WhatA middleware API that guarantees LLM outputs strictly conform to a provided schema — no added fields, no type changes, no deviations — by combining constrained decoding, validation, and automatic retries across model providers.
SignalDevelopers relying on structured output from models like GPT are frustrated that models arbitrarily add fields or change types instead of following the spec, breaking downstream systems and requiring manual intervention.
Why NowStructured output is becoming critical infrastructure as AI moves from chat to agentic workflows and production pipelines, but even frontier models still fail at strict schema adherence, and the problem gets worse as models get more capable and 'creative'.
MarketAny developer using LLM APIs for data extraction, code generation, or workflow automation; $1B+ market. Instructor and Outlines handle parts of this but are single-model libraries, not cross-provider reliability layers.
MoatDeep integration testing across every major model's structured output quirks, building a proprietary compatibility and failure-mode database that makes the service increasingly reliable over time.
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