Custom AI development, built into real products

The cost of custom AI development depends on the feature's complexity, the data it needs, and whether we add it to your existing product or build something new. A single focused feature, such as a chatbot that answers from your documents, is a very different scope from a full data pipeline and analytics platform. Running costs matter too, since AI models charge per use, so we plan for them from the start. Most of our projects run on fixed-price terms. After a free 30-minute call and a short discovery phase, we send a detailed proposal with a fixed price and clear milestones.

We build testing into every AI feature from the start. Each feature has a clear accuracy target, a test set of real inputs from your business, and monitoring once it is live. For chatbots and document search, we use retrieval (often called RAG), which grounds the model's answers in your own data. This greatly reduces the risk of made-up answers, known as hallucinations. When the model is not confident, it can say so, ask a follow-up question, or hand off to a person. We also show users where an answer came from, so they know when to double-check it. Accuracy is tracked over time, not just checked once at launch.

In almost every case, we can add AI to your existing product without a rebuild. We connect to your current stack through APIs and add AI features as new services or endpoints, so your core product keeps running as it is. Common examples are a support chatbot, smart search, document processing, or automated reports. A rebuild is only needed when the underlying data structure makes integration impractical, for example when key data is spread across systems that cannot talk to each other. If that is the case, we tell you openly during discovery and suggest the smallest change that makes the AI feature possible.

Anyone can call the OpenAI API. The hard part is everything around it. That includes the retrieval system that grounds the model in your data, the testing that catches bad answers before users see them, the cost controls that keep the feature affordable at scale, and the monitoring that tells you when accuracy drops. It also includes connecting the AI to your product, your users, and your permissions, so each person only sees what they should. That is what custom AI development means to us: the engineering around the model, not just the model call. We also design it so you can switch AI providers later if prices or quality change.

We use data minimization by default. We send the model only what it needs to complete a task, never raw database exports. Before we recommend any AI provider, we review its data retention and training policies, so you know whether your data could be stored or used to train models. For sensitive data, we look at private or on-premise model deployments where they make sense. We also build access controls, so data never leaks between users, teams, or sessions, and we log how the AI is used so you can review it. If you work in a regulated field, we plan these rules with your team during discovery.

A focused AI feature is realistic in 8 to 12 weeks. Good examples are an AI chatbot that answers questions from your documents, an automated data extraction pipeline for invoices or forms, or a smarter search for your product. The biggest success factor is a clear problem definition and access to the right data before we start. We help you pick the feature with the highest impact first, rather than trying to build everything at once. A small, working feature in real use teaches you more than a large plan on paper. Once it proves its value, it becomes the base for the next one.

We are product engineers who build AI into real products, not consultants who produce reports. Every custom AI development project follows the same engineering standards as the rest of your code: tested, monitored, and built to stay accurate over time. We build AI chatbots, document processing, automation pipelines, data dashboards, and AI features inside existing apps. And because we also build web platforms and mobile apps, we can put AI directly into the products your users already open every day. With 15+ years of experience and 400+ projects behind us, we focus on what works in production, not on demos.