Practical guides for mid-market companies and the firms that back them. Start with the company ontology, then go deep on the part you care about.
The idea behind Palantir's work with large enterprises, explained for the middle market. What a company ontology holds, why it does more than another dashboard or a data warehouse, and how agents and apps run on it so leaders can make better decisions.
Getting AI into daily use takes engineering work inside the business. This guide explains what forward-deployed engineers do, how an engagement works, and when the model makes sense for a mid-market company.
Palantir's model works. Its pricing is designed for governments and the Fortune 100. Here is what a company with $20 million to $500 million in revenue can buy instead, what each option includes, and how to choose.
Most AI sold to investment firms is a research copilot. The firms getting real value run AI on their own deals, relationships and portfolio data, then carry the same model into the companies they own.
Diligence and the IC memo are one workflow with one job: a decision the committee can defend. This guide covers what AI does at each stage, why the memo should be drafted from your own deal record, and why that record should follow the company after close.
The best portfolio monitoring starts inside the company. When each portfolio company runs on its own connected data, its managers get a reliable read every week, and the sponsor sees variance against plan without asking for it.
Most firms either mandate AI everywhere at once or leave each company to work it out alone. The rollouts that work set shared rules, improve one decision at one company, then carry what worked to the next.
Most regulated companies can adopt AI meeting transcription safely if they settle four things first: consent, which meetings are off limits, the vendor's security, and how long transcripts are kept. Here are the rules that apply, the checklist to run any tool through, and what investment firms need to add.