Chatbots that answer questions accurately — not that deflect them creatively.
Most business chatbots fail for the same reason: they're FAQ bots with a conversational wrapper. They handle the three questions they were trained on and fall apart on the fourth. Users quickly learn they're talking to a keyword matcher dressed as an assistant and stop using it. We build AI-powered chatbots on RAG architectures grounded in your actual knowledge base — so the bot answers from your documentation, your product data, and your support history, with citations the user can verify. We also design the escalation path: when the bot doesn't know, it routes to a human with the conversation context pre-loaded, rather than starting over. The result is a chatbot your customers actually use instead of immediately clicking 'talk to an agent'.
What's included
- RAG-powered knowledge base (your content, not generic training)
- Multi-turn conversational context management
- Live agent handoff with conversation transfer
- Multilingual support
- CRM & helpdesk integration (Zendesk, Freshdesk, HubSpot)
- Analytics: resolution rate, escalation rate, CSAT
How we deliver
- 1Chatbot use case & knowledge base scoping
- 2RAG architecture & vector store implementation
- 3Conversational flow design & system prompts
- 4Web widget or in-app integration
- 5Live agent handoff integration
- 6Analytics dashboard & performance reporting
Technologies we use
- OpenAI API
- Anthropic Claude
- LangChain
- LangGraph
- Pinecone
- pgvector
- Zendesk
- Freshdesk
- HubSpot
- Twilio
- Next.js
- Vercel AI SDK
Why Origin for AI Chatbot & Virtual Assistant Development
RAG grounded in your content — not general model knowledge
Every chatbot we build answers from your knowledge base with citations. When it doesn't know, it says so and routes to a human — not makes something up.
Live agent handoff with conversation context
We design the escalation path as carefully as the resolution path. When the bot can't help, agents receive the full conversation context — not a customer starting over.
Containment analytics from day one
We instrument resolution rate, escalation rate, and CSAT by topic from launch. Knowledge gaps are visible in the dashboard — we close them iteratively, not quarterly.
Industries we serve
“Our first chatbot had a 12% containment rate — basically nobody used it. Origin rebuilt it with RAG on our knowledge base and proper handoff design. Containment is now 71% and CSAT on bot-handled tickets is higher than agent-handled ones.”
Frequently asked questions
- What's the difference between your AI chatbot and tools like Intercom Fin or Zendesk AI?
- Off-the-shelf AI chat tools are good for standard support use cases where your knowledge base maps cleanly to structured help documentation. Custom-built chatbots are right when: your knowledge lives in non-standard formats (PDFs, internal wikis, ERP data, product catalogues), you need the bot to take actions (look up an order, raise a ticket, update a record), you have domain-specific terminology the general models don't understand, or you need tighter cost control than per-seat SaaS pricing allows at scale. We help you evaluate build vs. buy before recommending a custom solution.
- How do you ensure the chatbot answers from our content, not from the model's general knowledge?
- With RAG and system prompt constraints working together. RAG retrieves relevant documents from your knowledge base and includes them in the context window. The system prompt explicitly instructs the model to answer only from the provided context and to acknowledge when a question is outside it. We test this boundary extensively — asking questions the chatbot should answer, questions it should decline, and adversarial questions designed to get it to produce off-topic responses. All three behaviours are validated before launch.
- How does live agent handoff work?
- When the bot can't resolve an issue (below confidence threshold, customer requests human, or topic is flagged for human handling), it initiates a handoff to your live agent platform. The full conversation history transfers to the agent so they don't ask the customer to repeat themselves. We integrate with Zendesk, Freshdesk, Intercom, or any platform with a handoff API. The handoff is designed to be seamless from the customer's perspective — the transition is immediate and the agent has full context.
- Can the chatbot take actions — look up orders, update records, process requests?
- Yes — this is what separates a conversational interface from a search interface. We implement tool use (function calling) so the bot can query your database, look up order status via API, create support tickets, or trigger workflows when the user's intent requires it. Each tool is scoped precisely — the bot can retrieve an order but not refund it without human confirmation, for example. The action scope is defined explicitly in the design phase and implemented with appropriate authorization checks.
- How do you measure whether the chatbot is actually working?
- Containment rate (what percentage of conversations the bot resolves without human escalation), first-response resolution rate, CSAT on bot-handled conversations versus agent-handled conversations, and escalation rate by topic (which topics the bot consistently fails on). We build analytics into every deployment and review the metrics weekly in the first month. Topics with high escalation rates are knowledge gaps — we close them iteratively.