Every support ticket that sits unanswered costs customer trust. A large support team can take weeks to fully staff every channel, and response times stretch when ticket volume spikes. First-contact resolution drops, customers re-submit the same question, and the satisfaction score follows.
AI customer service agents change this math. An AI agent answers from the help center, past tickets, and product docs the business already runs, in the tone the brand uses. The agent resolves the majority of routine queries on first contact and hands off the complex ones with full context. Building this system by hand, the way most teams have done it, takes three to six months of integration work. Shakudo deploys it in days.
Shakudo deploys an AI agent that answers from the knowledge base, help center, and ticket history the business already has. The agent reads each incoming query, pulls the relevant documentation, and returns a grounded answer with a citation to the source it used. First-contact resolution rises and response times fall from hours to seconds for routine questions. The agent keeps its answer consistent across thousands of tickets, and it hands the complex ones to a human with a summary of what it has already tried. A support team that used to need three to six weeks of integration now runs the agent in days.
Because the AI runs entirely on the customer's own infrastructure, it can read the internal knowledge base, ticket history, and product docs that the business cannot send to a third-party cloud AI vendor. That is what makes this support possible in the first place. Most cloud chatbot platforms need the help-center content to leave the environment, and a lot of support documentation is too sensitive to leave. Sovereign AI on the customer's own infrastructure closes that gap. The agent stays fully owned and controlled, from model to memory.
The agent runs on a stack the data and ML teams already know. LangChain orchestrates the natural, context-aware conversation. Llama 3, a large language model, powers the natural language understanding and generation that reads each query and writes the reply. Qdrant is the vector store that makes the help center, docs, and ticket history searchable, so the agent always has the right answer at hand. Grafana shows agent performance and customer satisfaction in real time. MLflow tracks and manages the evolution of the AI models over time, and MinIO provides scalable storage for the growing knowledge base.
Support, customer experience, and operations teams that handle high volumes of customer inquiries and want faster, more consistent answers without scaling headcount one to one. The agent handles the repetitive inquiries that fill most of a support queue, so the human team can focus on the cases that truly need a human. Also a fit for companies whose support knowledge base or ticket history is too sensitive to hand to a public cloud AI vendor.
The agent pulls its answer from the help center, product docs, and ticket history the business already runs, and it returns a citation to the source it used. Qdrant makes that knowledge base searchable, so the agent grounds each reply in a real document. Answers stay consistent across thousands of tickets, and the agent flags any query it is unsure about for a human.
The agent hands off to a human with a summary of the customer's question and what it has already tried. The human agent gets the full context in one place, so the handoff is quick and the customer does not repeat the story.
A support team that would normally need three to six months of manual integration gets the agent running in days. Shakudo deploys the full stack, from the language model to the knowledge base, on the customer's own infrastructure.
For customer support, that means a routine ticket that used to wait hours now resolves in seconds, with a human agent for the rest. Book a demo and see an AI support agent answer from the knowledge base the business already has.