AI-Powered Review Analysis System
End-to-end pipeline for collecting, embedding, and analyzing customer reviews at scale using LLMs. Extracts sentiment, themes, and actionable insights from unstructured feedback across web sources and documents.
Data Ingestion & Preprocessing
Collect reviews from web sources and normalize document formats into clean, structured text for downstream analysis
Automates JavaScript-heavy review platforms (Amazon, Trustpilot, G2) that require interaction, form filling, or login to access full review content
Converts static review pages and sitemaps into LLM-ready markdown when sites don't require complex interaction
Parses review exports in PDF, Word, or PowerPoint formats from enterprise survey tools into structured markdown preserving tables and ratings
Vector Storage & AI Gateway
Unified LLM access with cost tracking and semantic storage for review embeddings to enable similarity search and clustering
Routes sentiment analysis requests across multiple providers (OpenAI, Anthropic, local models) with automatic failover and cost tracking per review batch
Lightweight vector database storing review embeddings for semantic similarity search, duplicate detection, and thematic clustering
Postgres extension option when reviews must coexist with existing relational transaction data in enterprise environments
Analysis & Insight Extraction
Agentic processing pipeline that extracts sentiment, entities, and themes from reviews using RAG and persistent memory
Document agent framework treating review collections as queryable knowledge bases for natural language Q&A (e.g., 'What do users hate about checkout?')
Universal memory layer preserving analysis context across review batches, enabling personalized insight filtering based on user roles (product vs. support teams)
Type-safe Python agents for structured extraction of star ratings, feature mentions, and urgency flags from review text
Workflow Orchestration
Automation layer scheduling review collection, batch processing, and alert generation without custom infrastructure code
Evaluation & Observability
Quality assurance for LLM outputs and production monitoring of analysis accuracy and costs
Red-teaming sentiment classification prompts against edge cases (sarcasm, mixed sentiment) to ensure consistent extraction before deployment
Evaluates RAG pipeline accuracy when retrieving similar past reviews to augment current analysis context
Production tracing of LLM calls with cost attribution per review batch, prompt versioning, and latency monitoring for the analysis pipeline
Delivery Interface
User-facing application layer exposing review insights via APIs and dashboards for business stakeholders