AI development services

AI assistants and chatbots, generative AI features such as summarization, drafting, classification and extraction, answers grounded in your own data through retrieval, AI agents and workflow automation, and the integration of all of these into products that already exist. WeaveLines builds them into web and mobile products, either adding to a product you already have or building one around AI from the start.

We build with existing models: Claude, GPT, Gemini and open models, chosen per use case. Most products get further, faster, with the right model and the right data around it than with a custom-trained one, and that is where WeaveLines puts its effort. We do not train custom models.

Claude, GPT or Gemini for most features, and an open model when your data cannot leave your environment or when cost matters more than the last point of quality. WeaveLines chooses the model per use case and builds the product so that switching models later is a small change rather than a rewrite.

Everything runs under your own accounts: the model provider, the vector store, the cloud. WeaveLines uses the provider settings that keep your data out of model training, and when data is not allowed to leave your environment at all, we run open models inside it.

We measure it. Before anything ships, an evaluation set built from real examples tells us how often the feature gets it right, and it runs again on every change afterwards. A person reviews the failures and decides what good looks like. WeaveLines does not ship an AI feature on a demo alone.

Yes. Most AI work starts inside an existing product. WeaveLines begins with a short review of your codebase and your data, picks the feature with the clearest value, and builds it in a way your existing team can maintain.

It depends on the feature, the state of your data and how much of the product it touches, so WeaveLines gives a timeline after the framing session rather than before it. Adding a feature to an existing product with existing models is the fastest kind of AI work; building a product around AI from the start takes longer.

A single AI feature is usually a fixed-price project: an agreed use case, an agreed definition of good, and a launch date. Larger AI products run on time and materials billed monthly. After launch, a retainer covers monitoring, improvements and the next features. WeaveLines quotes after a short call about what you have and what you want the feature to do.