Projects
TrustAnalytica
LLM Fine Tuning | Data Cleaning | Open AI Integration
TrustAnalytica — AI-Powered Review Analytics & Content Generation
TrustAnalytica is an AI-powered review intelligence system built to analyze Google reviews at scale and automatically generate human-like summaries, ranking justifications, and company profile descriptions. The platform ingests raw customer feedback, processes it through fine-tuned LLMs, and produces natural, SEO-friendly content for thousands of business listings, improving credibility, readability, and search discoverability.
Challenge
Role
LLM Pipeline Engineer responsible for training and developing three fine-tuned models — each targeted to a different narrative output (company description, ranking explanation, review summary). I handled dataset preparation, model refinement, and integration logic for scaling to thousands of pages.
Project Goals
- Automate Content for Business Listings – Remove dependency on manual content writers for business profile descriptions.
- Summarize Customer Reviews at Scale – Convert raw Google reviews into structured narratives that highlight customer sentiments.
- Explain Why a Business Ranks in Top 10 – Provide human-style justification for rankings based on real review data.
- Generate Unique SEO Content for Thousands of Pages – Improve search presence using dynamic AI content.
Development Process
Data Collection | Data Pre-processing | LLM Fine-tuning
- Review Data Extraction with Selenium – Automated scraping across multiple business pages.
- Dataset Structuring & Labeling – Cleaned using Pandas for consistent text patterns.
- Fine-Tuned Model Training – Three models trained separately for three output styles.
- Controlled Prompt Logic – Built strict formatting to maintain SEO and readability standards.
- Scalability for High Volume Pages – Designed logic to automate content generation at scale.
- Integration with Web Rendering – AI outputs embedded directly on public profile pages.
Final Outcome
The system uses three fine‑tuned LLM models: one generates human‑tone company profiles, another produces ranking‑justification narratives using sentiment‑based evidence, and a third creates natural, balanced review summaries. An automated data pipeline continuously scrapes and preprocesses Google reviews before feeding them into the models. A scalable content deployment system then distributes the generated outputs across thousands of live webpages, ensuring consistent, high‑quality information delivery at scale.