Advanced Data Analytics Solutions

With over 15 years of experience supporting the Department of Defense, medical research, and logistics sectors, ISSAC has developed a robust suite of modular data science tools built for flexibility and scale. Our flagship technology, Illuminative Analytics®, originally developed for the Missile Defense Agency, now empowers organizations across industries to unlock deep insights from complex, high-volume datasets. At the core of our approach is the Modular Analytics Framework (MAF)—a low-code, microservices-based platform designed to integrate seamlessly into cloud, on-premises, or hybrid environments. MAF accelerates deployment, enhances interoperability, and equips decision-makers with powerful, automated analytics tailored to their unique operational workflows.

  • Modular Analytics Framework (MAF)
The MAF consists of three different services that can be hosted in different environments to suit the needs of the stakeholders. These services include:

Module Library

Stores both the executable modules and their metadata.
Metadata includes:
  • What the module does
  • What data it accepts and produces
  • How it can be parameterized
Used by:
  • Analysts to build workflows
  • The Workflow Runner to connect modules to data infrastructure and execute them
  • Allows analysts to select, connect, and configure modules to design analysis workflows
  • Validates workflows based on module metadata from the Module Library
  • Provides real-time monitoring when a workflow is running
Deploys workflows into operational environments:
  • Local machines
  • Cloud environments
  • Hybrid setups
Connects data infrastructure with modules to execute workflows
  • Illuminative Analytics®

At the core of ISSAC’s Modular Analytics Framework (MAF) lies Illuminative Analytics®—a powerful, tailorable analytics engine that fuses prescriptive insights with AI to support data-driven decision-making across the full Big Data life cycle. From ingestion to recommendation, this service-based platform enables automation, discovery, and analyst augmentation to shift decision windows earlier and bridge the business-translator gap. Designed to accelerate enterprise intelligence, Illuminative Analytics® delivers advanced capabilities through six key differentiators:

  • Six Major Differentiators
Broad Spectrum Analytics –

Competes multiple analytic methods in parallel and fuses results for deep, meaningful insights.

Unknown Discovery –

Uses an unbiased approach to surface unknowns and uncovers hidden relationships in data, prompting user-directed discovery.

Hypothesis Generation and Exploration –

Employs custom techniques and ISSAC’s Hyper Agent Simulation Programs (HASP) to identify high-impact hypotheses automatically.

Iterative Analytics –

Orchestrates historical and real-time analytics to continuously refine insights and provide timely, accurate notifications.

Space/Time Representation –

Uses patent-pending tech to cut 4D storage size by over 55%, eliminate discontinuities, and support simple yet powerful comparative analytics.

Storage and Curation –

Supports multiple data views (graph, NoSQL, SQL) on the same dataset to boost retrieval speed and data exploitation.

  • Core Service Capabilities
Integrated Data Fusion –

Ingests, correlates, and fuses data while incorporating input from subject matter experts and stakeholders.

Advanced Modeling & Simulation –

Applies optimization, uncertainty quantification, concept exploration, forensic analysis, and model evolution to support comprehensive analytics.

Knowledge Management & Discovery –

Provides structural, search, and analytical tools for managing knowledge, including clustering, gap analysis, ad hoc data handling, and SPIDR™ evolutionary learning algorithms.

End-to-End Systems Engineering –

Supports the complete systems engineering lifecycle, fully integrated into the analytics process.

  • Analytics & Technical Features
Semantic Ingestion
Descriptive / Predictive / Prescriptive Analytics
Concept Repository & Resolution
Machine Learning & Mixed-Initiative Learning
Knowledge Discovery & Confidence Assessment
Basic Visualization
COA (Course of Action) Recommendations
Optimization & Management
Inductive / Deductive / Abductive Reasoning
Standard External APIs
SOA-Based Architecture
Hypothesis Generation
  • Atomic Graph Database (AGDB)

In working with complex, irregular data sets ISSAC has pushed the boundaries of what modern databases can handle, and, in some cases, we have completely broken through those boundaries. To handle large data sets that consist of highly interconnected and high dimensionality data ISSAC has developed a new type of database called an Atomic Graph Database (AGDB). In the AGDB, we are not constrained by schemas or rigid data structures, and we don’t have to dig into complex data objects to find the data we are looking for.

AGDB structures data as atomic units and connects them through relationships to form rich, contextual data graphs.

Data Atoms –

Individual, meaningful label-value pairs that represent the smallest unit of information.

Relational Structure –

Data Atoms are connected to each other to form complex, contextual data objects.

Single Instance Design –

Each Data Atom exists only once within the AGDB, ensuring consistency and shared context.

Shared Fields as Links –

Data objects referencing the same field automatically share the same Data Atom.

Inherent Relationships –

Shared Data Atoms create implicit relationships between different data objects.

AGDB is built to handle semi-structured or inconsistent data without forcing uniformity or risking data loss.

Semi-Structured Data Ready –

Designed to work with data from multiple, unnormalized, and inconsistent sources.

Raw Data Preservation –

Stores data in its original form without requiring cleanup or normalization.

Context-at-Query –

Users apply relevant context when querying, based on their specific needs.

Shared Fields as Links –

Data objects referencing the same field automatically share the same Data Atom.

Flexible Field Handling –

Accommodates data with variable field names, types, and reporting standards.

Data Flexibility

AGDB enables dynamic interaction with data by removing rigid structural limitations and allowing context-driven exploration.

Schema-Free Architecture –

No fixed schemas or hidden constraints limit how data can be used or queried.

Relationship-Driven Queries –

Users can follow relationships to explore, build, or restructure data as needed.

Template-Based Construction –

New objects can be created dynamically using query-based templates.

Custom Structures –

Users can define new data objects by combining context from across the data graph.

AGDB leverages built-in graph theory capabilities to surface data integrity issues, improve structure, and enhance analytic confidence.

Clumpy Data Detection –

Identifies irregular clusters or gaps in the graph that may indicate missing, incomplete, or overlooked data.

Error Detection –

Detects anomalies by analyzing structural deviations from expected graph patterns and relationships.

Duplicate Detection –

Reveals duplicate data as parallel relationships within the graph, enabling simple identification and selective handling.

Customer Testimonials

“Illuminative Analytics® is a unique reasoning engine that can make discoveries using deductive, inductive and abductive reasoning which allows for a full spectrum of unknowns to be uncovered!”
Rich LaValley
Analyst at OGSystems, Inc.

Trusted By Industry Leaders

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  • Rapid deployment with measurable ROI
  • Ongoing support and knowledge transfer
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