The Most Reliable LLM for Diligence on High-Stakes Deals
Dili (YC S23) is building the most reliable AI workforce for diligence on high-stakes deals. Across tax credit diligence, real estate loan & lease abstraction, private equity and private credit, Dili has run diligence for leading firms on 3000+ high-stakes deals to provide instant diligence reports and red flags. today’s AI models are not accurate enough for mission-critical use cases like underwriting $60M+ tax credit transactions, $100M+ loan & lease abstraction, deal screening and drafting investment memos. Dili is built to be the most reliable automated platform that provides confidence scores on every output so you know when something needs your attention. Firms can now tune Dili using their domain-specific knowledge and instructions to fit their custom templates, reports and SOPs. Dili has found and fixed critical red flags and human errors on several multi-million dollar transactions using this fine-tuned expertise.
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Anand Chaturvedi, Co-Founder, CEO
Anand Chaturvedi is the Co-Founder & CEO of Dili working in New York, NY. Anand led engagement growth at Coinbase (S12) to increase revenue by $50M, and built out KYC/AML systems. Previously, Anand has published ML research at Apple, working on the Apple Vision Pro headset & other new products. Anand is a fellow at Kleiner Perkins, and holds a Bachelor's & Master's in Machine Learning from Georgia Tech.
Brian Fernandez
Co-founder and CTO of Dili (YC S23), building the AI copilot for capital markets due diligence. Former Software Engineer at Coinbase. Passionate about 1) leveraging AI and LLMs to transform enterprise software and 2) creating quirky AI-art mashups.
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Hi everyone, we are Brian and Anand. We had a big month at Dili with +210% revenue growth MoM, and we wanted to publicly share how Dili can improve underwriting and investment outcomes for our customers.
🧨 The Problem: Due Diligence is time-consuming, prone to human error, and today’s AI is not equipped to handle these mission-critical workflows
Due diligence today can mean spending weeks digging up data in datarooms using your domain-specific knowledge and presenting findings in memos to make multi-million-dollar investment decisions. All of this work is manual, time-consuming, and very often prone to human error. There are several cases where it is not feasible to have a human sift through 100’s of documents to find the red flag buried deep in the data room. Today’s AI models are not accurate enough to assist with mission-critical use cases like underwriting $60M+ tax credit transactions, $100M+ loan & lease abstraction, deal screening, and drafting investment memos.
🧿 The Solution: Dili’s AI Platform reads every file to generate relevant reports - with confidence scores.
Dili is built to be the most reliable platform for automating diligence and portfolio management workflows. It can read every file in a dataroom and provide reports with confidence scores on every output so you know when something needs your attention. Using the in-app file viewer, you can inspect sources directly with text highlighted.
Firms can now tune Dili using their domain knowledge and instructions to fit their custom templates, reports, and SOPs. Using this fine-tuned expertise, Dili has found and fixed critical red flags and human errors on several multi-million dollar transactions across 3000+ deals.
Dili has been used to automate:
✅ Tax credit diligence to validate credits and underwrite risk on transactions
✅ Real estate OM screening, loan, and lease abstraction
✅ Private Equity and Private Credit deal screening and DD checklists
✅ VC portfolio monitoring and drafting investment memos
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🧿 Dili: Accelerating due diligence with AI
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