4️⃣AI Video Avatar & Chatbot Overview

Here’s how our chatbot and AI video avatar are trained.

Data Collection: We begin with a library of high-quality recordings of your agents, capturing voice, facial expressions, and gestures.
Voice Model Fine-Tuning: A neural text-to-speech engine is adapted to each agent’s tone and cadence using at least 10 minutes of clean, transcribed audio.
Facial Synthesis: A facial-animation network learns lip-sync and micro-expressions from the recorded footage, enabling the avatar to match speech with natural head and eye movement.
On-Demand Rendering: When a visitor arrives, the system dynamically inserts their name and property details into a script template. The fine-tuned TTS and animation models then generate a 15-second greeting, ready in under two seconds.

Pretraining: We start with a state-of-the-art language model (e.g. GPT-style) trained on billions of tokens across web and real-estate forums to build broad conversational fluency.
Domain Fine-Tuning: Next, we fine-tune on a curated corpus of real-estate dialogues—scripts, FAQs, and support transcripts—so the bot masters property terminology, market norms, and cultural nuances (e.g., Feng Shui concerns).
Intent & Entity Supervision: A smaller BERT-based classifier is trained on 5,000+ labelled examples to recognise visitor intents (PriceInquiry, FeatureRequest, NextSteps) and extract key entities (names, addresses, dates, cultural keywords) with > 95% accuracy.
Retrieval Augmentation: We index your property database and local-market guides in a hybrid SQL + vector store. At runtime, the chatbot retrieves precise facts (availability, price, specs) or context-rich passages (neighbourhood insights) to ground its responses.

Continuous Learning: Every live conversation is logged (utterance, bot reply, lead rating). Quarterly, we retrain the intent classifier and fine-tune the response model on the highest-value transcripts to boost accuracy and relevance over time.

Stage-by-Stage Breakdown

Video Transcript Ingestion A speech-to-text engine produces a fully timestamped transcript of the AI avatar’s welcome video.
Entity Extraction A transformer-based named-entity recogniser tags visitor names, property features, dates and cultural terms.
Intent Classification A fine-tuned BERT model classifies the visitor’s query into one of the predefined intents.
Knowledge-Base Retrieval A hybrid SQL + vector search returns exact facts or context passages matching the intent.
Response Generation A custom LLM assembles a prompt: combining system instructions, recent context and retrieved facts, then generates the reply.
Safety & Consistency Filtering Draft replies pass through fact-checks, brand-voice enforcement and policy filters to eliminate errors or off-brand language.
Context Update The final exchange is logged into session memory, so follow-ups remain grounded in the visitor’s journey, looping back to intent classification for the next input.

Last updated 1 month ago