eSapiens: A Platform for Secure and Auditable Retrieval-Augmented Generation
On the map: Retrieval-augmented generation → Agentic RAG, agents and memory
Abstract (arXiv)
We present eSapiens, an AI-as-a-Service (AIaaS) platform engineered around a business-oriented trifecta: proprietary data, operational workflows, and any major agnostic Large Language Model (LLM). eSapiens gives businesses full control over their AI assets, keeping everything in-house for AI knowledge retention and data security. eSapiens AI Agents (Sapiens) empower your team by providing valuable insights and automating repetitive tasks, enabling them to focus on high-impact work and drive better business outcomes. The system integrates structured document ingestion, hybrid vector retrieval, and no-code orchestration via LangChain, and supports top LLMs including OpenAI, Claude, Gemini, and DeepSeek. A key component is the THOR Agent, which handles structured SQL-style queries and generates actionable insights over enterprise databases. To evaluate the system, we conduct two experiments. First, a retrieval benchmark on legal corpora reveals that a chunk size of 512 tokens yields the highest retrieval precision (Top-3 accuracy: 91.3%). Second, a generation quality test using TRACe metrics across five LLMs shows that eSapiens delivers more context-consistent outputs with up to a 23% improvement in factual alignment. These results demonstrate the effectiveness of eSapiens in enabling trustworthy, auditable AI workflows for high-stakes domains like legal and finance.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation. Builds on 4 of its 11 references that are in the map. First posted on arXiv 13 Jul 2025.
Builds on
4 of 11 references.
References · 4
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,221
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,117
- 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,313
- 2020Distilling Knowledge from Reader to Retriever for Question Answeringcited by 226
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Citing papers per year: 2025: 1.
1 paper.
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- 2025eSapiens: A Real-World NLP Framework for Multimodal Document Understanding and Enterprise Knowledge Processing
- 2026Securing the Agent: Vendor-Neutral, Multitenant Enterprise Retrieval and Tool Use
- 2025eSapiens's DEREK Module: Deep Extraction & Reasoning Engine for Knowledge with LLMs
- 2025Retrieval Augmented Generation (RAG) for Fintech: Agentic Design and Evaluation
- 2026Navigating Global AI Regulation: A Multi-Jurisdictional Retrieval-Augmented Generation System
- 2025LLM and Agent-Driven Data Analysis: A Systematic Approach for Enterprise Applications and System-level Deployment
- 2026AgenticRAG: Agentic Retrieval for Enterprise Knowledge Bases
- 2024RAG Does Not Work for Enterprises