# The Design of an LLM-powered Unstructured Analytics System

arXiv: https://arxiv.org/abs/2409.00847 · HTML: https://mlsanity.com/paper/2409.00847/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 1 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction](https://mlsanity.com/paper/2304.11015/) (2023)
- [Can Foundation Models Wrangle Your Data?](https://mlsanity.com/paper/2205.09911/) (2022)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes](https://mlsanity.com/paper/2304.09433/) (2023)
- [TableFormer: Table Structure Understanding with Transformers](https://mlsanity.com/paper/2203.01017/) (2022)
- [A Declarative System for Optimizing AI Workloads](https://mlsanity.com/paper/2405.14696/) (2024)
- [Semantic Operators: A Declarative Model for Rich, AI-based Data Processing](https://mlsanity.com/paper/2407.11418/) (2024)
- [CHORUS: Foundation Models for Unified Data Discovery and Exploration](https://mlsanity.com/paper/2306.09610/) (2023)
- [CAESURA: Language Models as Multi-Modal Query Planners](https://mlsanity.com/paper/2308.03424/) (2023)

## Built on by (most cited first, 20 of 20)

- [DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing](https://mlsanity.com/paper/2410.12189/) (2024)
- [In-depth Analysis of Graph-based RAG in a Unified Framework](https://mlsanity.com/paper/2503.04338/) (2025)
- [Memory in the LLM Era: Modular Architectures and Strategies in a Unified Framework](https://mlsanity.com/paper/2604.01707/) (2026)
- [Cortex AISQL: A Production SQL Engine for Unstructured Data](https://mlsanity.com/paper/2511.07663/) (2025)
- [Cut Costs, Not Accuracy: LLM-Powered Data Processing with Guarantees](https://mlsanity.com/paper/2509.02896/) (2025)
- [Towards Resource-Efficient Compound AI Systems](https://mlsanity.com/paper/2501.16634/) (2025)
- [CoddLLM: Empowering Large Language Models for Data Analytics](https://mlsanity.com/paper/2502.00329/) (2025)
- [Beyond Relational: Semantic-Aware Multi-Modal Analytics with LLM-Native Query Optimization](https://mlsanity.com/paper/2511.19830/) (2025)
- [Cracking Vector Search Indexes](https://mlsanity.com/paper/2503.01823/) (2025)
- [A Case for Computing on Unstructured Data](https://mlsanity.com/paper/2509.14601/) (2025)
- [LLM-Powered Proactive Data Systems](https://mlsanity.com/paper/2502.13016/) (2025)
- [GraphSeek: Next-Generation Graph Analytics with LLMs](https://mlsanity.com/paper/2602.11052/) (2026)
- [Beyond Linear LLM Invocation: An Efficient and Effective Semantic Filter Paradigm](https://mlsanity.com/paper/2603.04799/) (2026)
- [GUIDE: Governed Unified Intelligence for Document-to-Artifact Generation in Enterprise Settings](https://mlsanity.com/paper/2608.12133/) (2026)
- [SAGE: Governed Artifact Generation from Enterprise Guidelines](https://mlsanity.com/paper/2609.17775/) (2026)
- [Semantic Integrity Constraints: Declarative Guardrails for AI-Augmented Data Processing Systems](https://mlsanity.com/paper/2503.00600/) (2025)
- [From Unstructured Communication to Intelligent RAG: Multi-Agent Automation for Supply Chain Knowledge Bases](https://mlsanity.com/paper/2506.17484/) (2025)
- [Evergreen: Efficient Claim Verification for Semantic Aggregates](https://mlsanity.com/paper/2604.26180/) (2026)
- [Efficiently Linking Unstructured Data for Multi-step Reasoning](https://mlsanity.com/paper/2609.19491/) (2026)
- [KathDB-FAO: Synthesized Query Plans in a Multimodal DBMS](https://mlsanity.com/paper/2609.28761/) (2026)

## Abstract (arXiv)

LLMs demonstrate an uncanny ability to process unstructured data, and as such, have the potential to go beyond search and run complex, semantic analyses at scale. We describe the design of an unstructured analytics system, Aryn, and the tenets and use cases that motivate its design. With Aryn, users specify queries in natural language and the system automatically determines a semantic plan and executes it to compute an answer from a large collection of unstructured documents. At the core of Aryn is Sycamore, a declarative document processing engine, that provides a reliable distributed abstraction called DocSets. Sycamore allows users to analyze, enrich, and transform complex documents at scale. Aryn includes Luna, a query planner that translates natural language queries to Sycamore scripts, and DocParse, which takes raw PDFs and document images, and converts them to DocSets for downstream processing. We show how these pieces come together to achieve better accuracy than RAG on analytics queries over real world reports from the National Transportation Safety Board (NTSB). Also, given current limitations of LLMs, we argue that an analytics system must provide explainability to be practical, and show how Aryn's user interface does this to help build trust.