PAXAFE offers an AI-enabled, device-agnostic risk management platform that automates the analysis of sensor and third-party data to predict and mitigate supply chain disruptions. By quantifying risk and providing actionable recommendations, the platform enhances operational efficiency and improves on-time delivery for industries such as pharmaceuticals and food and beverage.
Funding
$14.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
FVFounders
Product
Problem
Supply chains generate vast amounts of sensor and third-party data, but extracting actionable insights to proactively manage disruptions remains a challenge. Traditional visibility solutions often lack the intelligence and automation needed to effectively predict and mitigate risks such as delays and temperature excursions. This results in reactive responses, reduced operational efficiency, and strained customer relationships.
Solution
PAXAFE offers an AI-powered risk management platform that automates the analysis of supply chain data to predict and prevent disruptions. The device-agnostic platform contextualizes visibility data, quantifies risk on lanes and live shipments, and recommends improvements to standard operating procedures (SOPs). By leveraging machine learning, PAXAFE enables proactive decision-making, minimizes delays, prevents temperature excursions, and optimizes on-time delivery (OTD). The platform's recommendation engine automates control tower intervention, driving SOP improvements that positively impact the bottom line.
Target Audience
PAXAFE serves pharmaceutical, food and beverage, and cold chain providers seeking to enhance supply chain visibility, predict OTIF, automate CAPA/RCA, and quantify risk.
Features
- AI-driven prediction of potential supply chain disruptions, including delays and temperature excursions
- Device-agnostic data ingestion, supporting both passive and active monitoring devices
- Automated extraction of patterns, trends, and insights from sensor and third-party data
- Risk quantification on lanes and live shipments, enabling prioritization of critical factors
- Recommendation engine for SOP improvements based on contextualized data analysis
- Digitization of data from passive temperature loggers