FindingtheRightDataforHigh-StakesAI
Agentoid finds exactly the data your agents need, anywhere in your organization.
Better Retrieval Than Vector Search
Agentoid's proprietary navigation tech parses your documents and uses AI agents with tool-calling to navigate enterprise knowledge more intelligently than traditional search.
“Revenue concentrated in three large accounts representing 60% of income…”
“The document was last reviewed and updated in March 2024…”
“A single supplier provides the majority of critical components…”
“Definitions and abbreviations appear in the appendix on page 24…”
“Two leadership transitions occurred during the past fiscal year…”
Agents that think, not lookups that guess.
Best-in-class retrieval. Each chunk gets reasoned over before it earns its place in the answer. Outperforms classical and contextual RAG.
Theoretically guaranteed privacy.
We've developed the first method to run agentic inference with theoretical privacy guarantees — without compromising the agent's accuracy. Best-in-class privacy, end to end.
⤷“…account opened for client 4291 on the regional ledger…”
⤷“reported growth of 12.4% year-over-year in Q3…”
⤷“…period ending September 30, 2024…”
⤷“status: confirmed by reviewer on close of period.”
Agents you can trust.
Every field of every output carries a citation back to source. The whole record is verifiable, not narrated.
Our core team of experts dedicated to your project.
Praneeth Vepakomma
FounderVisiting Asst. Prof. (MIT), Asst. Professor (MBZUAI). PhD from MIT. Previously at Meta, Apple, and Amazon. His CERT lab enables collaborative intelligence, focusing on the pillars of Collaboration, Efficiency, Responsibility, and Trust, with applications to legal, health, finance, and edge-device ecosystems. His lab works on modulated learning, data markets, agentic AI, differential privacy, split learning, and federated learning.
Rushil Thareja
Founding EngineerPhD student at MBZUAI; undergrad from IIIT Delhi. Research on private and secure AI agents. Primary author of DP-Fusion (ICLR) and MAC, a prompt optimiser. Previously a quant and data scientist.
Gautam Gupta
Founding EngineerBTech CS at IIIT Delhi. AI researcher working on LLMs, privacy, and interpretability. Co-authored MAC, Published at ACL 2026 Findings and GenAI4Health @ NeurIPS.
Stephanie Faasch
Legal InternJ.D. Candidate at UTulsa Law with a B.S. in Biomedical Chemistry. Experience spans immigration law, corporate compliance, technology commercialization, and legal operations across law firm and in-house legal environments. Skilled in legal writing, research, and issue analysis.
Komal Kumar
LLM Special Ops Engineer (Intern)PhD student at MBZUAI; Master’s from IIT Mandi. Researching efficient fine-tuning, post-training, and multi-agent systems. Previously at Jio Research Lab. MBZUAI Rising Star Awardee, with publications in top venues including CVPR, NeurIPS, ACL, ACM MM, WACV, MICCAI, and Cell Stem Cell.
Advisory Board
Alex Pentland
Advisory Board MemberFaculty lead for digital society at Stanford HAI and Digital Economy Lab, and Toshiba Professor at MIT. A member of the US National Academies, his lab’s spin-offs span digital identity, healthcare, financial technology, and AI. His current focus is using AI to improve our social institutions.
Munther Dahleh
Advisory Board MemberWilliam A. Coolidge Professor of EECS at MIT, member of LIDS, and founding director of IDSS (2015–2023). His research spans robust control, learning and decision-making in networked systems, and systemic risk. He is a four-time Axelby Outstanding Paper Award recipient and winner of the Donald P. Eckman Award.
Eugene Soltes
Advisory Board MemberProfessor of Business Administration at Harvard Business School and head of its Accounting and Management unit. His research on corporate integrity uses data analytics to find the cultures and compliance systems that prevent and detect misconduct. Author of the bestseller Why They Do It: Inside the Mind of the White-Collar Criminal.
