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Urban Intelligence Infrastructure

Real-world events need real-time consensus.

Jalibor provides the infrastructure layer for distributed validation and tracking of moving real-world events.

Real-timeConsensus formation
PMCPresence Mesh Consensus
Human+AIHybrid node types
Live simulation — Presence Mesh Consensus

Watch the mesh activate

A moving entity triggers the network. Nodes activate, consensus grows, trajectory is reconstructed — in real time across the distributed mesh.

Phase 01 * AMBIENT
Ambient city state
Distributed nodes in standby. Mesh dormant.
0CONSENSUS
Active nodes
0
Validated
0
Mesh radius
--
Latency
--
Protocol
PMC v2.4
Node types
HUMAN * AI
Status
STANDBY
Coverage
--
The Problem

No system tracks
what is moving.

Every city has cameras. Every citizen has a smartphone. Every business has sensors. But none are connected into a unified intelligence layer capable of tracking what is happening — as it happens.

CAMERAS
Siloed
No shared intelligence layer
RESPONSE
Delayed
Slow to reach a moving event
TRACKING
Blind
Lost the moment it moves
CONSENSUS
Zero
Until Jalibor
The Shift

From static reports
to living consensus.

A report is one voice, frozen in time — believed or ignored. Consensus is many independent observers, and a confidence that keeps evolving as the event itself moves.

Static Report

One source. One moment. No corroboration, no trajectory, no way to earn trust. It is taken on faith — or discarded.

Living Consensus

Many independent observers converge on the same event. Confidence rises with corroboration, decays without it, and the event is tracked as it moves.

How Jalibor Works

Distributed tracking of
moving real-world entities

Jalibor uses Presence Mesh Consensus to validate and track real-world events in real time. Unlike platforms that report static events, Jalibor continuously reconstructs the trajectory of moving entities — through a distributed mesh of human observers and AI systems.

01
Event detected

A node submits a georeferenced, timestamped report.

02
Mesh activated

Nearby nodes are instantly alerted and enter validation state.

03
Validation

Independent nodes confirm or add information. Confidence grows with each corroboration.

04
Dynamic reconstruction

The consensus engine reconstructs the entity's trajectory across the city in real time.

Jalibor ● LIVE
EVENT · ZONE 4 87%
Moving entity tracked
CONFIRM
ADD INFO
Validate from anywhere

One mesh, every channel

Anyone can join the mesh and validate events through the channels they already use. Report, confirm, and track in real time — straight from your messaging apps.

WhatsApp
Report and validate events directly in chat. The most accessible entry point to the mesh.
LIVE
Telegram
Bot integration for instant alerts and validation. Real-time mesh notifications.
LIVE
Progressive Web App
Full live map, push notifications, and trajectory tracking. Installable, no app store needed.
LIVE
Confidence Engine · POPV

Confidence Engine

Proof of Presence Validation (POPV)

Every event generates a distributed confidence score updated in real time as independent nodes corroborate. Single reports never reach high confidence — structural anti-fraud by design.

0–14%Unverified report0–1 node
15–29%Initial signal2–4 nodes
30–49%Possible — alert state5–9 nodes
50–64%Probable — mesh active10–19 nodes
65–79%Confirmed — broadcast active20–39 nodes
80–89%Verified — trajectory live40–74 nodes
90–100%Strongly validated75+ nodes
Trust Architecture

Distributed anti-fraud

False reports become structurally difficult. Not through moderation — through math. Consensus requires independent corroboration across spatial and temporal dimensions simultaneously.

Multi-node requirement

A single report never achieves high confidence. Independent nodes — with no knowledge of each other — must corroborate independently.

Spatial coherence check

Validations must be geographically consistent with the event's reported location. Nodes outside the plausible radius are penalized.

Temporal coherence check

Reports must be temporally consistent with a plausible entity trajectory. Retroactive or impossibly-timed reports reduce the submitter's trust score.

AI visual arbitration

AI camera nodes provide independent visual verification. Contradictions between human reports and AI observations reduce confidence and flag the discrepancy.

The Presence Cone

Confidence is earned in
space and time.

Every observation is weighed by how close in space and how recent in time it is to the event. Presence near the event, right now, carries full weight. Presence far away, or long ago, fades toward zero — never rejected, just weightless.

Inside the cone — near and recent. Full presence weight.
At the edge — distant or fading. Partial weight.
Outside — too far or too old. Near-zero weight.
Presence weight w = e−d²⁄2s² · e−λ·Δt geographic × temporal — the core of Presence Mesh Consensus
Event confidence0%
Observers in cone0
← distance in space → ← time since event →
Network Participants

The mesh is everyone

Jalibor's network effect compounds with every node that joins. More participants means stronger consensus, faster validation, and wider geographic coverage.

Primary validation nodes. Report events, validate sightings, and track moving entities through the city mesh. Their verified presence is the foundation of PMC.

Human Node Layer

Connected cameras — residential, commercial, municipal — operating as autonomous AI validation nodes. Visual entity detection, direction tracking, and confidence contribution without human intervention.

Autonomous Node Layer

Real-time urban event intelligence via API. Situational awareness dashboards, dynamic threat tracking, and inter-agency event sharing. From reactive to predictive.

Command & Control API

Police, private security, and rapid response teams integrate PMC data for real-time situational awareness. Moving entity tracking. Trajectory prediction. Coordinated response.

Response Integration

Logistics, insurance, retail and real estate operators integrate urban intelligence to reduce operational risk, protect assets, and optimize coverage in high-density urban areas.

Enterprise Intelligence API

Connected vehicles, smart city sensors, public infrastructure nodes joining the mesh. As the IoT ecosystem expands, Jalibor's coverage and resolution increase automatically.

Next-Gen Node Layer
Network Effects

Every node strengthens every other node

The value of the network compounds with participation. Every node that joins strengthens consensus, widens coverage, and sharpens tracking for every other node — a defensible advantage that grows with density, not spend.

Stronger consensus
More independent observers raise confidence and resist manipulation.
Wider coverage
Each new node extends the area the mesh can observe and validate.
Faster validation
Denser presence means events are corroborated closer to real time.
Compounding moat
Density is hard to replicate — the advantage strengthens over time.
REAL-TIME URBAN INTELLIGENCE · PRESENCE MESH CONSENSUS · DISTRIBUTED VALIDATION

We build consensus together

Jalibor is real-time urban intelligence infrastructure — built for cities, municipalities, and enterprise security teams.

jalibor.app · technical@jalibor.app · Urban Intelligence Infrastructure · 2026