General-purpose language models are remarkable, but many production workflows do not need a model that can do everything. Transcribing a meeting, separating speakers, or turning structured input into audio is a concrete job. For those jobs, a specialized model can be the clearer engineering choice.
TaskInfer is built around that idea: task-specific AI capabilities for applications and agents. Each capability presents a focused public contract while TaskInfer handles the operational work required to run the model reliably.
A capability, not another general-purpose model
TaskInfer turns specialized models into task-specific capabilities that applications and agents can call through stable APIs. The customer chooses the task and the behavior they need, without depending on internal implementation details.
This focus keeps inputs, options, and outputs tied to the job. It also lets each capability evolve independently instead of forcing unrelated workloads through one general-purpose abstraction.
Start with Speech to Text
Speech to Text is the first available TaskInfer capability. It accepts the familiar OpenAI Whisper-style multipart request, supports common response formats, and can return timestamped output.
For conversations with multiple speakers, the same transcription endpoint can add speaker diarization. Diarization labels speaker turns in the audio; it does not attempt to identify people in the real world.
Production infrastructure around the model
A production inference API needs more than a model call. It needs authentication, request validation, metering, size limits, predictable errors, and a stable public contract. TaskInfer brings those concerns together so teams can add focused AI capabilities without operating every model themselves.
That same focus guides the developer experience:
- familiar request shapes where compatibility helps adoption;
- explicit task options instead of open-ended prompting;
- documentation generated alongside the public API contract;
- consistent authentication and error handling.
Expand one concrete task at a time
Speech to Text remains the quickest entry point, and Audio Separation is now available as an asynchronous API for dialogue, music, and effects tracks. Text to Speech with voice cloning and capabilities outside audio will be added to public documentation only after their callable contracts are ready.
This keeps the promise simple: every capability shown in the API documentation should be something developers can actually integrate.
Read the documentation, get started, or create a workspace to get an API key.