AI Development Services

Several factors determine AI development costs, including task complexity, data size, model selection, integrations, performance, and security requirements. The budget is also affected by architecture, deployment, cloud infrastructure, and the need for custom development. Before starting the project, we assess the use cases, technical requirements, and available data, then define the optimal scope and estimated cost.

The timeline depends on the scope, complexity of the use cases, data readiness, number of integrations, and security and performance requirements. We can launch a simple AI MVP in less than 90 days. An individual feature can be implemented faster, while complex systems require more time for architecture, development, testing, and deployment. At the start, we define the tasks, set priorities, and create a roadmap for building the artificial intelligence product.

Yes. As an AI development provider, we can join at any required stage, from evaluating a PoC to further developing an existing product. We examine the current architecture, code, models, data, and performance, and identify technical limitations and areas for improvement. If the solution has already demonstrated its value, we help prepare it for production, integration, and further scaling. This allows us to preserve what already works and focus engineering resources on the necessary changes.

The best approach is determined by the specific use case, data, and desired results. Ready-made LLMs and other models are often sufficient for generative AI, chatbots, content processing, and language applications, especially when fast deployment and controlled costs are important. Custom ML makes sense when specific algorithms, proprietary prediction logic, or work with unique data are required. We compare available technologies and business cases to choose the optimal artificial intelligence approach.

Existing company data is not always required. For some use cases, ready-made models, public datasets, or external sources can be used. If corporate data is already available, we assess its structure, quality, accessibility, and processing methods. If the information has not yet been prepared, we help define the required format, sources, and workflows for preparing it. This makes it possible to plan development even before the information base is fully ready.

We consider security during design and implementation rather than adding it after development. We examine what data is processed, where it is stored, and which systems have access to it. Depending on the requirements, we use isolated infrastructure, access controls, encryption, secure APIs, and appropriate compliance approaches. We also assess the terms of use for external models and cloud services to protect corporate data at every stage.

For each use case, we define relevant evaluation criteria, such as accuracy, relevance, factuality, latency, retrieval quality, or other metrics. We conduct evaluations using real or representative data and test key user scenarios and integrations. We also assess performance, security, and operational stability. This testing helps identify issues before launch and prepare the AI for real-world workloads.

Cost control begins at the architecture stage. We consider model selection, token usage, context, caching, routing, and infrastructure. Once deployed, the solution is monitored to track resource consumption and find ways to optimize it. When necessary, we switch models, refine workflows, and optimize infrastructure. This helps keep costs predictable even as workloads and the number of users increase.

Yes. After deployment, we can continue providing technical support and monitoring AI to track performance, quality, latency, usage, and infrastructure. We analyze how the system performs on real data, identify issues, and find opportunities for optimization. When needed, we update models, improve prompts, retrieval, or architecture, and adapt the system to new workflows.

Rights to the code, custom software, and other deliverables are defined by the terms of the agreement. Client data remains under the client’s control, and the terms of its use are agreed upon before implementation begins. We separately define the terms for third-party models, frameworks, APIs, and other technologies that may have their own licensing restrictions. Before the project begins, we document these matters to ensure transparent terms for the ownership and use of artificial intelligence.