Harness the magic
of compute beyond
the cloud
Keep systems running smoothly and cut cloud costs by processing data right where it's created.
Stop sending
your data
to the cloud
75% of data today is created at the edge.
Why send it to the cloud when we can leverage it where it already is?
Edge-first data
must be
Edge Native
On-device computation reacts to data as it's produced or received, without waiting on the cloud. Reduce latency, slash bandwidth costs, and make decisions in real-time, even if connectivity is unreliable.
Real-Time Data Hooks
Event-driven listeners on the device that trigger processing pipelines the moment new data is created or received, no cloud round-trip needed.
Incremental State Management
Local state stores that fold or diff incoming changes incrementally, keeping memory and CPU footprints minimal while preparing data for sync.
Offline-First Processing
Background workflows that queue, process, and checkpoint data mutations during disconnection, then seamlessly reconcile when connectivity returns.
Proximity-Optimized Analytics
Edge-tuned operators (aggregations, filters, transforms) that reduce data before any sync, slashing bandwidth and speeding up insights.
Peer-to-Peer
Data moves through direct or relay-based unprivileged peer nodes, bypassing centralized servers. This eliminates single points of failure and high-visibility attack targets.
Network Topology
Fully decentralized node network using P2P Libraries multi-addresses derived from asymmetric keypairs
Data Structure
Single evolving Merkle DAG per document with static Document Key IDs, maintaining consistency across mutations
State Propagation
Pub/Sub messaging for efficient head-tracking with interest-based subscriptions
Security Model
Keypair-based access control with edge verification replacing traditional authentication systems
Synchronization
Automatic conflict resolution through CRDT semantics, ensuring eventual consistency during intermittent connectivity
Interoperable
Tear down walls between data systems and formats; enable omnidirectional data flow for on-device analysis and processing.
Schema-Agnostic Adapters
Runtime data mappers that automatically reconcile evolving formats and versions without manual migrations.
Unified Transformation Pipelines
On-device engines that detect input types and apply standardized transforms to harmonize data flows.
Metadata-Driven Routing
Self-describing payloads carrying schema and version information to direct and reshape data between systems.
Format-Independent Messaging
Publish/subscribe channels capable of handling any data schema, decoupling producers and consumers.
Portable
Manage data wherever it originates or travels, whether that’s mobile, desktop, server, browser, or even embedded device.
Cross-Platform Runtime
A single core engine compiled to native binaries, WebAssembly, or JavaScript for seamless deployment on any device.
Swappable Storage Adapters
Pluggable backends for IndexedDB, SQLite, file systems, and in-memory stores that auto-select based on the host environment.
Host-Neutral API
Uniform library interfaces across languages and platforms, ensuring identical behavior whether in a browser, server, or embedded context.
Zero-Config Bundling
Automated packaging tools that detect the target environment and produce optimized bundles with minimal developer overhead.
Secure
Data you can't protect, you can't trust. Encrypt end-to-end, both in transit and at rest. Protect keys so only authorized devices and parties can access them.
Threshold Secret Rings
Collaborative Key Generation (CKG) produces aggregated public keys and requires a quorum of individual keyshares to decrypt or sign.
Proxy Re-Encryption Streams
Re-encryption keys enable ciphertext migration between public keys without ever exposing plaintext.
Zero-Knowledge KMS
User-friendly key management combining zero-knowledge proofs with public-key operations, offering both simplicity for end users and full control for advanced stakeholders.
Automatic End-to-End Encryption
Seamless on-device encryption/decryption pipelines for data at rest and in transit, with hardware-backed isolation where available.
Private
Data-level permissions travel with the data as it syncs, ensuring it's only accessed and processed as authorized.
Policy-Driven Access Control
A decentralized policy engine built on DIDs to enforce permissions consistently across all devices and data flows.
DID-Backed Permission Receipts
Each record carries a cryptographically signed access receipt that any node can verify before allowing operations.
Multi-Model Authorization Support
Built-in frameworks for RBAC, ABAC, ReBAC, MAC, and DAC enable granular, context-aware permissioning.
Partitioned Policy Enforcement
Policies are sharded and processed in parallel across devices, ensuring real-time, scalable enforcement even when disconnected.
Embedded Compliance Controls
Governance rules for consent, data residency, and minimization are baked into the data layer, guaranteeing regulatory adherence at the edge.
Verifiable
Each record contains cryptographic proof (signatures, hashes, Merkle trees) for end-to-end verifiability. Power multi-device collaboration over a shared source of truth, while supporting compliance and forensic audits.
Content-Addressed Event Chains
Every mutation is stored as a Merkle DAG node referencing its predecessor, ensuring tamper-evident, content-addressable history.
Causal Merkle Clocks
Directed acyclic graphs enforce forward-only event ordering, eliminating circular dependencies and preserving causal consistency.
Embedded δ-State CRDTs
State-based and operation-based CRDT payloads live inside each Merkle node, combining convergent and commutative replication semantics.
Recursive ZK Verification
Recursive zero-knowledge proofs enable high-performance snapshotting and integrity checks without traversing long DAGs.
Decentralized Identity Management
Self-sovereign identities via the DID protocol, enabling secure, cloud-independent authentication for users and services.
Tamper-Evident Auditing
Append-only provenance logs with cryptographic proofs, providing full traceability, accountability, and regulatory compliance.
AI Ready
Format data through consistent schemas, normalized feature sets, and embedded metadata. Directly ingest into training pipelines, inference engines, or vector stores.
Reduced Latency
Edge AI eliminates the need for cloud round-trips, enabling real-time inference on-device.
Enhanced Privacy
On-device processing keeps sensitive data local, minimizing breach risk.
Improved Reliability
AI models continue to operate offline or on intermittent networks, ensuring uninterrupted service.
Lower Costs
Local inference reduces bandwidth use and cloud compute expenses by cutting data egress.
Energy Efficiency
On-device ML slashes data transfer and cloud energy demands, lowering carbon footprint.
Source Makes
Edge-First Real
Focus on developing your products. Let us address the challenges of data management for the edge.
Break free from cloud dependencies with Source, the edge-first data management stack that reduces costs and eliminates centralized bottlenecks.
Build software that workseven when the internet doesn't
Space & Satellite Systems
Edge AI & Machine Learning
Smart Cities & Infrastructure
Autonomous Systems
Industrial IoT
Connected Transportation
Agriculture & Environmental
Why Developers Should Build On Source
Dwight Merriman
Co-Founder of MongoDB
"Modernization of the data layer in our systems is overdue. In a semi-centralized data center scenario, there are new databases, with new data models (often document-oriented), that work better for the use cases of today, which involve new data shapes, evolving schemas, polymorphism, and iterative development."
Mike Faltys
CTO of Phantom Neuro
"Source has solved a complex problem that addresses a critical need: enabling peripherals to manage data distribution independently, without requiring a constantly present central server."
Hood Khizer
Technical Director of AHOY
"Source's architecture addresses the critical challenge of decentralized data coordination – a key requirement for truly decentralized machine learning [applications] – through an innovative protocol with sophisticated key management and robust device discovery."
Carlos Herrera
CEO of Anymal
"Anymal leverages DefraDB as a portable, user-centric database to store and manage individual animal records, enabling trustless self-custody, granular attribute-level privacy, and seamless data portability across the ecosystem. Its intuitive GraphQL interface and outstanding developer support make it simple to build powerful, privacy-preserving applications for every corner of the animal economy."
Frequently Asked Questions
Source stack is designed for edge environments. Unlike cloud databases that require central coordination and constant connectivity, our databases, DefraDB, enables offline-first functionality, peer-to-peer sync, and local authority, making it ideal for edge and distributed environments, local-first software, edge AI, intermittently connected, or remote systems.
Source provides an edge-first data management stack with built-in offline functionality, peer-to-peer synchronization, verifiability, and flexible data governance. Developers can build resilient, responsive applications that don't depend on a constant cloud connection. Synchronization conflicts are resolved using a specialized data structure: Conflict-Free Replicated Data Types (CRDT).
DefraDB is designed to be highly adaptable. It runs across various edge environments, from smartphones and industrial sensors to satellites and remote servers. DefraDB can be embedded natively onto chips, integrated into device software, or deployed within operating systems, depending on the use case and compute power.
Source's stack enables peer-to-peer synchronization by allowing devices to securely share and merge data directly with each other without needing a centralized server. This creates a resilient, trustless network where each device holds only the data it needs and collaborates dynamically. Devices can passively sync to a secure global pubsub or actively to a specific target peer.
Source was designed for AI systems that need to think locally. Its cryptographically verifiable data structures ensure trust in training and inference pipelines, while built-in privacy controls keep sensitive data on-device. Support for federated learning, RAG (Retrieval-Augmented Generation), and offline vector search means you can deploy smaller and more specialized models where the data lives, with no reliance on centralized infrastructure.
Privacy, security, and governance aren't add-ons at Source—they're foundational. The stack supports end-to-end encryption, verifiable data integrity, and fine-grained access controls that travel with your data. Rather than routing everything through centralized intermediaries, Source enables secure, local-first enforcement of data policies across distributed environments.
Yes. Source is cloud-agnostic and interoperable by design. It can deploy natively or sync with major cloud platforms when needed, enabling hybrid edge-cloud deployments without forcing developers into vendor lock-in.
Source is a fit for any application where performance, resilience, and privacy matter. This includes smart cities, robotics, autonomous systems, industrial IoT, remote sensing, mobile apps, defense and space, and AI/ML systems.
Source's DefradB uses cryptographically verifiable data structures and Merkle CRDTs (Conflict-Free Replicated Data Types) to ensure every change is traceable, mergeable, and tamper-evident. Conflicts resolve automatically or via developer-defined logic—no need for a central authority. This makes it possible to synchronize data seamlessly across distributed devices, even in offline or low-connectivity environments.
Source provides developers with a suite of tools, including APIs and a CLI, designed for seamless integration into existing workflows. We offer language-specific support through our GraphQL interface, which enables the generation of bindings and SDKs for various programming languages. Additionally, WASM builds are available for direct embedding within applications.
Yes. Source's DefraDB natively supports vector embeddings and retrieval-augmented generation (RAG) pipelines. It's built to power AI workloads at the edge, enabling local search, recommendation, and inference systems with full privacy and verifiability.
Source's database, DefraDB, is built to run at the edge. Its architecture is optimized for moderate-low-power, resource-constrained devices—whether you're embedding it in a mobile app, running it on a sensor, or syncing across a mesh network. The stack compiles down to lightweight binaries, so it performs with minimal overhead while preserving full functionality, even offline.
Source is being used by pioneering developers and organizations building local-first software, mobile apps, autonomous systems, remote sensing networks, and AI/ML applications at the edge. Industries from distributed energy, telecommunication, and satellite to industrial IoT are adopting Source today.
