Build an AI Academic Paper Summarizer
A pipeline that ingests academic papers in PDF format, extracts structured content, and generates concise summaries using LLMs with retrieval-augmented generation for accurate citation handling.
Document Ingestion & Parsing
Convert academic PDFs into clean, structured text preserving figures, tables, and references
Purpose-built for converting academic documents into structured, gen-AI-ready formats with layout-aware parsing of tables, figures, and citations
Specialized PDF-to-markdown transformer that handles complex academic layouts including multi-column text and mathematical notation
Lightweight option for converting PDFs and office documents to clean markdown when simpler parsing is sufficient
Indexing & Retrieval
Chunk parsed papers and build a vector index for section-level retrieval during summarization
Leading document agent platform with built-in PDF chunking strategies, citation tracking, and query engines tailored for document summarization workflows
Lightweight embedded vector database that pairs well with any chunking pipeline for fast local similarity search across paper sections
End-to-end RAG platform with deep document understanding and chunk-level citation grounding out of the box
LLM Orchestration & Summarization
Route parsed content through LLMs to generate structured summaries with key findings, methodology, and conclusions
Vercel AI SDK provides a unified TypeScript interface for streaming structured outputs from multiple LLM providers, ideal for generating typed summary objects with sections like abstract, methods, and findings
Mature framework with map-reduce and refine summarization chains purpose-built for long documents that exceed context windows
Programming-first approach lets you optimize summarization prompts automatically against quality metrics rather than hand-tuning them
Evaluation & Quality
Evaluate summary quality for faithfulness, coverage, and factual consistency against source papers
Purpose-built RAG evaluation framework that measures faithfulness, answer relevance, and context precision — directly applicable to checking if summaries stay true to source papers
Lets you systematically test summarization prompts across different papers and models with custom assertions for coverage and accuracy
Traces the full summarization pipeline with cost tracking and latency monitoring, useful for optimizing multi-step summarization workflows in production