Avatar Chatbot: Eric AI for Network Marketing Pro
Chatbots
ai
Summary
Purpose
The client set out to create a conversational AI avatar capable of answering questions using private, internal knowledge, while remaining transparent, controllable, and easy to integrate into an existing product ecosystem. The goal was to deliver a production-quality Proof of Concept (PoC) demonstrating both technical feasibility and being ready for real-world user experience.
Methodology
We followed an iterative, delivery-first approach:
Rapid local prototyping to validate core assumptions
Early cloud deployment to uncover integration and latency issues
Multiple feedback and refinement cycles
Close collaboration with the client’s frontend and engineering teams
"Postdata Team helped us with an urgent project and ensured everything was delivered on time. We had a strict one-month deadline before our product release, and they managed the pressure perfectly.
They built an interactive chatbot that communicates with our clients and answers questions based on our internal database. The chatbot was carefully trained to stay polite, helpful, and strictly within our domain, avoiding any irrelevant or off-topic responses. It was delivered exactly on schedule and is already improving our customer experience.
Postdata Team also demonstrated strong expertise in visual GenAI technologies (such as D-iD and HeyGen), which was valuable in building an engaging and interactive solution.
Overall, we are very satisfied with their professionalism, technical skills, and ability to deliver under tight deadlines. Highly recommended." Network Marketing Pro
Project overview
Scope
Building an end-to-end backend system that would:
Retrieve answers from a private knowledge base using Retrieval-Augmented Generation (RAG)
Support multi-turn conversational context
Generate real-time talking avatar video responses
Deploy in a scalable, cloud-native environment
Integrate cleanly with the client’s frontend and internal systems
Document architectural decisions and future extensibility options
Ensure multilanguage support
We delivered a modular, cloud-deployed backend composed of two connected pipelines:
Conversational RAG Service
A FastAPI-based service that retrieves relevant content from a vector database, generates grounded answers using an LLM, and maintains short-term conversational memory.Video Avatar Generation Service.
A real-time avatar streaming integration that converts generated answers into lip-synced video using a third-party avatar provider.
The backend was deployed as a containerized service on Google Cloud Run, using automatic scaling to handle variable request load without requiring manual management. This ensured the PoC was not only functional, but also had production-ready architecture.
Results
Core Deliverables:
End-to-end conversational RAG backend: we delivered a fully functional backend capable of retrieving answers from a private knowledge base, maintaining short-term conversational context, and generating grounded, auditable responses using an LLM.
Real-time AI avatar video streaming: the system converts generated text answers into lip-synced video responses in real time, enabling a natural, engaging conversational experience for end users.
Cloud-native deployment and integration: the solution was deployed as a containerized service on Google Cloud Run with autoscaling enabled and integrated into the client’s system through well-defined HTTP APIs.
Data ingestion and indexing pipeline: a deterministic pipeline was implemented to clean, chunk, embed, and index client documents, ensuring reliable retrieval quality and repeatable updates.
Comprehensive documentation: we delivered detailed API references, deployment instructions, and operational documentation to support ongoing development and handover.
Additional Value Delivered:
Performance and cost optimization: we optimized LLM calls to reduce latency and control inference costs; we created internal endpoints for monitoring usage, controlling costs, and observing system performance.
Safety and quality of responses: prompt guardrails were introduced to define avatar behavior and ensure smooth user experience.
Extended capabilities and integration support: we added an LLM-based translation layer and worked closely with the client’s frontend team to ensure correct real-time streaming and end-to-end integration.
Insights & Conclusions
The project quickly delivered an AI avatar grounded in private knowledge without sacrificing architectural quality. In addition to meeting all technical objectives, the client successfully showcased the solution at a large public event with a high number of attendees, where the system ran live. Early cloud deployment, autoscaling infrastructure, and continuous feedback loops were critical to achieving this outcome within a short time.
Next steps
Following the successful PoC, the next phase will focus on introducing persistent conversation history, expanded multilingual support, automated re-indexing pipelines, and deeper RAG quality improvements such as enhanced multi-turn grounding and retrieval over chat history. Additional exploration may include evaluating alternative avatar providers and embedding models, selectively assessing the Vertex AI stack for potential performance or cost benefits, and extending interaction modalities with voice-based input.
Project duration:
4 weeks
Team
3
1 Lead Data Scientist, 1 Data Scientist, 1 Backend Developer
Technologies
Python, FastAPI, Gemini, Pinecone, HeyGen, Google Cloud Run
Tech challenge
Knowledge retrieval quality: we tuned chunking strategy, retrieval parameters, and prompt constraints limiting the LLM to retrieved context only.
End-to-end latency: optimized request orchestration and reduced token usage after early cloud deployment.
Multi-turn context management: we introduced conversation history summarization to maintain relevant context across turns while improving accuracy and token efficiency.
Cost control & observability: added usage and monitoring endpoints, optimized prompt length, and assisted the client team with request-limit management.
Avatar safety & brand alignment: ensured multiple prompt-guardrail iterations based on stakeholder feedback and real-world testing.
Frontend integration: provided clear API contracts, example payloads, and hands-on support for the client’s frontend team.
Solution
The project delivered a cloud-native, scalable AI avatar system that combines retrieval-augmented intelligence with real-time video generation. It established a robust foundation for future expansion into a production-grade platform.
