AI Architecture / Web-Based Conversational AI
AI Voice Agents for the Web — Grounded in Live, User-Specific Data
A useful web voice agent is not a chatbot with audio attached. It must retrieve the right data for the person using it, in real time, and answer from trusted systems rather than a static script.
Web-only voice agents for real self-service.
This service is for voice agents embedded in a website or web application. The architecture centres on real-time RAG, user-specific retrieval, function calling, secure session context, and integration with backend systems of record so the agent can give accurate, personal answers within a controlled web experience.
Retrieval-grounded answers
Design retrieval flows that fetch the right live records for the active user, rather than asking a model to improvise from generic knowledge.
Backend integration
Connect the agent to APIs, databases, identity, entitlement checks, workflow actions, and audit trails so it behaves like part of the application.
Accessible design
Shape the experience for users who may struggle with conventional forms or navigation, while retaining transparency, fallback paths, and control.
Anonymised architecture evidence.
For a large public-sector data platform, designed a RAG architecture for a conversational AI system supporting users who could not access standard digital channels. Each session triggered real-time retrieval of that specific person’s records from the backend system, grounding responses in live, user-specific data. The same architecture pattern applies directly to web-embedded voice agents.
When this fits.
This fits organisations that want a web voice agent grounded in their own live systems. It pairs with AI architecture and SaaS and internal tooling when the agent is part of a broader web product or self-service portal.