We integrate AI into existing business systems: websites, web apps, CRM platforms, support workflows, internal knowledge bases and mobile applications. The goal is practical automation, not a standalone chatbot with no business context.
answers using your services, prices, cases and FAQ
retrieves answers from documents, policies and internal notes
summarizes leads, classifies requests and drafts replies
generates drafts for listings, emails, articles and support messages
We use a backend-mediated integration so the frontend never exposes model keys or unrestricted access to private context.
The assistant should know the service scope, pricing, limitations and next steps. We prepare the knowledge source and test prompts before launch.
A good AI assistant can collect a lead, summarize it, send it to the CRM, notify a manager and preserve the conversation context.
Start with one workflow where AI can save time or improve conversion: support answers, lead qualification, CRM summaries or internal knowledge search.
Yes. We usually add a backend proxy, knowledge source, prompt controls, logs and lead handoff instead of placing a raw model call in the frontend.
Most business use cases do not require fine-tuning. Retrieval augmented generation is usually enough: the model receives relevant context from your data and answers using it.
Yes, if access is designed carefully. We can restrict what the model sees, log requests and keep sensitive actions behind explicit user confirmation.
Tell us what data AI should use, where the answer should appear and what should happen after the answer.