Build an AI-Powered Review Analysis System
Collect, process, and analyze customer reviews at scale using AI for sentiment analysis, topic extraction, and actionable insight generation.
Data Collection & Ingestion
Crawl and ingest reviews from websites, APIs, and documents into a structured pipeline
LLM-friendly web crawler purpose-built for extracting structured text from review sites and forums at scale
Managed web data API that handles JavaScript-rendered review pages and outputs clean markdown for downstream AI processing
Converts PDF reports, survey exports, and other document formats containing reviews into LLM-ready text
AI Analysis & Reasoning
Run sentiment classification, topic extraction, and insight generation using LLM pipelines
Programmatic framework for building optimizable NLP pipelines—ideal for chaining sentiment scoring, aspect extraction, and summarization without brittle prompts
Mature agent engineering platform with built-in chains for document QA, classification, and structured output from review corpora
Ensures LLM outputs conform to typed schemas (e.g., sentiment labels, rating scores, topic lists) via structured extraction
Vector Storage & Retrieval
Store review embeddings for semantic search, clustering, and similarity-based trend detection
Lightweight embedded vector database perfect for storing review embeddings and enabling semantic similarity queries across thousands of reviews
High-performance vector database with advanced filtering—useful when review volume grows large and you need faceted search by product, date, or rating
LLM Gateway & Observability
Route AI requests across providers with cost tracking and monitor pipeline quality over time
Unified gateway to 100+ LLM APIs with spend tracking—lets you compare sentiment accuracy across models and optimize cost per review analyzed
Open-source LLM observability platform for tracing each review through the analysis pipeline, debugging low-confidence classifications, and tracking accuracy metrics
Dashboard & Interaction
Present analysis results through a conversational interface for exploring review insights
Build a conversational UI where stakeholders can ask natural-language questions about review trends, top complaints, and sentiment shifts over time
Self-hosted chat interface that can serve as an internal review intelligence assistant with multi-model support and conversation history