A chatbot that actually knows your business.
Generic chatbots give generic answers — canned responses, no real knowledge of your product or documents, and no way to verify where an answer came from. Customers bounce, support tickets pile up anyway.
A conversational AI chatbot backed by real retrieval — it reads your actual documents, product data, or knowledge base, and answers with citations back to the source. Not a script tree, an assistant that knows your business.
Talk to PRIYA — a live example of this exact system
Try It Live →User Query -> Bi-Encoder Retrieval -> Cross-Encoder Re-Ranking -> LLM Generation -> Faithfulness Scoring -> Response
For simple business AI implementations — chatbot, FAQ assistant, basic knowledge base, MCP setup.
Workflow automation, API/Telegram/email integrations, custom MCP servers.
RAG, NL2SQL, business analytics, document intelligence.
Tool-using AI agents and multi-agent systems that plan and act.
For larger or more complex requirements — scoped on a call.
Directional, not a fixed quote — real scope depends on your data and integrations.
Most chatbots either follow a fixed script or send every question to a raw LLM with no grounding. This uses retrieval — it actually reads your documents or data before answering, and can point to the source it used.
The AI Starter package begins at $249 for a focused chatbot build. Final scope and price depend on your data sources and integrations — every project gets a real quote after a conversation, not a generic number.
It doesn't need retraining to learn new information — feed it new documents and it retrieves from them immediately. That's the core advantage of RAG over fine-tuning for most business use cases.
Tell me what you're trying to solve — I'll tell you honestly whether this is the right fit.