Generative AI Development Services for Startups, SaaS & Enterprises

Traditional AI is primarily designed to classify, predict, or detect patterns in existing data — for example, identifying whether a transaction is fraudulent or predicting customer churn. Generative AI goes further: it creates new content — text, code, images, audio, video, or structured data — based on patterns learned from training data. The practical difference for your business is that generative AI can produce, write, and build — not just analyse and flag.

We build a wide range of generative AI applications including AI writing assistants and content generators, RAG-based document intelligence chatbots, custom ChatGPT integrations, fine-tuned domain-specific models, AI-powered search and knowledge bases, code generation copilots, image generation tools, automated report generators, and multimodal AI applications that process text, images, and documents together.

The cost of generative AI development depends on the complexity of the application, the model used, the volume of data to be processed, integration requirements, and ongoing support needs. A focused first deployment — such as a RAG-based chatbot or an AI content generator for a specific workflow — typically ranges from $5,000 to $25,000. Enterprise-grade, multi-system generative AI platforms are scoped individually. Contact us for a custom estimate based on your requirements.

A well-scoped generative AI project typically takes 6 to 10 weeks from kickoff to production deployment. Simpler integrations — such as adding GPT-4o to an existing web application — can go live in 2 to 3 weeks. More complex RAG systems, fine-tuned models, or multi-system integrations take longer. We will give you a realistic timeline in our first scoping call.

Yes — and this is one of Moonstack's core strengths. We integrate generative AI with your CRM, ERP, CMS, e-commerce platform, internal databases, communication tools, and custom applications via API and native integrations. Your AI works within your existing ecosystem, not alongside it as a separate silo.

Security and data privacy are non-negotiable in every generative AI solution we build. We implement data anonymisation, role-based access controls, encrypted data pipelines, on-premise or private cloud deployment options, and compliance frameworks aligned to GDPR, HIPAA, and the India DPDP Act. For regulated industries, we document every data handling decision for audit purposes.

Not necessarily. Modern approaches like RAG (Retrieval-Augmented Generation), prompt engineering, and parameter-efficient fine-tuning mean you can build highly accurate, domain-specific generative AI solutions with relatively small, well-curated datasets. We assess your data readiness in our initial discovery session and recommend the most appropriate approach for your available data.