For Developers | Ferment AI — Creative Intent. Deterministic Power.

For Developers

Build With the Engine.
Not From Scratch.

Modular Working Units via API. Faster-than-FFmpeg transcoding. Sub-100ms processing latency.

The Problem

Building sophisticated media processing from scratch is slow and expensive. Stitching together FFmpeg, ML models, and custom pipelines creates fragile infrastructure that's impossible to scale.

The Media Infrastructure SDK

Proprietary Intelligence. Your Stack.

Modular Units

Access specific Working Units — Segmenters, Perceptors, Encoders — via API. Use what you need, nothing more.

Performance

Faster-than-FFmpeg transcoding with sub-100ms processing latency. Optimized for GPU clusters and elastic scaling.

Seamless Integration

Native hooks for Python, Rust, and Ruby environments. Comprehensive documentation and SDK binaries.

Developer Experience

Ship in Minutes.

from ferment import VisualEngine, SegmentInput

result = VisualEngine.process(
    SegmentInput(
        source="footage.mp4",
        prompt="isolate the subject",
        output_format="rle"
    )
)

print(f"Found {len(result.masks)} segments")

use ferment::{AudioEngine, StemInput};

let stems = AudioEngine::separate(
    StemInput::new("track.mp3")
        .with_targets(vec!["vocals", "drums", "bass"])
)?;

for stem in &stems {
    println!("{}: {} samples", stem.name, stem.samples.len());
}

require 'ferment'

result = Ferment::SemanticEncoder.process(
  source: "interview.mp4",
  mode: :hybrid,
  include_audio: true
)

result.embeddings.each do |embedding|
  puts "#{embedding.timestamp} => #{embedding.vector.size}d"
end

Developers Engine

Ready to Scale?

Private GPU clusters, dedicated support, and custom Working Unit configurations for enterprise-grade media pipelines.

Explore Engine

Start Building.

Request API access and explore the full documentation.

Request Access Read the Docs

Build With the Engine.Not From Scratch.