SBIR/STTR Research Award

SBIR Phase I: Scalable AI-Enabled Automated Pricing Tool for Service Industries

Award identifier e94d3c4021851c322a08fa1e7e32b8978e26f6cc37396396cddc4f8319e10a47Awarding agency National Science Foundation

Award amount

$299,700

Program and phase

SBIR · Phase I

Award year

2025

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
National Science Foundation
Branch
Not reported
Recipient company
Pricing Service, Inc
Proposal award date
2025-10-01
Contract end date
2026-09-30
UEI
S72QKYJUQ843
Solicitation number
NSF 24-579
Topic code
AA

Research abstract

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to empower organizations that are exposed to dynamic pricing – such as in the transportation, retail and e-commerce sectors - with affordable, automated revenue management tools that enhance pricing decisions and improve financial performance. Organizations in these sectors often lack the resources, data infrastructure, and expertise needed to implement advanced pricing strategies. This project aims to provide access to smart pricing technology by developing an artificial intelligence-driven platform capable of operating effectively in data-scarce and competitive environments. The broader societal impacts include strengthening small businesses and supporting the economies surrounding dynamic pricing sectors. The innovations in forecasting, optimization, and competitive analysis have potential applications across a range of sectors experiencing dynamic pricing, thereby offering the potential for substantial commercial impact. This Small Business Innovation Research (SBIR) Phase I project addresses the challenge of optimizing pricing decisions in dynamically priced market sectors with limited historical data and intense price competition. The research objectives are to develop (1) novel demand forecasting methods that combine observational data with lightweight online experimentation; (2) an automated competitor analysis engine using econometric and machine learning tools; and (3) fast heuristic optimization algorithms to support real-time, network-wide pricing decisions. The project will implement and validate these innovations using real-world data and simulated operational settings. Anticipated technical results include accurate demand forecasts, robust pricing algorithms, and demonstrated revenue gains in pilot environments - laying the groundwork for scalable commercial deployment and continued innovation in dynamic pricing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Technology keywords

Not reported

Agency intelligence

National Science Foundation

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.