AI Development Services

Which Service Fits Your Problem

Most projects start from a problem rather than a technology, so the quickest way to choose is to start from what is going wrong. If people spend their day answering the same questions from documents, manuals, or policies, that is RAG development: answers grounded in your own content, with a citation for every claim. If you want a model such as GPT or Claude inside a product or internal tool you already run, that is LLM integration.

If the work is a multi-step process someone currently drives by hand, such as qualifying inbound leads, assembling estimates, or chasing project updates, that is agentic AI development: software agents that plan, act across your systems, and hand off to a person when judgement is needed. When the data those systems depend on is scattered, stale, or locked in spreadsheets, data engineering comes first, because retrieval and agents are only as reliable as what feeds them.

If you are not yet sure which workflow deserves the investment, enterprise AI consulting is the starting point: a readiness assessment and a short, ranked list of use cases with the return each is expected to deliver. When the goal is a new product with language models at its core, custom application development covers the full build, web and mobile. Most engagements combine two or three of these, and the case studies show how that played out in practice.