Ω OmniPulse
Docs ↗

Compliance & IP infrastructure for the generative media era

Generative media moved faster than provenance. We rebuilt the substrate.

OmniPulse is a wavelet-scattering fingerprint plane that identifies, attributes, and licenses synthetic and human-authored media in under 40 ms — on-prem, GPU-accelerated, MCP-native.

Module stack

omni-wst-core
0.1PyPI
omni-ffi
0.1crate
omni-hnsw
0.1crate
omni-sw
0.1crate
omnipulse-agent
0.1PyPI

Modular commercialisation

Four tiers, one fingerprint plane

Phase I

Phase I — Institutional Research

Standalone DSP wheels for desktop labs and HPC clusters. C++/CUDA Wavelet Scattering primitives, no orchestration required. Apache 2.0.

pip install omni-wst-core
→ Know how it works↗ View on PyPI

Phase II

Phase II — High-Velocity Concurrent Processing

The vector substrate. omni-hnsw + sliced-wasserstein Rust crates for sub-millisecond nearest-neighbour over billion-scale fingerprint catalogues. Apache 2.0.

cargo add vector-index sliced-wasserstein
→ Know how it works↗ View on crates.io

Phase III

Phase III — Autonomous Agentic Control

Drop-in MCP server. omnipulse-agent parses operator requests and routes them to the Rust orchestrator over line-delimited JSON-RPC 2.0. Apache 2.0.

pip install omnipulse-agent
→ Know how it works↗ View on PyPI

Phase ENTERPRISE

Enterprise SaaS Pipeline

Commercial

Cloud-native multi-tenant deployment. Kubernetes control plane, GPU autoscaling driven by Prometheus queue depth, ed25519-signed license tokens, IPFS pinning.

→ Know how it worksTalk to founders →

Interactive simulator

The fingerprint pipeline, step by step

Drop a WAV file. Step through each swimlane. Every value is deterministically computed from your input — no server calls.

Upload audio
Tensor ingested
SHM pinned
RPC in-flight
FFI bridge
Kernel running
Completed
🐍Python control plane
# SharedMemoryManager.ingest_media_tensor audio = np.array([...], dtype=np.float32) shm_name = shm.ingest_media_tensor(audio) # → "..." (28 hex chars)
🧮Shared-memory pointer
/a3f7b2c1d4e5f6a7b8c9d0e1

SHA3-256(raw).digest()[:14].hex() — 28 chars fits macOS PSHMNAMLEN=31

📡Stdio JSON-RPC envelope
{
  "jsonrpc": "2.0",
  "id": "01J4...",
  "method": "tools/call",
  "params": {
    "name": "generate_fingerprint",
    "arguments": { ... }
  }
}
🦀Rust orchestrator
// server.rs:162-211 — generate_fingerprint handler let signal = shm::read_and_unlink(&req.media_shm_name, n * 4)?; let fp = tokio::task::spawn_blocking(move || { kernel.run(&signal, &cfg) }).await??;
🌉cxx FFI bridge
// omni_ffi_kernel.rs:90-97 unsafe fn run_wst_pipeline( ptr: u64, len: u32, j: u32, q: u32, depth: u32 ) → WSTResult // → omni_ffi::execute_fingerprint_pass(...)
⚙️C++/CUDA kernel
// CPU Morlet fallback — wst_bridge_cpu.cpp AnalyticMorletBank<8, 16> bank; bank.scatter_radix2(ptr, len); // Radix-2 FFT # build: cargo build -p omnipulse-mcp --features omni-ffi
🧭HNSW index (SW₁ metric)
ConcurrentHnsw<PointCloud, SlicedWasserstein> .insert(fp, id) // node 0 // SW₁ distance < 1e-6 → nearest neighbour
0

WstConfig

Kernel

CPU Morlet (Radix-2 FFT)

Custom Pricing & Enterprise Use Cases

Built for organizations where provenance is a legal question.

One fingerprint plane, three high-stakes domains. Every deployment is on-prem, GPU-accelerated, and produces a write-once audit trail. Pricing is contract-based — talk to us.

Major Label · Studio

Derivative detection at catalogue scale

Detect unauthorized derivatives, interpolations, and AI reproductions across a 100 M-asset library. Deterministic Morlet scattering fingerprints — no model, no retraining, no false-positive amnesty. Sub-40 ms per track on H100.

sub-40 ms · Apache audit log · on-prem — no egress

Generative Platforms — Suno · Udio

IP verification before distribution

Every generation gets a fingerprint at creation time. Match probability is computed against the rights-holder catalogue before the track ships. The JSON-RPC audit trail writes the compliance documentation automatically.

per-generation attestation · IPFS pin hash · licensable origin certificate

IP Litigation · Forensics

Expert-witness-grade match probability

The Sliced-Wasserstein distance between two fingerprints is a number a judge can examine. Every comparison frame is a write-once JSON line. No opaque model, no black-box score — a mathematically interpretable result.

full audit replay · on-prem · zero data egress

Custom PricingContract-based · SLA committed
Talk to founders →

Founders & creators

The people behind the plane

ARCHITECT — APPLIED AI / MLOPS

Samvardhan Singh

Automation Engineering & AI/MLOps Research, NielsenIQ

Automation engineering, AI/MLOps pipelines, engineering outcomes.

↗ samvardhan.vercel.app/✉ shekhawatsamvardhan@gmail.com

Maintains

omni-wst-core— C++/CUDA DSP engineomni-ffi— cxx zero-copy FFI bridgeomnipulse-agent— Python agentic control plane (PyPI)

ARCHITECT — SYSTEMS / OPTIMAL TRANSPORT

Yash Mishra

Senior Software Engineer, Bajaj Finserv

Concurrent systems, optimal transport, real-time indexing logic.

↗ www.linkedin.com/in/mishra-yash2002/✉ yash01012002@gmail.com

Maintains

vector-index— Concurrent HNSWsliced-wasserstein— SW₁ distance metric for HNSW

Get started

Ready to integrate the fingerprint plane?

Start with the open-source modules. The core is Apache 2.0 — enterprise production is a conversation.

View the Showcase Repo →