AI Development Services

AI development services are the engineering and advisory work that turns a business problem into a working AI system. That usually means picking the use case, preparing the data, building or fine-tuning a model, wiring it into a product or workflow, and monitoring it once it is live. Consulting-only engagements stop after the first step.

A focused proof of concept typically runs $15K to $40K. A production LLM application or a custom model inside a live workflow usually lands between $60K and $200K, and enterprise programs with heavy data work run higher. What moves the number most is the state of your data, not the model. After a free discovery call we come back with a scoped estimate, no obligation.

A proof of concept on data you already hold typically takes 4 to 6 weeks. A model or LLM application serving real users usually runs 3 to 5 months, and data preparation is often the longest stretch of that. Sprints are two weeks, so you see the model's output on your own data long before launch.

When the decision you want to automate depends on data only you hold, and it repeats often enough that a small gain in quality pays for the build. If a tool you can buy already does most of the job, buy it and spend the budget on integration instead. We say that to clients more often than you might expect from an AI vendor.

Yes. Most AI work we do goes into systems that already exist: a CRM, an ERP, a support desk, or a product with paying users. The model sits behind an API and its output appears in the screens your team already works in. The integration and the data plumbing usually take longer than the model itself.

It depends on the task, not on a universal minimum. Fine-tuning a language model for a narrow job can work from a few hundred good examples. A tabular prediction model usually wants thousands of labeled outcomes. A retrieval assistant needs no training data at all, only documents worth searching. We check this in week one, because it decides whether the project is viable.

Start with an existing foundation model through an API. It is faster to trial, cheaper to abandon, and it improves without you doing anything. Move to fine-tuning when prompt work stops closing the quality gap, and to a self-hosted open-weight model when data residency, unit cost at volume, or latency makes the API the wrong fit. Training a foundation model from scratch is almost never the right call for a business application.

No. Your data is used to build your system and nothing else. We do not train shared models on it, we do not reuse it for another client, and we configure commercial model providers so training on your inputs is turned off. Retention and deletion terms are written into the contract, and we delete on request.

You do, 100%. The code, the trained model weights, the fine-tuning datasets, the prompts, and the evaluation sets are yours, assigned in the contract, including anything derived from your data. We sign NDAs on request. Where a third-party model is part of the system, we tell you upfront what its license permits so nothing surfaces later at renewal.

Against a test set and a pass mark agreed with you before the build starts, never against a demo. We define what a correct answer looks like for your use case, hold back data the model never sees, and have your subject matter experts grade a sample of the output. Your team signs off on the result. If it does not clear the bar, we say so instead of shipping it.