Overview
Rhino.ai is an AI-powered modernization platform that turns legacy black boxes into fully understood, agent-ready applications. It accelerates transformation, reduces costs, cuts technical debt and improves application quality by automating discovery, documentation and requirements generation with full traceability and flexibility.
Comprehensive Discovery & Documentation Rhino.ai rapidly analyzes and documents complex legacy enterprise applications--including SaaS, low-code and traditional codebases--to build complete visibility across your technology landscape. Its agentic AI extracts business logic automatically from existing code, documentation, databases and workflows, capturing hidden dependencies and inefficiencies. Rhino.ai analyzes legacy code bases, SaaS platforms, natural-language documentation and process manuals, giving you multi-perspective clarity. The platform extracts requirements, process flows and knowledge from technical specs, user manuals and process documents, and it tracks code structures, database schema and APIs across your portfolio.
Intelligent Documentation & Traceability Discovery results are organized into comprehensive deliverables that support multiple personas. Rhino.ai generates universal application documentation, user stories, test cases and process flowcharts, along with functional documentation, technical architecture mappings, business-rule extractions, modernization roadmaps and implementation-ready specifications. These artifacts are available in structured, machine-readable formats so both human teams and AI agents can consume them. Fine-grained extraction control and coverage statistics provide audit-level evidence that nothing was missed, and source-to-requirement linking delivers unparalleled trust and traceability.
Platform Architecture & Universal Application Notation At the heart of Rhino.ai is a three-phase platform architecture. In the Understand & Extract phase, AI scans code, documents, SaaS applications and rules to analyze existing systems and identify hidden logic, dependencies and inefficiencies. The Organize & Structure phase captures extracted insights in a structured repository known as Universal Application Notation (UAN). UAN standardizes business logic, giving users the power to refine existing logic before moving forward so they modernize instead of merely migrating. Finally, the Generate & Transform phase converts legacy workflows into scalable applications, supporting SaaS platforms like ServiceNow and Appian, open-source microservices and external agents. UAN outputs can produce both modern applications and comprehensive documentation.
Deployment & Control The Rhino.ai platform supports flexible deployment models: choose an enterprise-grade SaaS offering or install Rhino.ai in your self-managed environment and bring your own language model. Rhino.ai does not access your databases or any data in your environment; your data stays entirely under your control. You maintain control over security policies, compliance requirements and access controls. You can also use your preferred AI models (OpenAI, Azure OpenAI, Anthropic or your own fine-tuned models), and Rhino.ai comes with a full set of audit trails, citations, and other capabilities which provide full trust and transparency.
Human & AI-Ready Deliverables With Rhino.ai, you can update requirements or create new ones, produce user stories, test cases, ERDs and flow diagrams to accelerate implementation. Rhino.ai's documentation is ready for both human teams and AI agents. Development teams receive clear, implementation-ready documentation and detailed architecture diagrams for informed decision making. AI agents like AWS Kiro, Windsurf, Cursor, as well as low-code platform agents like Appian Composer, ServiceNow Now Creator, and Outsystems Mentor can immediately consume the Rhino.ai output to power the generation of the new, modernized application.
Flexible Output & Agentic Transformation Beyond documentation, Rhino.ai offers multiple modernization paths. It can transform legacy code and SaaS apps to modern microservices or SaaS architectures with minimal disruption. Its automated code and SaaS analysis modernizes to modern microservices or SaaS architecture and converts applications to be AI-ready. Rhino.ai also supports replacing outdated processes with agents through agentic workflows, process automation and human-agent collaboration. Rhino.ai gives organizations a secure, flexible and comprehensive modernization platform--delivering faster modernization, lower costs, reduced technical debt and higher-quality applications through automated discovery, documentation and requirements generation with full traceability and flexibility.
Highlights
- Extracts detailed functional and technical understanding from the widest variety of legacy code and low-code platforms on the market
- Generate comprehensive documents with flexible options for structure, detail, tone and more so you can tailor your results to executive, analyst, or technical audiences
- Update requirements or create new ones, produce user stories, test cases, ERDs, and flow diagrams to accelerate implementation of your reimagined application
Details
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
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Delivery details
Helm Chart
- Amazon EKS
Helm chart
Helm charts are Kubernetes YAML manifests combined into a single package that can be installed on Kubernetes clusters. The containerized application is deployed on a cluster by running a single Helm install command to install the seller-provided Helm chart.
Version release notes
The 3.02.0 release strengthens document generation quality, Copilot reliability across Nova/Claude/GPT-4.1, and platform stability. It fixes critical export, citation, and graph-view issues while introducing observability and admin experience updates for safer, faster workflows.
- Highlights
More reliable UAD and Custom Document generation at scale
Copilot stability and model parity improvements (Nova/Claude/GPT-4.1)
Faster, clearer Dynamic Object import/export with safer linking
Routine observability and diagnostics included
Admin and onboarding improvements, including new default landing experience
- Documentation & Export
Resolved document batching quality regression causing degraded section outputs
Fixed duplicate export options and improved DOCX export reliability
Diagrams: fixed generation and export issues for large/complex sections
Upload Document now supports Markdown (.md) files
Preserved system-template skeletons during large document assignment/generation
- Copilot
Added GPT-4.1 support; improved Nova Pro prompt quality and grounding
Stability: fixed long-running tool crash, routine invocation asset picker, CRUD/ID issues
SQL Tables routine failures on Sonnet resolved
Simplified tooling by removing agent extraction tool
Introduced Copilot error logging and chat mode selection
- Extraction, Graph & Observability
Routine reliability: fixed large-file executions, routine request-limit failures, low-yield consolidations
Incorporated routine observability
Graph view: corrected related-element linking and indentation alignment
- Dynamic Objects, Import/Export & UI
Bulk-write import pipeline; clearer import progress; safer cross-reference linking
Prevent silent merges for same-named DOs across routines; preserve sources on import
Export resilience under concurrent load; re-import parity for same-DO flows
Fixed misleading tree ordering and cross-routine child rendering
Fixed DO edit panel visibility and permanent-delete logout issue
- Platform & Admin
Bedrock/Nova/Claude catalog alignment; prompt caching via Bedrock
Enabled GPT-4.1 across supported features
- Quality Improvements & Fixes
UAN/UAD citations: restored missing cases across Nova and Claude
System flows: resolved multi-root inbound issues and confusing renders
Business/Data entities: corrected empty ID-field scenarios
General cleanup across UI states, export errors, and routine history timestamps
Additional details
Usage instructions
- Obtain license file and license keys from Rhino.
- Stage the license file into a Kubernetes ConfigMap
- Stage the license keys into a Kubernetes Secret
- If you wish, deploy a DB password secret to your namespace and set global.postgresql.auth.existingSecret and related keys
- Set frontend.service.url to your desired URL exposing the Rhino application
- Configure the frontend.service parameters as desired (node port, load balancer) to expose the web application outside the cluster
- Configure Helm values for license file and keys
- Configure additional Helm values/secrets for AI services, worker replicas, etc.
- Deploy: helm install rhino -n [namespace] ...
Support
Vendor support
Email: support@rhino.ai Web:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.