Interfaze

Interfaze's bet is that the interesting AI workloads in 2026 aren't conversations but deterministic pipelines — parse this invoice, transcribe this call, classify this ticket — and that those workloads need something a general-purpose chat model doesn't give you: a way to know when the model is wrong. That's the real product. Ask Interfaze to pull first_name, last_name, dob, and driver_licence_number off an ID and you don't just get strings back. You get a precontext payload where each field carries a confidence value (the docs example shows 0.99, 1.0, 0.98) and bounding boxes with top_left/bottom_right coordinates. You can auto-accept anything above 0.95, route the rest to a human, and keep a paper trail for the auditor. For teams in insurance, healthcare intake, legal discovery, or KYC, that's the difference between a demo and something you can put in production. The architecture is a hybrid Mixture-of-Architecture: specialized DNNs and CNNs handling the precision-sensitive parts, a transformer layer providing flexibility, and a chain of multipass preprocessing steps that expand input context in exchange for higher determinism and lower output token cost. That tradeoff is visible in the billing — Interfaze's own docs warn that your input token count can exceed the input you actually sent, because the preprocessing chain inflates it. The published rate is $1.50 per million input tokens and $3.50 per million output tokens, but your effective input spend can be higher than that number implies. The company reported up to 10x cost reduction on 2026-07-29 through token efficiency and caching work, and since cached tokens aren't counted toward usage, repeat workloads benefit more than one-shot ones. Cost by workload is published and unusually concrete: $0.002–$0.004 per OCR'd document page via run-task (roughly $2–$4 per 1,000 pages), $0.016 per extracted ID or invoice (~$16 per 1,000), $0.006 per minute of audio transcription, $0.03–$0.08 per scraped and structured web page, and about $0.006 per classified or moderated text snippet. If you've been pricing a Document AI or cloud OCR vendor per page, those numbers are worth comparing directly. The bundled infrastructure is the quiet value. A sandboxed code execution environment and a headless browser engine ship inside the same API as the model, with no separate infrastructure charge — infrastructure usage converts to tokens and lands in your input/output counts. So does STT, speaker diarization, time-series forecasting, GUI detection, OpenWebSearch, and object detection. Teams that would otherwise run Playwright on a separate box and ETL the results now make one call. Safety tooling is configurable rather than opinionated: you pass a guard array naming the categories you want (S1 violent crimes through S14 code interpreter abuse, plus S12_IMAGE for image content) and the model filters accordingly. That's a different posture from vendors who bake one moderation policy into the endpoint. Where it