Sovereign Artificial Intelligence

Unite Your Intelligence.
Keep Your Data.

Collaborate on AI models across hospitals, banks, and enterprises — without moving a single record. The production-grade Rust daemon for enterprise federated learning.

Install CLIRead Docs
bash
$ cargo install sangrah-cli
    Updating crates.io index
  Downloaded sangrah-cli v1.2.4
   Compiling sangrah-core v1.2.4
   Compiling sangrah-crypto v0.9.1
   Compiling sangrah-cli v1.2.4
    Finished release [optimized] target(s)
  Installing ~/.cargo/bin/sangrah

$ sangrah init --node enterprise-a
Initializing secure enclave...
Success: Node identity generated.

Built for regulated industries

mTLS Everywhere
Ed25519 Signing
Zero Data Ingestion
Secure Aggregation
View Security Architecture →

Architecture

How It Works

Three steps to collaborative AI without compromising data sovereignty.

01

Deploy in Your VPC

Install the Sangrah daemon inside your infrastructure. Your data never leaves your boundary.

02

Train Locally

Models train on your nodes using your data. Only cryptographically secured gradients are produced.

03

Aggregate Securely

The coordinator aggregates masked gradients across the federation — no raw data is ever shared.

01 / The Problem

The Great Compromise.

For a decade, the pursuit of artificial intelligence has demanded a dangerous sacrifice: the pooling of sensitive, proprietary data into centralized silos.

Organizations were forced to choose between leveraging collective intelligence and maintaining absolute data sovereignty. The result was stalled innovation in highly regulated sectors and unacceptable risk profiles for those who moved forward.

0

Data breaches in 2024

$4.88T

Global cost of breaches

“The centralized approach to AI forces a false choice between intelligence and privacy.”

— The Federation Manifesto
Centralized Silo
Hospital A
Bank X
Corp Z
Lab M
↓ Data flowing to centralized silo

Hospitals, banks & enterprises forced to pool raw data
into vulnerable centralized silos — creating massive attack surfaces

SangrahSecure Aggregation
Hospital APatient Records
Bank XTransaction Data
Research LabGenomic Data
Enterprise ZProprietary Data
02 / The Sovereign Network
Model out Gradient in

Models travel to data — data never travels to models
Only encrypted gradients leave your perimeter

02 / The Solution

Decentralized Power.

Sangrah flips the paradigm. Instead of bringing your data to the model, we bring the model to your data.

Through our secure enclave node architecture, models train locally within your infrastructure. Only cryptographically secured gradients—never raw data—are shared across the network.

Zero Data Ingestion

Your raw data never leaves your infrastructure. Models come to you.

Cryptographic Gradient Aggregation

Only masked, encrypted model updates are transmitted across the network.

Sovereign Node Identities

Ed25519-signed identities ensure every participant is verified and authorized.

Platform

Command Your Federation

Monitor training rounds, audit every gradient exchange, and manage participants — all from a unified dashboard built for enterprise operators.

  • Real-time epoch & round monitoring
  • Participant health & enrollment
  • Privacy budget & audit trail
Explore Dashboard
Federation Dashboard

Active Epoch

24Round 3/5 · 5 nodes

Participants

5/5

Healthy

Privacy ε

3.2

Within budget

Models

12

Signed

Drift

None

Stable

Implementation

A Foundation of Rust.

Built for extreme performance and memory safety. The Sangrah daemon runs transparently within your VPC, requiring minimal overhead while providing maximum cryptographic security.

bash / deployment
$ cargo install sangrah-cli
    Updating crates.io index
  Downloaded sangrah-cli v1.2.4
   Compiling sangrah-core v1.2.4
   Compiling sangrah-crypto v0.9.1
   Compiling sangrah-cli v1.2.4
    Finished release [optimized] target(s)
  Installing ~/.cargo/bin/sangrah

$ sangrah init --node enterprise-a
Initializing secure enclave...
Success: Node identity generated.
Status: Awaiting federation parameters...
Technical DocumentationView Source

Industries

Built for Sensitive Domains

Where data sovereignty isn't optional — it's the law.

Healthcare

Cross-Hospital Research

Train diagnostic models across hospital networks without centralizing patient records. HIPAA-aligned by architecture.

12 hospitalsConnected in federation
HIPAAPHI Safe

Finance

Fraud Detection at Scale

Collaborate on fraud models across institutions while keeping transaction data within each bank's perimeter.

99.7%Detection accuracy
SOXPCI-DSS

Research

Academic Consortia

Enable multi-institution ML research — up to 5 federation nodes for open collaboration.

5 nodesFree forever
Open AccessReproducible

Contact

Get in touch

Want to try Sangrah or book a demo? Send us a message and we'll get back to you within 24 hours to help you explore federated learning for your organization.

Trusted by leading organizations

Helix Research
Nova Health
VaultBank
Meridian Labs
Axiom Finance
Cortex Institute
Helix Research
Nova Health
VaultBank
Meridian Labs
Axiom Finance
Cortex Institute

FAQ

Common Questions

No. Sangrah never ingests raw data. Models train locally within your infrastructure, and only cryptographically secured gradients are shared across the federation. The coordinator aggregates masked updates without accessing individual contributions.

A Sangrah node runs as a lightweight Rust daemon inside your VPC or on-premise environment. You need outbound connectivity to the federation coordinator over mTLS. Typical deployment takes under 2 hours for a single node.

Centralized ML requires pooling data in a shared location — creating compliance risk and single points of failure. Sangrah brings the model to your data, enabling collaborative training while each organization retains full sovereignty over their records.

Sangrah supports configurable (ε, δ)-differential privacy, secure aggregation via pairwise masking, and privacy budget tracking per epoch. Training automatically halts when privacy thresholds are reached.

Reach out via email at arthsrivastava1@gmail.com, WhatsApp at +91 94518 07965, or call +91 87997 20386. We'll walk you through a demo, discuss your use case, and help scope your federation deployment.