Our Solutions
We deliver cutting-edge solutions across multiple domains to help your project thrive.
Advanced Analytics (AI/ML)
In today’s federal landscape, advanced analytics and artificial intelligence are more than emerging technologies—they are critical enablers of mission insight and operational advantage. When applied responsibly, analytics and AI/ML help agencies anticipate trends, optimize performance, manage risk, and make faster, more informed decisions. iCUBE delivers end-to-end Advanced Analytics and AI/ML solutions aligned with federal mandates, ethical AI guidance, and agency priorities. Our approach transforms data into predictive, explainable, and actionable intelligence—supporting mission outcomes, accountability, and public trust.
Data Management
In today's federal landscape, data is more than an asset—it is a strategic enabler of mission success. Effective data management empowers agencies to make evidence-based decisions, increase operational efficiency, reduce costs, and deliver greater transparency to the public.
iCUBE provides end-to-end Data Management solutions that align with federal mandates and agency priorities. Our approach ensures data is accurate, accessible, and actionable—supporting both internal operations and external accountability.
Advanced Analytics Capabilities
Five pillars of advanced analytics excellence, designed specifically for federal agencies
Descriptive & Diagnostic Analytics
Analyzing historical and operational data to understand what happened, why it happened, and where performance gaps and opportunities exist.
Predictive Modeling & Forecasting
Applying statistical models and machine learning techniques to anticipate trends, forecast demand, and support proactive decision-making.
Prescriptive Analytics & Optimization
Using optimization, simulation, and scenario analysis to evaluate trade-offs and recommend actions that improve outcomes and resource allocation.
AI/ML Model Development & Deployment
Designing, training, validating, and operationalizing machine learning models in secure environments, aligned with federal AI governance and risk management requirements.
Responsible AI & Model Governance
Establishing explainability, bias detection, performance monitoring, and lifecycle management practices to ensure AI systems are transparent, compliant, and trustworthy.
Process That Delivers
Assessment
Evaluate data availability, quality, and analytical maturity to identify priority use cases, risks, and readiness for advanced analytics and AI/ML.
Planning
Define analytic objectives, success metrics, and a phased roadmap that aligns models, data, and governance with mission needs and federal requirements.
Implementation
Develop, validate, and deploy analytic solutions and AI/ML models within secure environments, integrating them into operational workflows.
Optimization
Continuously monitors model performance, data drift, and outcomes, refining approaches to improve accuracy, reliability, and mission impact over time.
Advanced Analytics (AI/ML) Success Story

 Transforming Patient Safety with AI/ML‍

At iCUBE, in collaboration with the GSA Centers of Excellence (CoE) Technology Transformation Services (TTS) and the Agency for Healthcare Research and Quality (AHRQ), we developed a prototype for modernizing the Quality and Safety Review System (QSRS) — a national platform used to identify and track Hospital Acquired Conditions (HACs) such as infections, falls, and surgical complications.

Traditionally, human abstractors spent over an hour reviewing each patient case file, sometimes thousands of pages long, to answer complex medical questions. This manual process was slow, costly, and prone to human error.

Through the integration of Artificial Intelligence (AI), Machine Learning (ML), and Optical Character Recognition (OCR), we transformed this process:


Speed & Scale: Automated abstraction reduced review time by over 6000%, enabling 20 patient records to be processed in just 9 minutes.

Accuracy: AI/ML models improved HAC detection accuracy to 90–95%, surpassing manual review rates.

Cost Efficiency: Data abstraction costs dropped to as little as $5–$8 per case, saving millions annually.

Patient Safety Impact: Faster, more reliable data enabled hospitals and CMS to identify risks earlier, implement preventive measures, and reduce penalties tied to HAC rates.

Future Ready: The solution is designed to integrate with Electronic Health Record (EHR) systems, empowering healthcare providers to monitor patient safety in real time. Hands-on mentorship and design coaching to build internal capability and support lasting transformation.

This joint effort between iCUBE, AHRQ, and GSA CoE/TTS demonstrates how advanced analytics can revolutionize healthcare, turning labor intensive processes into scalable, intelligent systems that improve outcomes for hospitals, regulators, and patients alike.

Client Testimonial

"iCUBE exceeded task order requirements for quality, demonstrating extraordinary attention to detail in developing and testing AI/ML algorithms. Their proactive management, innovative workarounds, and customer‑oriented approach ensured deliverables were consistently ahead of schedule, under budget, and of exceptional quality. By leveraging subject matter expertise and advanced analytics, iCUBE strengthened data accuracy, improved patient safety outcomes, and provided lasting value to the Government. Based on their performance, I would recommend them for similar requirements in the future.”

Ready to Transform Your Federal IT?
Let's discuss how our expertise can drive your agency's technology initiatives forward
Phone
(571) 225-1631
Email
info@thinkicube.com
info@thinkicube.com
Address
2982 Rittenhouse Circle, Fairfax, Virginia 22031
Contact iCUBE
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Phone
(571) 225-1631
Email
info@thinkicube.com
Location
2982 Rittenhouse Circle, Fairfax, Virginia 22031