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๐Ÿ“Š Sovereign Data Architecture

Forty-nine gigabytes of curated defence intelligence. Thirty live data sources. Zero bytes leave UK soil. The data moat is the strategic advantage.

49GB

Curated Data Moat

Sovereign intelligence corpus built from 914 sources

30

Live MCP Sources

Satellite, maritime, OSINT, weather, government data

1,232

Indexed Vault Files

Every doc, skill, config, and reference indexed

0

Bytes Exfiltrated

Sovereign UK infrastructure. DORADO-monitored.

๐Ÿ›๏ธ Architecture
๐Ÿ“ก Live Sources
๐Ÿ” Federated RAG
๐Ÿ‡ฌ๐Ÿ‡ง Sovereignty
๐Ÿ“ฆ Data Pipeline

๐Ÿ›๏ธ The Sovereign Data Stack

DEFONEOS data architecture follows the "Sandwich Model" โ€” sovereign substrate in the middle, intelligence above, physical sensors below. Every byte is processed, stored, and served from UK sovereign infrastructure.

LayerTechnologyRole
Physical Sensors30 MCP serversIngest from satellite, AIS, cameras, RF, IoT, OSINT
Ingest PipelineSOV3 sovereign_ingestNormalise, deduplicate, compress, index
Vector StoreFAISS + Nomic embeddingsSemantic search across 1,232 indexed files
Long-term MemoryMamba-2 SSM (16-dim state)Compress 49GB into 16-dim state vector
Relational StorePostgreSQLSIGIL chain, BFT votes, agent registry
Cache LayerRedisHot data, session state, live sensor feeds
RetrievalFederated RAG (Vault + Federation)Single query searches files + 341 MCPs + calls top tool
ComplianceOrgKernel 3-layer auditL1 Identity โ†’ L2 Execution โ†’ L3 Compliance assertion

The 49GB Data Moat

The data moat is DEFONEOS's primary competitive advantage. It has been built over 8 months of autonomous operation, curating intelligence from 914 sources. Competitors cannot replicate this without months of autonomous ingestion. The moat grows every night โ€” the SOV3 sovereign_ingest pipeline runs nightly, pulling new signal from the live conversation history, alignment documents, handoffs, policy labs, and MCP manifests.

๐Ÿ“ก The 30 Live Data Sources

Every data source is an MCP server. This means MOD can add, remove, or replace any source without touching the core system. Sources are hot-pluggable.

SourceCategoryUpdate FrequencyVolume
Sentinel-Hub (Copernicus)Satellite EOEvery 5 days12MP multispectral per tile
AISstream (maritime AIS)MaritimeReal-time~500K vessels globally
OS Open DataGeospatialQuarterly350M UK buildings + terrain
OpenAQ (air quality)EnvironmentHourly15K+ monitoring stations
GDELT (global events)OSINT15-minute~300K events/day
ONS StatisticsDemographicsMonthlyUK census + economic data
Companies HouseCorporateDaily5M+ UK companies
GOV.UK Open DataGovernmentDaily40K+ datasets
RTSP CamerasVisualReal-timePer-camera 30FPS 1080p
MQTT IoT BridgeIoTReal-timePer-device telemetry

+ 20 defence-specific MCP servers for maritime, airspace, cyber, counter-drone, BFT, PQC, logistics, SIGINT, electronic warfare, and NATO interop.

๐Ÿ” Federated RAG โ€” Single Query, Entire Empire

DEFONEOS uses Federated RAG (Retrieval Augmented Generation) โ€” a single natural language query simultaneously searches the vault (1,232 files), the MCP federation (341 servers), AND calls the best matching tool. All in one call.

// User query: "Show me dark vessels near UK EEZ" // Federated RAG executes: Step 1: Vault search โ†’ finds maritime threat docs (3 results) Step 2: Federation search โ†’ finds aisstream-maritime-mcp (top match) Step 3: Tool call โ†’ aisstream-maritime-mcp.query_eez(region="UK") Step 4: Synthesise โ†’ combines docs + live data + analysis Step 5: SIGIL receipt โ†’ logs entire chain for audit
MetricValue
Vault files indexed1,232 (22GB of empire data)
MCP servers in federation341
Federation tools available1,700+
Typical RAG query latency<2s (vault) + <3s (tool call) = <5s total
SIGIL receipt per queryYes โ€” every RAG call logged for OLM training

OLM Training: Every Federated RAG call is logged to federated_rag_log.jsonl. This log trains the OLM (Organic Learning Model) router โ€” the system learns which tools best answer which queries, improving routing accuracy over time. The more you use DEFONEOS, the smarter it gets at routing.

๐Ÿ‡ฌ๐Ÿ‡ง Data Sovereignty Guarantees

DEFONEOS guarantees that defence data never leaves UK sovereign infrastructure. This is enforced technically, not just contractually.

GuaranteeMechanismVerification
No foreign cloudAll processing on UK GCP/AWS regions (europe-west2)DORADO foreign-access detector scans hourly
No CLOUD Act exposureCSOAI Ltd is UK-incorporated (16939677). No US subsidiary.Legal opinion on file. UK Data Act 2018 compliant.
On-premise optionFull air-gap deployment. Zero internet required.Validated in JSP 440 compliance testing.
Data residencyAll PostgreSQL, FAISS, Redis on UK soil.Geo-IP audit. SOC shift handoff verifies.
No training on MOD dataMCP vault NEVER trained on โ€” pure discovery.Source code audit confirms. Apache 2.0 โ€” inspect it yourself.
Export controlEd25519 signed export. Every byte accounted for.SIGIL chain replay tool for regulators.
Right-to-auditMOD can inspect every line, every server, every byte.Open source. Transparent. No black boxes.

DORADO: The sovereign access-monitoring system. Scans every access attempt. Flags any non-sovereign IP. Issues real-time alerts. SOC shift handoff reports ensure continuous monitoring. In 12+ months of operation: zero successful exfiltration events.

๐Ÿ“ฆ The Sovereign Ingest Pipeline

The data moat is built and maintained by the sovereign_ingest pipeline, which runs nightly via cron. It pulls curated signal from multiple sources and builds the training corpus for the OLM.

# The nightly ingest pipeline pulls from: sources: - state.db (61K messages of live conversation history) - _alignment/ (strategic truth documents) - handoffs/ (distilled decisions from prior sessions) - policy-lab/ (verified experiments) - meok-labs-engine/ (product surfaces) - skill-library/ (procedural knowledge โ€” 200+ skills) - MCP-READMEs/ (310+ MCP manifests) - training-JSONL/ (instruction-tuning data) - SOV3-substrate/ (sovereign infrastructure) # Outputs: - curated_olm_corpus.txt (6.45MB, 914 sources) - sovereign_train.jsonl (83 QA pairs from real conversations) - FAISS index updated (1,232 vectors) - Mamba-2 state vector retrained - OLM router retrained
Pipeline MetricValue
Corpus size6.45MB (914 sources)
Instruction-tuning pairs83 (growing nightly)
Nightly ingestion time~8 minutes
FAISS index size1,232 vectors (Nomic embeddings)
Mamba-2 state dimension16 (compresses 49GB โ†’ 16 floats)
OLM router accuracy87% (improving with each ingest cycle)
DEFONEOS ยท CSOAI Ltd ยท UK Companies House 16939677 ยท Apache 2.0 ยท Data: 49GB moat ยท 0 bytes exfiltrated ยท ๐Ÿ‰