SCIBOR ANALYTICS COMPUTE & VISUALIZATION PLATFORM

The database that runs
inside your app.

Most analytics ships your events to a cloud endpoint and waits. SciBor puts a columnar database and a stream processor inside the host application instead. Aggregations run on the client, the third-party script tags are gone, and live data renders without a round-trip. That's the entire difference.

// 01 ARCHITECTURAL DIVERGENCE

Why SciBor works differently

Most analytics SDKs treat the client as a dumb sensor feeding a cloud silo. SciBor treats it as a database — and as a compute node. That's an architectural difference, not a marketing one.

01 // LATENCY BOUNDARY

Answers on the device, not in a datacenter

Reactions to telemetry and user behavior happen on the device, in microseconds. No HTTP round-trip, no ingress stall — and it all still works with the network unplugged.

02 // PRIVACY

Raw events don't leave the device

Raw events never leave the device. Only aggregated deltas sync out, which takes most of the weight off GDPR, HIPAA and international data compliance.

03 // IN-PROCESS RUNTIME

Ad-blockers can't see it

It's a compiled library linked into your application code. No third-party network script to block, no filtering to lose data to, and full telemetry fidelity.

04 // NO VENDOR TAX

No Per-Event Billing

Self-hosted, with your schema and your retention policy. You run it on your hardware, and nobody sends you a bill that grows with your event count.

05 // THE FEEDBACK LOOP

The Live Telemetry Feedback Loop

Because the database lives in-process, the app can query its own analytics while it runs. Roll a mean, cluster a behavioral embedding, watch an anomaly threshold — then act on it without leaving the device.

// 02 ENGINE SPECIFICATIONS

How the engine works

MODULE 01 // LOCAL INGESTION

Lock-Free RAM & SIMD Columnar Ingestion

Events land in pre-allocated circular RAM buffers with atomic sequence counters. A background thread batches them with SIMD instructions (AVX-512 / ARM NEON) and flushes straight to compressed columnar Apache Arrow on local disk.

MODULE 02 // STREAM PROCESSING

Incremental View Maintenance (DBSP)

Rolling averages don't need a table re-scan. SciBor evaluates query derivatives with Database Stream Processing (DBSP), so materialized views such as rolling means, variance spikes and anomaly thresholds update incrementally in sub-microseconds as events arrive.

MODULE 03 // MULTIMODAL STORAGE

One store, four index types

One embedded engine, four ways to look at the data: vector search (HNSW) for similarity, graph relations for component topologies and user journeys, full-text search (BM25), and Arrow columnar analytics. They share a single storage layer, so a query can join a similarity result to a topology walk without copying either one into a second system.

MODULE 04 // CLUSTER SCALING

QUIC Commit Log & State Simulation

Aggregated batches sync over QUIC as append-only commit logs. On the server the same engine scales out with DataFusion batch SQL, and an Entity-Component-System (ECS) model replays historical telemetry to simulate predictive states.

// 03 VISUALIZATION ENGINE

120 FPS Windowed Visualization & WASM Plugins

A rendering canvas plus a modular WebAssembly extension framework. You can build multi-view consoles that never contend with the UI thread.

120 FPS

The stream doesn't stutter

WebGL and WebGPU update charts, histograms and spatial views at up to 120 FPS. Because it writes to vertex buffers straight from Arrow columnar memory, there are no JavaScript object allocations to trigger a GC pause.

MULTI-WINDOW

Every viewport renders on its own

Several diagnostic views render at once — a 3D device pose, rolling metrics, a Fourier transform — each docked and updating live.

WASM PLUGINS

You can ship your own widgets

Write your own widgets and domain visualizers in Rust, C++ or Go. They compile to WebAssembly and load into the dashboard sandbox at runtime.

// 04 OPERATIONAL DOMAINS

Where it gets used

PRIMARY SECTOR 01 OFFLINE-FIRST

Healthcare & Medical Diagnostics

Bedside monitors, surgical telemetry and wearables aggregate patient vitals on the device itself. Nothing raw crosses a foreign network, which keeps HIPAA and GDPR posture clean, and the device tunes its own alarm thresholds from its own data.

INVARIANT: ZERO RAW PHI LEAVE DEVICE // LOCAL IVM ALARMS
PRIMARY SECTOR 02 AUTONOMOUS EDGE

Robotics & Autonomous Edge Systems

Rovers, surgical arms and warehouse fleets compute torque variance and joint temperature regressions locally. When the wireless uplink drops, the edge node simulates against its own history and intervenes anyway.

INVARIANT: ZERO CLOUD DEPENDENCE // SUB-MS TORQUE REACTION
SECONDARY EDITION // LIGHTWEIGHT AGENT HIGH THROUGHPUT

Tech & Telecom Products (SciBor Light)

A lightweight dependency-free library for web, desktop and mobile apps. It drops in where a tracking pixel used to sit, batches offline, never gets caught by an ad-blocker, and carries no per-event infrastructure tax.

METRIC: < 50KB CLIENT FOOTPRINT // 100% RETENTION OWNERSHIP
// 05 PIPELINE TOPOLOGY

How a reading reaches the dashboard

STAGE 01: RAM RING
Lock-Free Buffer
Zero-cost atomic writes
──▶
STAGE 02: ARROW
SIMD Columnar
Compressed batch flush
──▶
STAGE 03: DBSP IVM
Z-Set Materialization
Microsecond Live Queries
──▶
STAGE 04: QUIC LOG
Append-Only Sync
Aggregated deltas only
──▶
STAGE 05: ECS SIM
DataFusion Cluster
Time-travel state simulation