Beyond the demo chatbot
Most chatbot projects fail for boring reasons: the bot cannot access live inventory, it hallucinates return policies, or it has no way to hand off to a human when stuck. We build assistants that connect to real systems — order status, ticket creation, appointment booking — not just static FAQ pages.
For a context-aware marketplace chat we linked conversations to specific products and orders so vendors and buyers were not repeating information already in the system. For an FMCG ERP we built an internal assistant that answers margin and stock questions by querying live database views, not cached spreadsheets.
Agents that take action
Modern models support tool calling — the assistant decides when to look up a record, create a ticket, or send a notification. We define those tools carefully with permission checks so a customer-facing bot cannot trigger admin actions. Each tool returns structured data the model can reason over, which cuts down on made-up answers.
When the model is uncertain or the user asks for something outside scope, the conversation routes to a human with the full thread attached. Support teams stop asking customers to repeat themselves.
On-prem when you need it
Some clients — banks, government contractors, healthcare — cannot route customer data through third-party APIs. We deploy open-weight models via Ollama or vLLM on your servers, with the same RAG and tool layers as cloud setups. You trade some model capability for data control, and we are upfront about where that line sits for your use case.