Genum

Instead of hand-writing an elaborate spec, you describe what you want in plain language and Genum converts that intent into a structured, reusable AI definition. The output carries the schema and configuration with it, so the same spec runs consistently across environments. This lowers the bar for people who understand the business logic but not prompt syntax.

Genum saves test cases alongside your prompts and replays them to compare outputs against expected results. A newer release can check an agent's tool calls step by step, not just the final answer, so a broken chain gets caught before users see it. Replays run against the current draft, which means you can test an edit before committing it.

Every prompt is committed with full metadata, including the model configuration it ran with. You can roll back, compare versions, and see what changed between releases. This brings the commit-review-revert loop that developers expect from Git into prompt work.

Each prompt execution is logged with its inputs, outputs, model metadata, and timestamps. That record supports the sign-off and traceability that compliance teams ask for, and it turns a past run into a test case you can replay under the same conditions.

Prompts and test suites work across multiple providers, including OpenAI, Gemini, and custom endpoints, with support for custom output schemas. You avoid rewriting everything if you switch models, and you can keep sensitive workloads on a local model while sending others to the cloud.

Specialized agents handle prompt generation, validation, auditing, and commit messaging. The point is to cut the manual overhead of maintaining a prompt library, so routine checks and messages happen without a person babysitting each step.

You upload PDFs and images and let an LLM extract text, describe content, or convert documents into structured data. Receipts, invoices, and forms become clean fields that downstream systems can process, which removes a common manual step in back-office work.