v0.4.0 · PRODUCTION READY

INDB

Not a database.
An Epistemological Engine
designed to preserve truth in the age of manipulation.

INHALE EXHALE AXIOM
SCROLL
"When everyone lies,
INDB remembers what you saw."
02

CORE
PHILOSOPHY

01

01 · INHALE

Accept chaotic reality

Multi-protocol intake — UDP, gRPC, HTTP, WebSocket. Raw, unfiltered, no judgment. Only observation.

< 1ms latency · 1000+ events/sec
02

02 · EXHALE

Compress truth. Purge noise.

Reputation is gravity. The system exhales lies as waste heat. Only signal survives.

Mass = Count × Reputation²
03

03 · AXIOM

Irrefutable cryptographic core

What remains is Ed25519-signed, AES-256-GCM encrypted, immutable truth. Cannot be rewritten.

99.7% success rate under load
TYPE

MINIMAL.
UNIVERSAL.
INDESCRIBABLE.

INDB has no schemas. No migrations. No column types. Everything is an Event — a universal container with just 7 fields. The type system is intentionally minimal: if you need more, you're thinking wrong.

fieldtypepurpose
idstrUUID v4 — auto-generated, immutable identity
timestampfloatUnix epoch — time of original observation
raw_data_anchorList[str]Tokenized signal fingerprint — universal & abstract
locationstr | OSMSpatial anchor — plain path or OSM {lat, lon, osm_id}
ttlint | NoneTime-to-live in seconds (None = permanent)
fusion_countintHow many events have merged into this one
source_idstr | NoneTrust chain reference — verified source registry
blind_payloadstr | NoneZero-knowledge encrypted blob — engine never reads
binary_payloadbytes | NoneRaw binary: images, streams, gRPC — any format

BLIND TYPE

blind_payload is zero-knowledge encrypted storage. The intelligence layer never sees its contents — it is stored, replicated and queried as an opaque blob. ChaCha20 encryption. No key stored on server.

BLIND

THE DATABASE
EVEN ITS OWNER
CANNOT READ.

Not even us.

blind_payload is a zero-knowledge encrypted field. INDB stores it, replicates it, and returns it — but never reads it. The encryption key never touches the server. Even the database owner cannot see your data.

HOW IT WORKS

CLIENT
raw_data_anchor:
["felt", "almost", "impossible"]
blind_payload:
"He felt it would be
impossible for him
to speak now"
CLIENT ENCRYPTS
WITH OWN KEY
INDB SEES
raw_data_anchor:
["felt", "almost", "impossible"]
blind_payload:
███████████████
███████████████
███████████
OPAQUE BLOB
STORED AS-IS
INTELLIGENCE
Prism, Echo, Instinct
work only on →
["felt", "almost",
"impossible"]
blind_payload: ignored
FULL ANALYTICS
ZERO KNOWLEDGE
↕ Raft replication to 3 nodes — each node copies the encrypted blob without decrypting

WHAT INDB STORES

// You send:
{
  "raw_data_anchor": ["felt", "impossible"],
  "blind_payload": "SECRET CONTENT"
}

// INDB stores on disk:
{
  "raw_data_anchor": ["felt", "impossible"],
  "blind_payload": "7f3a9b2c4e1d8f..." // encrypted
}

WHAT INDB CANNOT DO

Read the blind_payload contents
Include it in search or pattern analysis
Log or expose its value in any interface
Decrypt it — the key is never on the server
Know when two blind payloads contain the same data
MEDICAL

Symptom patterns. Private notes.

The AI detects disease patterns from signal tokens. The actual patient notes, names, and diagnoses live in blind_payload — invisible to the hospital's own infrastructure.

INTELLIGENCE

Pattern detection. Source protection.

Behavioral patterns are detected across thousands of events. The original source data — names, locations, communications — is encrypted in blind_payload. The operator sees nothing.

PERSONAL

Your truth. Your key.

Store everything INDB cannot understand. Journaling, private communications, documents — the signal flows through INDB's cognition. The meaning stays with you.

SPATIAL

location field

SPACE IS
INDEX.

Every event in INDB is geometrically grounded. The location field is not a tag — it is a spatial anchor that enables the Cognitive Engine to reason about proximity, clustering, and relevance in physical space.

Format A — Virtual Namespace

Plain string path for logical namespacing — books, services, agents.

{
  "location": "books/Dostoevsky/The_Idiot"
}
Format B — OSM Geo Object ✦

Full OpenStreetMap anchor with validated osm_id — ties events to real-world objects.

{
  "location": {
    "lat":    55.7558,
    "lon":    37.6173,
    "osm_id": "node/1234567"
  }
}
Nodes · Ways · Relations

osm_id validates against all three OSM entity types. A café, a street, a district — all are valid spatial anchors.

Proximity Scoring

Echo's harmonic analysis weights meta-location at 50%. Events near the same OSM object resonate more strongly.

Fusion Penalty

Multiple osm: tokens in one event are penalised during fusion — preventing over-clustering on geographic coincidence.

03

INTELLIGENCE
MODULES

Prism

Contextual Synthesis

~20ms avg
100% success

Transforms raw events into meaning. Calculates significance scores, context labels, identity alignment.

Significance ScoreContext LabelInsightIdentity Alignment
Echo

Resonance Detection

< 30ms
100% success

Finds similar events through harmonic analysis. Weighted by token similarity, emotional proximity, and meta-location.

Token Similarity
20%
Emotion Proximity
30%
Meta (Location)
50%
Instinct

Adrenaline Reflexes

~25ms
100% success

Adaptive response based on urgency. Three modes from deep analysis to lightning-fast reflex matching.

Analytical 0.0Alert 0.5Instinctive 1.0
Fusion

Neural Fusion Engine

real-time
active

Semantic deduplication using DBSCAN. Adaptive frequency tracking with penalty system. Temporal awareness.

Semantic DedupPenalty SystemAuto-tuneTemporal
FUSION

Neural Fusion Engine

MEMORY
COMPRESSES.

Repetition is not data — it is pattern. The Fusion Engine merges semantically identical events into a single compressed signal, tracking frequency while preserving meaning. The database grows in intelligence, not in size.

Before Fusion — 3 events
id: a1b2 · tokens: ["home", "arrived"]
id: c3d4 · tokens: ["home", "arrived"]
id: e5f6 · tokens: ["home", "arrived"]
After Fusion — 1 event ✦
tokens: ["home", "arrived"]
fusion_count: 3
frequency: high
last_seen: 2026-03-04T23:00Z
COMPRESSION RATIO: 35:1 VERIFIED
Adaptive Frequency

Each merge increments fusion_count. High-frequency patterns become heavy signals — low-frequency anomalies stay distinct.

Penalty System

Rapid identical ingestion is penalised. Fusion is earned by genuine repetition — not flooding. Spam cannot artificially inflate signal weight.

Anomaly Preservation

Critical divergences stay unfused. A routine "home arrived" compresses. An unusual "alarm triggered at 3am" remains a distinct Axiom.

LENS

Contextual Lens

ADJUST YOUR
PERSPECTIVE.

Reality is not one query. The Contextual Lens blends two data contexts at a configurable ratio — recent vs historical, simple vs complex, local vs global — to produce a focused view of memory tuned to the moment.

RECENT-HEAVY · 90/10

What happened in the last 24h dominates. Historical context is a whisper. Use for real-time alerting and live monitoring.

BALANCED · 50/50 ✦

Equal weight. Now and then, present and past — combined into a single coherent truth. The default cognitive mode.

HISTORICAL · 10/90

Long-term pattern analysis. What has always been true? Use for trend detection, archival reasoning, and behavioural baselines.

Available Contexts

Last 24 hours. Fresh events, low fusion — the system has not had time to compress them yet.

alarmtriggered
×1
23:58 today
meetingstarted
×1
20:00 today
homearrived
×3
18:30 today
coffeeordered
×2
09:15 today
screenon
×4
08:02 today
low fusion
compressed pattern
high-frequency signal
04

VERIFIED
PERFORMANCE

<1ms
Ingestion Latency
Standalone mode
99.7%
Success Rate
997/1000 under load
4800%
Stability Improvement
Raft cluster vs baseline
0
Re-elections
Over 5+ min production run
6
Protocols
HTTP · UDP · gRPC · WS · GraphQL · MCP
0
External Databases
Zero Redis / Mongo / Postgres
AES-256
Encryption at Rest
GCM + Ed25519 signatures
35:1
Fusion Compression
Repetitive signal reduction
05

PROTOCOL
STACK

Unified · Port 8000
HTTP / REST
Standard API, queries, Swagger
GraphQL
Schema introspection, flexible queries
WebSocket
Real-time event broadcasting
MCP
Model Context Protocol — AI agents
Dedicated ports
:50051
gRPC
Binary streams, service-to-service
:9001
UDP
Fire-and-forget IoT ingestion
06

ZERO
COMPROMISE
SECURITY

AES-256-GCM at Rest

All data encrypted on disk. PBKDF2 key derivation with 100,000 iterations. Zero plaintext persisted.

Ed25519 Signatures

Every API response cryptographically signed. Tamper detection on every byte.

TLS/SSL in Transit

mTLS mutual authentication for gRPC. Automatic certificate generation and renewal.

RBAC + Audit Log

Role-based access control. Merkle tree-backed immutable audit trail.

AXIOM

TRUTH ENGINE

Inhale the chaos. Exhale the noise. Remember the Axiom.

Contact

INTERESTED
IN INDB?

Whether you're building something that needs a truth engine, exploring partnership, or just want to understand how the Axiom works — write to us.

Integration & API access
Partnership & investment
Custom deployment
Rust rewrite team