Rhino provides an enterprise logic layer by automatically discovering and mapping fragmented business rules across disparate systems. This extracted logic is organized into a governed, traceable logic graph, creating a system of record for enterprise operations. The platform enables faster system modernization, reduces rework, and provides governed business rules necessary for AI agent enablement.
Funding
$50M 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.
KDFounders
Product
Problem
Enterprises face challenges in modernizing legacy applications due to the complexity of understanding and extracting business logic, data structures, and workflows from outdated systems. Traditional modernization approaches are often time-consuming, costly, and require extensive manual effort. This can lead to delays, increased technical debt, and vendor lock-in.
Solution
Rhino.ai offers an AI-powered platform that automates the modernization of legacy applications by extracting business requirements and transforming them into modern software solutions. The platform uses AI to mine code, documentation, and application artifacts to discover business logic, workflows, and data structures. It then provides tools to analyze, document, and transform these elements, enabling the generation of new applications deployable to various platforms, including SaaS, low-code/no-code, microservices, and cloud-native environments. This approach reduces modernization costs, accelerates development, and avoids vendor lock-in.
Target Audience
The primary target audience includes enterprises and government agencies seeking to modernize legacy applications, reduce technical debt, and accelerate digital transformation initiatives.
Features
- AI-powered discovery and extraction of business requirements from legacy applications, source code, and application artifacts.
- Automated generation of detailed documentation for legacy systems, customizable business requirements, and design documentation for new applications.
- Cross-platform AI-enabled modeling of workflows, schemas, roles, and interfaces.
- Application generation and deployment to low-code/no-code platforms (e.g., ServiceNow, Unqork, Appian, OutSystems), microservices architectures, and cloud-native solutions (AWS, GCP, Azure).
- Portfolio analysis tools for comparing and rationalizing applications to identify optimization opportunities.
- Automated refactoring of applications in line with best practices.
- Customization analysis for ServiceNow modules to detect and assess unwanted customizations.