Flapico

The playground is where prompts get written and tried out. You run a prompt against different models and adjust its configuration without leaving the page, so you can see how the same instruction behaves across providers. Versioning is built in, which means each edit becomes a recorded version rather than an overwrite. It is the starting point for everything else the platform does.

Flapico runs large tests on your datasets with different combinations of models and prompts. Runs update in real time, execute concurrently and can keep going in the background, so you're not stuck waiting on a single pass to finish. This is the part that separates a managed prompt workflow from testing one response at a time in a chat window.

Results feed into the platform's Eval Library, where you evaluate outputs with shared criteria instead of personal judgment alone. For each LLM call you get granular detail, alongside metrics and charts that show how versions compare. Running evals here turns a pile of model responses into something you can actually decide on.

The model repository keeps all your models in one place. It ships with built-in support for popular models, so you don't have to wire each provider up from scratch. Everything sits under the same security model as the rest of the platform, which matters when models and prompts are tied to real product data.

Flapico markets itself as enterprise ready, and the security section backs that up. Your credentials are protected with Fernet encryption at AES-128, and documents you upload are stored in HIPAA compliant systems. Access restriction at the database level adds a layer beyond software bugs, and role based access controls let you manage what each person in your organization can reach. For teams handling sensitive prompts, these controls are often the deciding factor.