ExistBefore
IT

Developer

Software, development, SaaS, AI

Code, datasets, algorithms, AI models, and software deliveries change constantly, which is exactly why they must be accurately documented. This section helps you secure versions, files, technical components, and shared materials, allowing you to prove what existed, what was delivered, and in what form, prior to modifications, integrations, or disputes.

  1. How to prove what was actually delivered in a software project

    In software, "delivered" can mean many things: code, APIs, access credentials, documentation, or even a promise made during a call. Putting things in order and locking down the right versions avoids endless arguments about what was included. If you have important deliveries,…

  2. How to protect an internally trained open-source AI model

    An internally trained open-source model is a curious hybrid: the foundation is public, but the value lies in what you built on top of it. Protecting it means properly documenting what you did, when, and in what version. Before sharing it or putting it into production, prepare a…

  3. How to protect a proprietary algorithm

    A proprietary algorithm should be treated like a secret recipe written in code: the value lies in the logic, the versions, the datasets, the tests, and the decisions that brought it to life. Documenting it properly helps reconstruct "what existed, when, and in what form".…

  4. How to protect a clean AI dataset before sharing it with third parties

    A clean AI dataset is often more valuable than the raw dataset: it contains selections, corrections, normalisations, exclusions, annotations, and operational decisions. Before handing it over to a client, you should document exactly what you are sharing, in which version, and…

  5. How to manage an atypical service contract

    An atypical contract is often a puzzle built from emails, calls, attachments, and operational promises. Managing it well means turning that puzzle into a clear sequence of versions, decisions, and documented deliveries. If the relationship is already underway, start immediately…

  6. How to certify an internal AI training dataset

    An internal AI training dataset is a living part of the product: it contains data, choices, exclusions, cleaning processes, versions, annotations, and often also "distilled data" generated by other models. Documenting it well ensures you know what was used, when, where it came…