Wireless Locator Platform

Delivery of wireless caller location technologies and solutions for safety and security markets, ensuring compliance with FCC Phase II E-911 mandates and achieving exceptional accuracy and uptime for AT&T and T-Mobile networks. The network-based location technology known as Uplink Time Difference of Arrival (U-TDOA) is deployed nationwide by cellular network providers. Realized significant revenue from deployments by meeting SLAs and enhanced product competitiveness through platform re-factoring and IP/SCTP network optimization. Additionally, spearheaded market trials to surpass incumbent A-GPS technology in Verizon CDMA networks.

AI Contact Center

The key modules of platform include call center software, agent dispatch, provisioning portal, multi-party conferencing, reporting, dashboards, and call recording. The Products include ACD Call Control, ACD Media Services, ACD multi-tenant portal, and ACD analytics. The agent call recordings were analyzed to predict the sentiment of the call, outcome of the call, and agent performance.

Network Asset Management

Configuration Mgmt. DB

Communication Service Providers can easily manage the network resources required to deliver services faster and address issues proactively. Telecommunications Network Inventory product gives network engineers the power to accurately design, build, and manage network resources and services. Through automated workflows and an accurate view of your inventory lifecycle, it’s possible to optimize resource utilization and network investment.

Carrier Grade NAT

Carrier-Grade NAT for uninterrupted IPV4 addresses availability via smart resource allocation to subscribers. The project deliverables include Edge Data Collection Units, Central data collection using Message collectors, CGNAT Data Analytics, Monitoring and Threshold Alerts, and Subscriber CGNAT Fail-over.

Enterprise Communications

Provide enterprises with reduced operations costs for calls, presence, and messaging to Land Lines (PSTN) and Mobile Networks over Voice Over IP (VoIP) using Session Initiation Protocol. Replaced legacy trunks with Internet Trunks to reduce cost

Mobile Feature Activation

Mobile Telephone Activation System (MTAS) is the Provisioning system for the Verizon Wireless. It provisions subscriber for activation requests, deletes, feature changes, equipment changes, suspends, restores, subscriber re-homes and subscriber migrations. 

Trunking Gateway

Provide enterprise PBX with outbound calling to (PSTN, Mobile, VoIP) networks over Internet by replacing legacy trunks (PRI channels) with SIP Trunks (VoIP channels) to reduce cost. The product delivers a SIP Trunking Gateway with a call routing engine, routing tables, provisioning management, and SIP cluster management.

Field Service Management

Deliver Field service management product to dispatch technicians to perform cable service delivery to subscribers. The dispatch software streamlines orders, scheduling, job costing, payments, GPS tracking, inventory, and analytics. The product provides AI driven service automation, service process management, and chatbots to provide technician collaboration.

Geo spatial Analytics, Geo fencing

Reconciliation as a Service

Machine Learning system to predict adjustments to trading discords between front office and back-office systems. The model guides reconcilers to prioritize complex reconciliation dispositions while system is actively learning from their expertise.

Finance Data Platform

Multi-tenant finance data platform to host business applications (Taxes, Group Finance, Product Control) providing self-serve data onboarding, secure data access, and automated management of data sources.

Collateral Management

Automate cheapest-to-deliver collateral to counterparties by optimizing (liquidity, funding costs, asset costs) and dynamic substitution of high liquidity with collateral assets. The system achieved accuracy by classifying assets, forecasting funding, and prediction of counterparty substitution risk.

Anti-money Laundering

Next generation AML for custodian bank that provides a machine learning solution to identify fraud transactions and a private blockchain (permissioned, KYC) to ensure transactions can be traced back to known identities.

Liquidity Forecasting

Forecast future loans and deposits to optimize use of capital and meet regulatory requirements. The neural network model improves accuracy over expert models and ensemble of ARIMA, ETS (Error, Trend, Seasonal)

Compliance Audit Analysis

Automate data quality rules and governance of key data elements used in regulatory reports (FR Y-9C, FR Y-15, CCAR). The learning system predicts anomalies in data sources and potential audit findings impacted by data. The system also helps trace impacted audit data source.

Threat Analysis

Automate analysis of security alerts and escalation emails to classify threat classes to apply manual intervention or standard resolution procedures. Threats are scored in the model.

Automated Contract Review

Automate review of thousands of contracts in multiple languages, multiple currencies, and multiple products for IBOR impact. The goal is to classify contracts impacted by LIBOR, extract LIBOR text and encode fall-back legal text

On Demand VaR

Distributed and massively parallel processing framework to compute the exposure of portfolio valuations over different scenarios and over time. The solution was to outperform existing grid computing solutions in terms of performance, scale, and total cost of ownership

Financial Blockchain

High Quality Liquid Assets

Debt Capital Markets

Straight through processing of HQLA (High quality Liquid assets) trades by exchanging ownership and managing transactions.

Distributed Ledger for Syndicated Loans for DCM (Debt Capital Markets). The ledger provides easier tracking of multiple lenders providing loans to borrowers.

Price Consensus

The platform design delivers next-generation Independent Price Verification (IPV) services by opening up traditional black-box’s with fully transparent and seamless OTC price consensus services.

EHR Optimization

FHIR Cloud Data Warehouse

The Cloud-Based FHIR Data Warehouse and Application Platform is a comprehensive solution designed to facilitate seamless integration, storage, and utilization of healthcare data in compliance with FHIR standards. Key features include data extraction and transformation from EHRs, FHIR data warehousing, FHIR API services, SMART on FHIR applications, backend applications for healthcare operations, integration and interoperability, and security and compliance measures. The project utilizes Azure cloud,, ETL tools (NiFi, Talend), databases (PostgreSQL), FHIR server implementations (HAPI FHIR), frontend and backend frameworks (React.js), business intelligence tools (Power BI), workflow automation platforms (Camunda), integration protocols (HL7 v2, OAuth 2.0), and security solutions (AES encryption, IAM).

Patient Master Index

The AI-Driven Patient Master Index project revolutionizes patient identification and record linkage in healthcare by leveraging generative AI and similarity matching with vector databases. Through patient embeddings and advanced algorithms, it efficiently identifies and links patient records across diverse healthcare systems, prioritizing accuracy and facilitating streamlined care coordination. This innovative solution aims to enhance patient outcomes, care quality, and operational efficiency in healthcare.

Population Health Management

The Population Health Management Platform project aims to implement a comprehensive solution to monitor, analyze, and improve the health outcomes of a defined patient population. Key components include platform development with data aggregation and analytics capabilities, care coordination tools, patient engagement features, and performance measurement capabilities. Data sources such as electronic health records (EHRs), claims data, social determinants of health, and patient-generated data are integrated to provide a holistic view of patient health. Artificial intelligence (AI) enhances the project by analyzing vast amounts of data to identify trends, predict health outcomes, stratify risk levels, and prioritize interventions. The AI-driven insights enable proactive population health management, enhance care coordination, and drive improvements in health outcomes and cost savings.

Medicare Charge Automation

The Automated Medicare Claim Processing System revolutionizes the claims submission process for healthcare providers participating in Medicare. By harnessing the power of cutting-edge technologies like natural language processing (NLP), generative AI, and robotic process automation (RPA), the system predicts charge codes and payments with precision, creating claims based on predictive models derived from extensive historical data. It seamlessly integrates Medicare rate card adjustments to ensure compliance and accuracy, while also providing a user-friendly dashboard for reviewers to make manual adjustments when necessary. Through automation of claim submission via RPA, the system minimizes errors, accelerates reimbursement cycles, and optimizes revenue cycle management for Medicare providers.

AI Revenue Cycle Management

The AI-driven Revenue Cycle Management project aims to optimize financial performance and operational efficiency in healthcare organizations by leveraging artificial intelligence technologies. Key objectives include improving claims adjudication accuracy, enhancing decision-making for claims submission, reducing claims abandonment rates, improving medical billing accuracy, and streamlining verification of benefits processes. By automating and optimizing revenue cycle workflows with AI-driven insights and decision support, healthcare providers can minimize errors, accelerate reimbursement processes, and ensure compliance with payer requirements, ultimately enhancing patient care delivery and financial outcomes.

Risk Stratification Engine

Risk stratification engines identify the costliest patients and those most likely to be positively impacted by active care management. Using patient data from medical claims, electronic health records, and demographics such as age, marital status, gender, and more, patient risk stratification engines can effectively predict a patient’s change in health over time with unparalleled precision. The product provides AI based predictions on how risk stratification can be mitigated by active care management. The product also measures ACG measures predictive cost risk, concurrent risk, likelihood of hospital admission, risk of poor care coordination. The product also uses avoidable and unavoidable diagnoses, Carlson co-morbidity index, and hierarchical condition coding.

Utilization Management

The Utilization Management project aims to optimize resource allocation and improve the quality of care by integrating utilization review, risk management, and quality assurance. By leveraging AI and machine learning, the project facilitates the identification and prioritization of high-risk cases, allowing nurses to allocate their time more efficiently. Key features include automated case identification and stratification, prioritized presentation of patient conditions, data-driven insights based on evidence-based medicine, and real-time alerts of care status. This approach ensures that nurses can focus their efforts on cases that require the most support, ultimately enhancing patient care outcomes.

KOL Engagement Platform

The Data-Driven KOL Engagement Platform utilizes advanced data analytics and artificial intelligence to facilitate meaningful interactions between Key Opinion Leaders (KOLs) and stakeholders in healthcare. It features data analysis and profiling tools to identify KOLs based on expertise and influence, predictive models for proactive engagement, personalized engagement strategies driven by AI, content creation and distribution capabilities, and comprehensive performance analytics for insights-driven decision-making. This platform empowers healthcare organizations to foster collaboration, drive innovation, and enhance patient care through effective KOL engagement.

Patient Recruitment

HCP Segmentation

Patient and HCP Based Forecasting

The project aimed to revolutionize pharmaceutical sales forecasting by integrating physician prescription patterns with patient-based and historical sales data. Meticulous data collection from various sources included historical sales records, patient-level data, and physician prescription patterns, with rigorous preprocessing ensuring data integrity and aggregation to match time series. The implementation of Long Short-Term Memory (LSTM) networks automated feature engineering, leveraging deep learning to capture temporal dependencies and patterns in sequential data. Advanced multivariate time series analysis, including LSTM, provided crucial insights into market dynamics. Forecasting models were developed by amalgamating patient-based features and physician prescription patterns, with model integration and validation involving meticulous evaluation of performance metrics.

Predictive Underwriting

Predict underwriting classes to improve risk selection, expedite underwriting process, minimize need for medical tests, and automate customer onboarding.

Long Term Care Lead Generation

Micro-target LTCI prospects using high dimensional clustering. Predict leads to upsell LTCI to existing Life Insurance Customers. Predict leads with an immediate need for Long Term Care Services. Recommend sales channel & influencers to sell LTCI to prospective customers.