Enjins ads

Every investor, including PE and VC, is currently figuring out: “𝗛𝗼𝘄 𝘁𝗼 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮𝗻 𝗔𝗜-𝗻𝗮𝘁𝗶𝘃𝗲 𝗳𝘂𝗻𝗱?” Although using AI tools such as Claude or ChatGPT can be great as a starting point, especially for personal or small team workflows and a bottom-up approach, a more holistic approach is needed to really move towards team-wide adoption of the 20+ key agentic workflows. This is why we are building tailored AI-native investment platforms for PE and VC investors, with a multi-agent core that serves as the foundation, the knowledge stays in your operational systems, and workflows tailored to your fund’s way of working and investment thesis. That’s how the different deal stages (sourcing, Due Diligence, LP reporting, etc.) become AI-native one by one, separating judgment from data gathering, combining, and visualizing. Want a sneak peek? Check the demo below 👇 P.S. If you’re wondering why this video looks so great, check out the brag skill on github. Feel free to reach out if you want to know more.

Adopting a new technology like AI comes with a real learning curve, and there are plenty of ways to get it wrong along the way. You learn and correct, but all of that costs time you don't always have. Shypple's CPO Patrick Weissert recognized this early on. Rather than navigating every pitfall internally, he brought in a team that had already been through it many times before. As he put it, working with someone who has done it many times can simply save a lot of time compared to figuring it out from scratch. That decision paid off. Within nine months, Shypple had a production-grade AI foundation in place, without spending months on failed experiments to get there. 👉 Read the full case: https://lnkd.in/eDNhiFru

Shypple's operations team used to process thousands of shipment documents every day, all manually. Each one had to be uploaded, assigned to the correct shipment, and checked by hand. With AI now handling that workflow, the process runs 80% faster. Documents are automatically extracted and assigned to the right shipment, and the operations team's role has shifted from manual data entry to reviewing and approving what the AI produces. It's a human-in-the-loop setup: AI handles the heavy lifting of extraction and assignment, while people keep final control over what goes through. 👉 Read the full case: https://lnkd.in/eresURQZ

Logistics runs on communication. At Shypple, that means emails, attachments, and shipment documents moving back and forth across dozens of stakeholders, carriers, agents, and consignees, every single day. Doing that manually was never going to scale. Operations teams were uploading documents by hand and matching them to the right shipment file one by one, which is exactly the kind of repetitive work that eats into capacity as volume grows. So Shypple automated it. Incoming emails and shipment documents now flow through an AI pipeline that extracts the relevant data, assigns it to the correct shipment, and routes it onward, with operations staff reviewing the output instead of doing the manual matching themselves. The result was an 80% improvement in processing speed, freeing up significant time that used to go into manual document handling and leaving one less bottleneck between a shipment and the people tracking it. 👉 Read the full case: https://lnkd.in/emZHhCsM

In nine months, Shypple moved from AI ambition to AI in production, and it came down to three concrete outcomes rather than just one. First: a scalable production infrastructure for training and improving AI models. Robust, reliable, and built to grow. Second: automated email and document-based workflows that previously required manual handling at every step. Third: a trained internal AI team with the competence and DNA to continue scaling independently. Infrastructure, use cases, and a team: that combination is what actually makes AI adoption stick, rather than leaving a company with just another prototype. 👉 Read the full case: https://lnkd.in/eHcDU584

Turn your AI use case into a working prototype in 3 weeks.