Speech recognition has moved far beyond simple phoneme matching. Modern pipelines now combine dedicated audio encoders with large language models to produce structured, context-aware transcripts that capture speaker labels, technical terminology, and formatting. For developers building these systems, the infrastructure challenge is not just model accuracy, but managing the cost and latency of sending long audio transcripts through LLM post-processing. Oxlo.ai offers a developer-first inference platform that addresses this directly with request-based pricing and a full suite of audio and language models accessible through a single OpenAI-compatible API.
Why LLMs Matter for Modern Speech Recognition
Traditional ASR systems output a flat text stream. That is often insufficient for production workflows in legal, medical, or customer-support domains where transcripts must include speaker diarization, timestamp alignment, domain-specific terminology correction, and structured formatting. Feeding raw audio through a model such as Whisper is only the first step. The
