AI SaaS & Product Development

Scope drives the number, so we agree the build before quoting. What moves it most is whether you need retrieval over your own data, how many models or tools are orchestrated, how much of the surrounding product has to be built alongside the AI, and how strict your data-handling requirements are. Our free SaaS cost calculator gives an early range, and we confirm a fixed price for the agreed scope before development starts.

Typically three to eight weeks. That covers the product the AI lives inside, accounts, data, interface, as well as the AI-specific work: model integration, retrieval pipeline, agent design, streaming and prompt work. Products orchestrating several models or tools run longer, mostly because evaluating them properly takes time.

Primarily Anthropic's Claude models and OpenAI's GPT models, chosen per use case rather than by brand preference. For cost-sensitive or privacy-sensitive workloads we integrate open-weight models. Many products end up using more than one behind a routing layer, sending cheap high-volume work to a small model and reserving a frontier model for the parts that need it.

Retrieval-augmented generation lets a model answer using your documents and data rather than only what it learned in training. You need it whenever correct answers depend on information the model has never seen, your product catalogue, your policies, a specific customer's records. If your questions can be answered from general knowledge, you do not need one, and adding it would only add cost and latency.

A chatbot takes input and returns text. An agent takes actions, calling APIs, retrieving data, chaining several steps and deciding what to do next. Agents are more capable and considerably harder to make reliable, which is why we scope their boundaries tightly. An agent that can do anything is an agent nobody can predict.

No user data goes to a model provider unless that is an explicit, deliberate part of your design. We advise on data residency, on what each provider's terms allow them to do with your inputs, and on anonymisation where it applies. Where requirements are strict enough, self-hosted open-weight models are a workable alternative and we will say so rather than sell you something heavier.

It varies enormously with usage, which is why we model expected token consumption before building rather than after. A low-traffic internal tool can run on a very small monthly spend, a high-volume customer-facing pipeline is a different order of magnitude. The important part is knowing which one you are building before you commit to it.

Yes. We review the existing codebase first and then advise honestly on the approach, sometimes integrating directly is right, and sometimes a separate service alongside your application is cleaner than threading model calls through a codebase that was never shaped for them.

Yes, and it is a large part of what we do. Building AI in from day one is different from adding it later, the data model, the permissions and the cost structure all get designed around the model from the start, instead of retrofitted around a product that was never shaped for it. This is the same first-release process as our SaaS and MVP development, with the AI architecture decided alongside everything else rather than bolted on afterwards.