, so application code can branch on the result immediately., Sub-Second Latency: A decision returns in about 90 milliseconds, answer calls in 0.3–0.9 seconds, and extraction in 2.5–3.5 seconds at medium detail, making the model usable inside request paths rather than background jobs., Calibrated Probabilities: Responses include per-label probabilities and a confidence value, so near-ties surface as uncertainty your application can route to a human instead of acting on silently., Schema-Driven Extraction: Send a JSON Schema and get back a filled object — up to five fields per extract call — ready for validation before writing to a record., Image Input: Send JPEG, PNG, or WebP images up to 5 MB as bytes, a data URL, or base64, billed as a fixed token count set by the detail level you request, with no image storage retained. to help users with Support Ticket Routing: Classifying inbound messages into billing, shipping, or technical queues and flagging urgent ones for faster response., Invoice and Receipt Processing: Extracting invoice number, vendor, total, and currency from document text or images into validated fields before writing a record., Content Moderation and Policy Checks: Verifying whether a return request, listing, or submission satisfies a written policy before it reaches a human reviewer., Sentiment and Priority Scoring: Rating customer frustration on a defined scale to sort a support queue by how badly each thread needs attention., Entity Recognition in Records: Pulling people, organizations, claim IDs, and dates out of free-text notes for search and record matching..

Key features include Typed Decision Endpoints: Eight purpose-built routes — yes-no, classify, classify-tree, rate, answer, extract, entities, and verify — each returning structured JSON rather than free text, so application code can branch on the result immediately., Sub-Second Latency: A decision returns in about 90 milliseconds, answer calls in 0.3–0.9 seconds, and extraction in 2.5–3.5 seconds at medium detail, making the model usable inside request paths rather than background jobs., Calibrated Probabilities: Responses include per-label probabilities and a confidence value, so near-ties surface as uncertainty your application can route to a human instead of acting on silently., Schema-Driven Extraction: Send a JSON Schema and get back a filled object — up to five fields per extract call — ready for validation before writing to a record., Image Input: Send JPEG, PNG, or WebP images up to 5 MB as bytes, a data URL, or base64, billed as a fixed token count set by the detail level you request, with no image storage retained..

Milliseconds.ai is useful for anyone interested in Support Ticket Routing: Classifying inbound messages into billing, shipping, or technical queues and flagging urgent ones for faster response., Invoice and Receipt Processing: Extracting invoice number, vendor, total, and currency from document text or images into validated fields before writing a record., Content Moderation and Policy Checks: Verifying whether a return request, listing, or submission satisfies a written policy before it reaches a human reviewer., Sentiment and Priority Scoring: Rating customer frustration on a defined scale to sort a support queue by how badly each thread needs attention., Entity Recognition in Records: Pulling people, organizations, claim IDs, and dates out of free-text notes for search and record matching..

Milliseconds.ai offers a free tier with paid plans for advanced features.

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