From Product Zero to Agentic Chat: A Five-Year Build

From Product Zero to Agentic Chat: A Five-Year Build

The Challenge

The client set out to build a content intelligence platform that could deliver real-time, context-aware answers drawn from diverse data sources. Early retrieval systems were too slow, with response times stretching to tens of seconds. This created a poor user experience and added significant computational load. As the product matured, the client needed to evolve from static website tools into a conversational, AI-driven platform.

Hypothesis

An agentic conversational system powered by Retrieval-Augmented Generation (RAG) could overcome existing latency and accuracy challenges while offering personalization through continuous learning. By integrating multi-agent coordination, contextual retrieval, and user profiling, the platform could evolve into an intelligent copilot that guides discovery, recommends content, and builds engagement across the buyer journey.

Dataiku Workflow Solution

Tatras worked with the client through every phase of development over five years:
  • Full Platform Build: Designed and developed the complete backend and front-end infrastructure.
  • Generation Model Development: Built and deployed the core generation model powering product features.
  • ChatFactory Agent: Created an LLM-based conversational assistant that enables natural Q&A, content recommendations, and personalized microsites.

Outcomes

The updated approach significantly improved citation relevance and accuracy, leading to more trustworthy AI systems, higher quality of RAG-based answers, and easier validation by end users within an explainable AI question answering framework.

Project Highlights

80%

reduction in manual effort
through payment process
automation in Dataiku.

90%

decrease in error-prone calculations, significantly improving accuracy, reliability, and error reduction in financial workflows.