Chatbots that resolve questions. Not redirect them.

Off-the-shelf chatbots typically match keywords against pre-written responses or follow decision trees. They work for simple FAQ deflection but break down when customers ask questions in their own words, need multi-turn context, or have requests that cross knowledge domains. A custom conversational agent uses LLMs for natural language understanding, retrieves answers from your actual knowledge base via RAG, maintains conversation context across turns, and escalates to humans with full context when needed. The tradeoff: more upfront investment, but significantly higher resolution rates and a system that improves with your content.

The agent is designed to know its limits. Every response carries a confidence score. When confidence is low, the agent routes the conversation to a human agent with the full transcript, identified intent, and what was already attempted. The customer does not start over. The analytics dashboard tracks these handoffs so you can see which question types are escalating and feed that back into knowledge base improvements.

Yes. The retrieval pipeline connects to your existing content — help articles, product documentation, internal wikis, FAQ databases, and support ticket history. We build the ingestion, embedding, and search infrastructure so the agent retrieves relevant content in real time. When your documentation changes, the agent reflects those changes automatically — no manual retraining step.

The conversational agent deploys to web chat widgets, mobile apps, Microsoft Teams, Slack, and voice interfaces. The core dialogue engine is channel-agnostic — one agent serves all channels with channel-specific adaptations for UI and interaction patterns. You can start with a single channel and expand to others without rebuilding the agent.

Guardrails are built into the pipeline at multiple layers. The retrieval layer uses confidence scoring to ensure answers are grounded in your knowledge base — not hallucinated. Content safety filters catch inappropriate or off-topic responses. Response validation checks for factual consistency against source documents. And the human handoff mechanism ensures anything below the confidence threshold goes to a person, not to the customer as a guess.

The Discovery Sprint produces a working prototype in 2 weeks using your actual content. A production build typically takes 6–10 weeks depending on the number of channels, the size and complexity of your knowledge base, and whether integrations with CRM or helpdesk systems are involved. The production build includes intent recognition, dialogue management, handoff workflows, analytics, and deployment — not just the chatbot interface.