Trading Places is about information: who has it, who lacks it, who believes they control it, and who can act on it before anyone else understands what has happened. That isn't just commodities trading. That's venture capital, private equity, and M&A. In every transaction, information is leverage.

Practical term sheet guide for deal principals and deal teams: what key provisions actually control and where terms drift in drafting. Covers economics, governance, and rights with clear watch-outs—plus a glossary and review checklist.

Imagine you are an M&A associate preparing a comparison memo two hours before a signing call. The partner wants confirmation that the indemnification cap and survival periods in the final agreement reflect what was negotiated.

Using the Caterpillar–Siwei acquisition as a case study, this article examines why verification breaks down in complex M&A transactions when diligence relies on static snapshots instead of continuous alignment.

Most people in venture capital can recognize the terms inside a term sheet. Fewer people realize that understanding them is often what allows a dealmaker to push back when the drafting starts to move the deal.

Deal drift is the widening gap between what stakeholders believe the deal is and what drafts become. Learn how misalignment turns into structural risk before signing.

Most transaction data captures only the final agreement. But the real intelligence of a deal lies in the sequence of decisions that produced it. This article explores “deal memory,” the structured history of negotiations and revisions that reveals how judgment shaped a transaction, and why that history may become one of the most valuable datasets in AI-driven private markets.

Legal AI isn’t limited by model size. It’s limited by architecture. Learn why skills architecture, risk calibration, and citation discipline matter for defensible, transaction-ready AI outputs.

In M&A today, a simple belief is gaining momentum. Better data will produce better deals.

AI is clearly speeding up legal work, but this article argues that speed alone is not the right metric in transactional practice. It explores how acceleration can outpace verification, why traceability and defensibility matter more than ever, and how teams risk moving faster while becoming less able to show their work when conclusions are questioned later.

Aracor CEO Katya Fisher discussed AI's impact on dealmaking pace, precision, security, and compliance on TechTalk.

Aracor has won the Legal Insider Award 2025 in the category “Most Innovative AI-Native Dealmaking Platform.”

Aracor is built on the idea that true AI intelligence comes not from generic or synthetic data, but from verified, real-world dealmaking records — term sheets, redlines, diligence memos, agreements, risks, negotiations, and outcomes that reflect human judgment under pressure. By unifying documents, stakeholders, diligence, negotiation, verification, and oversight into one secure, continuously updated system, Aracor turns proprietary transaction data into living intelligence that strengthens over time, preserves institutional knowledge beyond closing, and helps legal and investment teams anticipate risk, maintain context, and make better decisions with every verified outcome.

AgentKit’s drag-and-drop workflow revives the debate in legal tech: should lawyers design flows or rely on systems that run them?

Prohibited, high-risk, obligations apply to providers and users. Aracor delivers compliance via secure, certified AI platforms.

How Aracor Uses Qdrant to Accelerate Legal Due Diligence Resulting in 90% Faster Workflows

𝗔𝗿𝗮𝗰𝗼𝗿 𝗶𝘀 𝘁𝗵𝗲 𝗼𝗻𝗹𝘆 𝗔𝗜-𝗻𝗮𝘁𝗶𝘃𝗲 𝗱𝗲𝗮𝗹𝗺𝗮𝗸𝗶𝗻𝗴 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝘁𝗵𝗮𝘁 𝗶𝗻𝗰𝗹𝘂𝗱𝗲𝘀 𝗮𝗻 𝗟𝗟𝗠-𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 𝗱𝗲𝘀𝗶𝗴𝗻.

How Katya Fisher Built the Legal Tech Platform She Always Needed