Omniscience

[ DATA & INTELLIGENCE PLATFORM ]

Designing a scalable data intelligence platform to visualize complex algorithms and bring clarity to large volumes of data.

Assembly Requirements

MODULE INVENTORY (5 COMPONENTS)

Omniscience focused on creating a platform capable of handling large volumes of data while making complex algorithms understandable and usable through clear visualization.

The project aimed to provide confidence in data-driven decision-making by transforming chaotic data into structured insights.

Primary focus

Visualization of big data algorithms

Centralized data intelligence

Clear and usable interfaces

Scalable cloud-based architecture

What we delivered

Data intelligence platform architecture

UX design for complex data visualization

Full front-end and back-end implementation

Cloud-ready scalable foundation

STEP 02 — CHALLENGES IDENTIFIED

Assembly Requirements

MODULE INVENTORY (5 COMPONENTS)

REQUIRED CAPABILITIES

Engagement. Making complex data and algorithms understandable for users without technical backgrounds.

Structure. Designing a system capable of aggregating and organizing large volumes of data.

Scalability. Ensuring the platform could scale across cloud infrastructure and increasing data loads.

STEP 03 — ASSEMBLY PROCESS

Solution Architecture

MODULE INVENTORY (4 COMPONENTS)

MODULE 3.1

● DATA FOUNDATION

A structured data layer was created to collect, normalize, and organize data from multiple sources.

Diagram showing data aggregation and processing flow from input sources like documents, Slack, databases, and files into a unified data layer with features: structured, searchable, accessible, multi-source, normalized, and optimized.

MODULE 3.2

● ANALYTICS SYSTEM

User-facing interfaces were designed to visualize data and algorithms in a clear and actionable way.

Analytics dashboard showing overall rating 23%, SPF 38% with error, DKIM 82% good, DMARC 78% good, BIMI 19% error, and Domain Age 36% error with options to Get Omni or Save Progress.

MODULE 3.3

● CORE PLATFORM

The platform architecture was built using cloud infrastructure to support scalability, performance, and extensibility.

Cloud infrastructure diagram showing Azure as primary cloud and AWS as secondary cloud. Architecture layers include Application Layer with Angular and Ionic, Service Layer with Java and Elasticsearch, and Infrastructure Layer with Terraform and Data Storage. Features listed are Scalable, Performant, and Extensible. Infrastructure as Code section highlights reproducible, version-controlled, and automated provisioning via Terraform.

STEP 04 — ASSEMBLY COMPLETE

Delivered Impact

MODULE INVENTORY (5 COMPONENTS)

PERFORMANCE OUTCOMES

Enabled visualization of complex data algorithms

Improved clarity and usability of large data sets

Delivered a scalable, cloud-ready intelligence platform

ASSEMBLY PHILOSOPHY

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Omniscience

Designing a scalable data intelligence platform to visualize complex algorithms and bring clarity to large volumes of data.

Assembly Requirements

Assembly Requirements

Solution Architecture

Delivered Impact