Generative AI Development Services

Generative AI development services involve building custom AI solutions that create, summarize, transform, or analyze content and data using large language models. This includes AI chatbots, document assistants, content generation tools, RAG-based search systems, custom LLM integrations, and AI-powered features inside SaaS products. Zygobit designs and builds these solutions tailored to your business data, workflows, and goals.

Generative AI focuses on creating or transforming content and data: drafting text, answering questions, summarizing documents, or generating structured outputs from unstructured input. Agentic AI goes further by taking sequences of actions autonomously, planning steps, calling tools, and completing multi-step workflows with minimal human input. Many real-world AI products combine both. Zygobit builds both, and this page covers our generative AI work specifically.

Yes. We build chatbots that are grounded in your own data using retrieval-augmented generation (RAG), fine-tuning, or structured prompting, depending on your use case. The chatbot can be deployed on your website, inside your app, or as an internal tool. It will answer based on your documents, policies, or product content rather than general internet knowledge.

Yes. We specialize in connecting generative AI to existing CRMs, helpdesks, CMSs, databases, SaaS platforms, and internal tools via APIs and data pipelines. The goal is to make AI useful inside the tools your team already uses, not to replace them with a separate system.

A focused prototype typically takes two to four weeks. A production-ready generative AI feature or standalone product, including integration, testing, and safety evaluation, typically takes six to twelve weeks depending on scope. Larger platforms with multiple AI features or complex data pipelines take longer. We give a written estimate after a scoping call.

Yes. We design architectures that keep your data in your own infrastructure wherever possible. For RAG-based systems, your documents stay in your vector store, not inside a third-party model. We use access controls, private API deployments, and audit logging to meet your data security requirements. We discuss data residency, compliance needs, and model provider policies with you during planning.

We work with GPT-4, Claude, Gemini, Mistral, Llama, and other open-source or fine-tuned models depending on what fits your use case, latency requirements, cost budget, and data privacy needs. We are model-agnostic and recommend the best option for your specific situation rather than defaulting to a single provider.

Retrieval-Augmented Generation (RAG) is an architecture where the AI searches your own data before generating an answer. Instead of relying purely on what the model learned during training, it retrieves relevant documents or records from your knowledge base and uses them as context. Use RAG when you want the AI to answer based on your specific policies, products, documentation, or business data rather than general knowledge.