Sieve

What is Sieve?

Sieve is an AI platform built for cloud operations, with a focus on audio and video enhancement. It gives developers, designers, and product managers the tools to build their own AI applications while keeping full control and ownership. The platform ships with pre-built models and applications, plus a collaborative space where teams can experiment. Its infrastructure scales automatically to handle increased traffic without manual configuration.

Deploying custom models is straightforward: you define your code’s dependencies and compute requirements, then deploy with a single command. Ready-to-use applications and models can be combined with just a few lines of code, speeding up deployment of customized AI models.

Who created Sieve?

Sieve was developed by a team of developers at Sieve and launched on July 5, 2022. The core idea is to empower developers, designers, and product managers to build custom AI applications with complete control and ownership, supported by ready-to-use models, collaborative playgrounds, and automatically scaling infrastructure.

Key Features

• Transcription: Transcribe audio and video content.
• Lip syncing: Sync lips in videos to match audio.
• Background noise removal: Clean up audio by removing background noise.
• Background cleanup: Clean up backgrounds in videos and images.
• Scalability: Automatically handles increased traffic with no manual configuration.
• Collaboration: Auto-generated playgrounds let teams experiment and share tools together.

Pricing

Sieve uses a flexible pricing model based on compute usage. You’re charged for what you actually use, which helps keep costs in check.

Getting Started

1. Sign up and explore the ready-to-use models for transcription, lip syncing, and background removal.
2. Implement these capabilities with a few lines of code to get AI-powered features working quickly.
3. When you need something custom, define your model’s dependencies and compute type, then deploy with a single command.
4. Use the collaborative playgrounds to experiment with your team and share what works.