mlsanityData to 13 Oct 2026

eSapiens: A Platform for Secure and Auditable Retrieval-Augmented Generation

arXiv 2507.09588 ↗First posted 13 Jul 2025cs.AIcited by 1

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

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,221
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,117
  3. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,313
  4. 2020Distilling Knowledge from Reader to Retriever for Question Answeringcited by 226

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2025eSapiens: A Real-World NLP Framework for Multimodal Document Understanding and Enterprise Knowledge Processing
  2. 2026Securing the Agent: Vendor-Neutral, Multitenant Enterprise Retrieval and Tool Use
  3. 2025eSapiens's DEREK Module: Deep Extraction & Reasoning Engine for Knowledge with LLMs
  4. 2025Retrieval Augmented Generation (RAG) for Fintech: Agentic Design and Evaluation
  5. 2026Navigating Global AI Regulation: A Multi-Jurisdictional Retrieval-Augmented Generation System
  6. 2025LLM and Agent-Driven Data Analysis: A Systematic Approach for Enterprise Applications and System-level Deployment
  7. 2026AgenticRAG: Agentic Retrieval for Enterprise Knowledge Bases
  8. 2024RAG Does Not Work for Enterprises