Test AI workflows inside proprietary and closed systems
Develop proof-of-concept agents and workflows using internal documentation, retrieval, validation, and automation.
Start a conversationWhat matters
Proprietary platforms may have little or no public documentation.
Internal knowledge can be difficult to connect with AI workflows.
Enterprises need a practical way to explore an AI use case before building a larger system.
Benefits
Turn an AI workflow idea into a working proof of concept.
Use internal documentation to support retrieval and validation.
Explore automation within proprietary platforms and closed enterprise systems.
Evidence
Built more than 100 AI prototypes across science, education, healthcare, finance, and other areas over three years.
For a biobanking company, scraped internal documentation and built retrieval and validation tools for a proprietary platform with zero public documentation.
Wired the biobanking tools into a pipeline and deployed working user workflows.
Questions
Can you work with a platform that has no public documentation?
Yes. The biobanking proof of concept used scraped internal documentation to create retrieval, validation, and workflow tools for a proprietary platform with zero public documentation.
What might a proof of concept include?
Depending on the use case, it can include internal knowledge retrieval, validation tools, an AI agent, workflow automation, and a working pipeline.
Interested?
Contact the company through its original website.