AI development services

Our services cover 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 extending a product you already operate or building one around AI from the start.

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

We use 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 selects the model per use case and structures the product so that switching models later is a small change rather than a rewrite.

Everything runs under your own accounts, including the model provider, the vector store and the cloud. WeaveLines applies the provider settings that keep your data out of model training, and when data is not permitted 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 establishes how often the feature answers correctly, 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 the strength of a demonstration alone.

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

The timeline depends on the feature, the state of your data and how much of the product it touches, so WeaveLines provides one 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 delivered as a fixed-price project with 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 provides a quotation after a short call about what you have and what the feature should do.