Awarding Agency: Department of the Treasury/Internal Revenue Service (IRS)

Contract Number: 2032H5-24-P-00097

Relevant Phase I/II Award Numbers: AN 1938135, AN 2028451, Award #2327618 | Agency: National Science Foundation

Description: Forward Edge-AI is working with the agency's Information Technology executives, staff, and other stakeholders to provide data analytics support to the TBM program. The work includes analysis of TBM data to identify trends, patterns, and anomalies that can provide insights into the organization’s IT spending.

Forward Edge-AI is also leveraging data forensic techniques derived from a National Science Foundation SBIR to determine data sources and ownership for Servers, Data Centers, Storage, Applications and Services and other information to determine lineage and provenance of data sources.

Awarding Agency: Department of the Navy

Contract Number: N0001923C0035

Relevant Phase I/II Award Numbers: AN 1938135, AN 2028451 | Agency: National Science Foundation

Description: While partnering with PMA-271 to develop a data analytics, modeling, and simulation capability, we developed advanced cost estimating methodologies heavily focused on flight machine maintenance and readiness. PMA-271 recognized the critical need for precision in cost estimates early on as aircraft maintenance intricacies and the demand for high readiness levels make it necessary for a comprehensive understanding in budgetary and financial implications.

We are currently establishing a robust data analytics framework geared towards maintenance budgets and fleet readiness cost estimations to support the enablement of resource allocation optimization, maintenance process streamlining, and overall readiness enhancement. The synergy between precise cost estimation and advanced analytics equips PMA-272 with the tools to allocate resources judiciously. By identifying cost drivers and understanding the impact of various scenarios through simulations, the organization can make informed decisions that optimize budget utilization, enhance maintenance efficiency, and elevate the overall readiness of the flight machines.

Awarding Agency: US Air Force

Contract Number: FA701423C0029

Relevant Phase I/II Award Numbers: AN 1938135, AN 2028451 | Agency: National Science Foundation

Description: The objective of this SBIR Phase III Award with the Office of the Deputy Under Secretary of the Air Force, Management and Deputy Chief Management Office (SAF/MG) is to put in place more effective and efficient business/mission practices. Forward Edge-AI is delivering to SAF/MG the capabilities to guide enterprise funding and transformation decisions, to include centralized planning capability, defined enterprise architecture, and real-time data through systems and process integration.

Currently, we spearhead efforts within the Security of the Air Force, Management organization's to illustrate how modern data collection, coupled with advanced investment review and decision making capabilities, can amplify operational effectiveness. Realizing and leveraging AI's extensive data analysis and pattern recognition capabilities, enables us to be better equipped when conducting issue identification, problem resolution, and strategic planning. AI algorithms have the ability to analyze historical data, current threats, and other dynamic variables to optimize mission planning and identify threats. This capability not only enhances the efficacy of operations but also optimizes resource utilization, aligning seamlessly with budgetary constraints. With this, we are working with SAF/MG to ensure the organization not only contributes to cost effective operations but aligns initiative with overarching budgetary goals.

Awarding Agency: Department of Homeland Security/FEMA

Contract Number: 70FA3122A00000002

Relevant Phase I/II Award Numbers: AN 1938135, AN 2028451 | Agency: National Science Foundation

Description:

As part of establishing FEMA’s Robotic Process Automation (RPA) Center of Excellence (CoE), we implemented and leveraged cost benefit analysis (CBA) and cost estimate tools to streamline and optimize operations, enhance efficiency, and reduce costs. The CBA tool allows FEMA to assess potential benefits and costs of a proposed automation project, giving detailed budgetary insight when considering enterprise RPA demand management and prioritization.

We use AI to evaluate both tangible factors including cost savings, increased efficiency, error reduction, increased employee satisfaction, and enhanced customer experience. FEMA can easily define key performance indicators (KPIs) and success criteria to measure the impact of automation initiatives on various aspects of the business. Cost estimate tools allow the enterprise to estimate the costs associated with the development, deployment, and maintenance of automations. Development hours, infrastructure costs, licensing fees, and ongoing maintenance expenses are cost inputs used to determine a more accurate and transparent budget and total cost of ownership for each automation opportunity/developments. The project successfully achieved Authorization to Operate (ATO).

Awarding Agency: Department of Commerce/Census Bureau

Contract Number: 1333LC21P00000119

Relevant Phase I/II Award Numbers: AN 1938135, AN 2028451 | Agency: National Science Foundation

Description: Leveraging RPA, Generative AI (GAI), and ChatGPT to automate repetitive processes related to claims filings, EMMA uses Automation Anywhere Bots to perform tasks, parse, trigger error-free responses, and reduce the time to complete a claims form from 72 hours to minutes. Also, employing Microsoft Teams, Power Automate, and Virtual Assistants reduces the burden on the “Are You Hurt?” hotline enables faster support and response for Census employees while reducing the burden of various department staff. This Phase III Project won Federal Computer Week's (FCW)/Fed100 Award for Business Transformation. Click to view Contractor Performance Assessment Rating (CPAR). See videos Bureau of Census EMMA Project, Applicant Registration Bot, Registration Bot 2

Award Numbers: AN 1938135, AN 2028451 | Agency: National Science Foundation

Description: In Phase I, we digitally transformed Federal Communication Commission and Federal Trade Commission compliant filing processes using Robotic Process Automation (RPA)/Intelligent Automation. We developed an Enterprise Knowledge Graph, Real-Time Machine Learner, and data auto-tagging using semantic metadata. The technology won the Service to the Citizens Award. See also US Patent# 11,689,660 B2.

In Phase II, we developed a method for storing data in a blockchain in a high-performing manner, to ensure quality training of ML/AI models, training data (data governance) and production level inference. The innovation addressed the challenge of data drift and adversarial AI attacks where the provenance of the data is unknown. The blockchain also facilitates electronic discovery throughout the complaint filing and litigation process.

Phase II leveraged a Large Language Model (LLM/Generative AI (GAI)), Enterprise Knowledge Graph, and semantic reasoning to counter scams, misinformation, and disinformation. The detection and counter malign influence technology works in 28 different languages. A honeypot was developed that interacts autonomously with bots and malign influence actors.

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