At Open Core Ventures, our Ops team is a lean machine. As our portfolio grows, so does the operational complexity, and the only way to keep up is by scaling with smarter and more efficient assets. We’ve been building an AI-powered Ops stack that lets us do more with the same team, and we want to share what we’ve built.
Our stack starts with Gobii AI, an OCV portfolio company that lets anyone spin up virtual coworkers. Gobii tackles some of our core operational and data processes—no code, workflow mapping, or APIs needed. We simply create AI agents that are onboarded the same way a new remote hire would, by communicating with words. We give them access to the same internal systems and accounts a real employee would use, and teach them the workflows conversationally. If you can explain it in plain language, you can build it.
One hard requirement for anything in our Ops stack is self-hosting capability. We run Gobii on our own infrastructure, which means the data our agents consume and store stays fully within our control.
Portfolio data auditing with Gobii
The first Gobii agent we created automates our portfolio data audit process. While it’s not quite a full operations analyst yet (more like a really good intern), the results have been promising.
Pulse is OCV’s internal VC ecosystem that houses key information on our portfolio (investments, financial metrics, founder teams, etc.), including profiles for each of our portfolio companies. Keeping those profiles up-to-date and complete is critical to our ability to manage operations. Manually auditing our growing list of companies is tedious and time-consuming. So we gave it to a Gobii agent, aptly named Pulsey Jackson.
Before Pulsey could get started, it needed access to Pulse. Instead of using an API or giving it access to an existing user, I created a dedicated login for Pulsey and gave it the only permissions it needed to do its job, just like we would for any new employee.
Once Pulsey was connected, I asked it to log in, review every portfolio company profile, and flag any data fields that appeared incomplete. Pulsey compared each company profile against a fully filled-out model profile that I audited myself. Once it was finished, it sent me a PDF report, which alerted me that the monthly projected burn rate was missing from a significant number of companies, alongside the list of all missing fields and the corresponding profiles they were missing from.
A task that would have taken roughly two hours a week to manually review was handled in a single automated run. Nice job, Pulsey.
As our portfolio continues to grow, this kind of data hygiene would only get harder to maintain without automation. The next step is to train Pulse on the nuances of our portfolio. Ultimately, we expect it to audit, edit, input, and broadly manage the data integrity of Pulse with only the minimal required human oversight. After that, we can integrate more Gobii agents to work with Pulsey to monitor the portfolio, dissect financial information, and expand our dataset.