Chatbot Design for Enterprise CRM
Designing an intelligent customer relationship management system integrating it with conversational AI that routes intent, governs responses, and escalates with human oversight.
The Challenge
Enterprise sales teams were spending hours manually logging data and trying to prioritize leads. The existing CRM was clunky, overwhelming, and lacked predictive capabilities. The goal was to reduce friction and introduce AI-driven automation without losing human oversight.
The Solution
We redesigned the core CRM experience from the ground up, integrating AI agents that automatically summarize meeting notes, score leads based on historical conversion data, and suggest the next best actions for sales representatives. This streamlined the workflow and allowed teams to focus on relationship building rather than data entry.
Conversational Flow
The rule-based, deterministic layer. Triggered by an intent, the virtual agent follows a scripted branch you author exactly how the bot responds. No ambiguity, no improvisation. Every reply is mapped, reviewed, and version-controlled before it reaches a customer.
AI Search & RAG
Content comes from knowledge base articles; phrasing comes from the LLM summarizing them. You author what lives in the KB, not the reply itself. This separates truth (curated articles) from voice (model-generated prose), keeping answers grounded and editable.
Generative Actions
Generative responses bounded by prompts and guardrails. The model composes within constraints: tone, length, approved topics never freewheeling. Guardrails are authored alongside the prompt, so every generative path has a defined edge it cannot cross.
Intent Routing
Every conversation begins with detecting what the user actually needs. The system classifies intent across hundreds of trained categories, then routes each one to the mechanism best suited to resolve it : deterministic flow, knowledge search, or generative response.
Response Governance
No response reaches a customer without passing through guardrails. Prompts are bounded, topics are constrained, and every generative path has a defined edge. Governance is authored alongside the prompt, not bolted on after.
Human Escalation
When confidence drops or the request exceeds the bot’s scope, the conversation routes to a human agent with full context. The handoff is seamless: the transcript, attempted resolution, and escalation reason all travel with it.
Issues during testing
"The chatbot sounds dumb"
"Its stuck on simple questions"
View Clickable Prototype Below
Examples of Input mapping, utterances, error handling and recovery modes.
Making use of digital space
We decided the client cards took up too much real estate on the sales dashboard based on feedback from the sales team. We gave users the option to filter out their accounts to make the UI less overwhelming for sales teams. Below is the light mode version of what we decided on.
Below are examples of the compact version of the client cards
