AI product & SaaS development from idea to MVP to launch

In ordinary software the same action gives the same result. In an AI product the answer varies, and the cost of each request varies with it. So three layers are added: quality evaluation that measures the answers, cost tracking on every call, and a protection layer against abuse. The rest of the product, such as accounts, interface and payment, is built like any well-made system. If you only need a business system without a model at its core, our custom software development service is the better fit, and if you need an agent that performs tasks, we have a separate service for that. Teams that build these products also need both skills at once: understanding models and running a live service over time.

It depends on scope, but a focused MVP with one core AI capability, one main screen, accounts and basic billing is usually built in a few weeks of work rather than half a year. What mostly sets the difference is the number of capabilities. That is why we begin with a specification that narrows the product to the most important thing and leaves the other ideas for later versions. At the end of scoping you receive a detailed schedule for each stage, with a decision point after the feasibility proof. In practice every new capability goes through a short definition of what counts as a good answer, which prevents surprises after launch.

We measure every call per user and per feature, and work out what an active customer costs before pricing is set. Then cost is reduced in several ways: a small model for simple tasks, a cache for repeated questions, shorter context and quotas for each plan. An alert is also defined for unusual consumption, so you do not discover a problem in an invoice. Pricing then rests on real data rather than on a guess, and you can see how each change affects the cost of an average customer. Each of these decisions is recorded in a short document, so anyone who joins the team later understands why the product is built the way it is.

We limit what the model can see and do: it receives only the current user's data, and sensitive actions require confirmation. We add rate limiting, filtering of malicious input and output screening. Customer data is isolated between accounts, and keys stay on the server side only. Suspicious usage is logged as well. In line with Amendment 13 to the Privacy Protection Law, we define in advance which data is stored and for how long. This is not legal advice, and a privacy adviser can review the result if your case requires it. Before launch we also test the product against common bypass attempts, and we repeat the check after every significant change.

You do. The code, design, prompts and evaluation sets belong to your company, and the repository and the cloud and model-provider accounts are opened in your name from the start. There is no lock-in with us: at the end of the project you receive documentation and training, and development can move to an in-house team. The agreement states this in writing. Open-source components keep their own licences, and we list them so it is clear exactly what the product uses and under which terms. If partners or investors ask for proof of ownership, we can prepare an organised document of the components and licences for them.

Yes, and we recommend it. An abstraction layer isolates your code from the provider, so changing a model is a configuration change rather than a rewrite. The evaluation set shows straight away whether a new model is better, cheaper or worse on your cases. This protects you from price increases, service interruptions or a newer model that is more cost-effective, and it lets you use several providers in parallel depending on the type of task, for example a strong model for analysis and a cheap one for simple classification. That way the product does not depend on the commercial decisions of one company, and contract and cost management stays in your hands.