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The deals that didn't close did still cost you. You just never counted them properly. It wasn’t just the fees. It was the analyst hours that went into a target that was never going to pass IC. The partner time spent building a relationship with a deal that was always going to die. The new opportunities that didn't get looked at because the pipeline was full of deals that were still technically alive. That's the real cost of late qualification. And it shows up nowhere on a P&L! Most deal teams do know this instinctively. They've felt it. The deal that ran six months too long. The one where everyone quietly knew by month two but the process had its own momentum by then. The red flag that got rationalised because a relationship had already been built The NDA that created commitment before there was ever real conviction This morning Jonas Moortgat walked through what changes when you move qualification to the front. Not a new process. Not more headcount. Just asking the right questions before a partner has spent time on it, before an NDA has been signed, before the relationship makes it hard to walk away. ⚡ The signals were there from day one. This webinar recap shows you exactly where to look. Watch the full session. Link in the comments 👇 https://bit.ly/3RNj348

One year on. What did the PE forecasts get wrong? Professor Sophie Manigart returns to openthebox with a data-led verdict on 2025 and a clear-eyed outlook for the year ahead.

99 family offices. €43 billion. Now mapped. openthebox releases its first Belgian Family Office Monitor. Join Sophie Manigart for the full 2026 PE market outlook.

💡 Something I’ve learned in Customer Success at openthebox: After onboarding, once teams are actively sourcing and screening companies, a pattern shows up quickly. Here's how it goes: Before openthebox many users feel they already “know” their market, customer, prospects.... They recognize the obvious companies. The familiar structures. The usual players. But partial visibility creates false confidence. So often, questions surface: ❓ Are we really seeing the full opportunity set? ❓ What sits outside the standard classifications? ❓ How exposed are we to blind spots we didn’t know existed? When users gain broader market visibility with openthebox, I see a real shift. ✅ Sourcing deals, prospects and company data becomes less reactive. ✅ Screening companies becomes more consistent across teams. ✅ Decisions move faster, with fewer revisits and fewer doubts after the fact. One user's quote captures it all: "Current tools don't provide deep enough analysis for acquisition targets, but with openthebox I get more access to company information than other ways of sourcing deals. It saves hours of work also." From a Customer Success perspective, that’s the real impact: actually seeing the full market doesn’t just support decisions, it changes how teams make them. 👇 Try searching a company yourself in the comments

We built openthebox to eliminate the operational overhead that shouldn't exist in deal workflows. 75% reduction in research time isn't about working faster, it's about removing the manual data operations (exports, reconciliation, context-switching) that don't scale. Days to hours on screening. Real-time notifications on your criteria. Zero spreadsheet archaeology. The technical infrastructure should be invisible. The analysis shouldn't be. Message after the beep... I mean, link in comments 🫠

👀 Here's my 2 cents on the current AI-craze in our ICP, from an openthebox' Customer Success POV: AI ambition in financial services isn’t the issue anymore. Execution of that same ambition is. What I hear from openthebox users: “We’re investing heavily in AI.” “We have pilots running.” “...But to be honest it’s not really changing how decisions are made yet.” That gap shows up again and again. Fragmented data. Unclear ownership. Skill gaps between teams. And suddenly AI confidence depends less on the model and more on: Repeatability Explainability Trust after go-live Our AI Readiness Survey, based on insights from senior decision-makers across Belgium and the wider Benelux, confirms this pattern. Very few organizations have truly embedded AI into day-to-day decision-making. That’s exactly why we pulled everything together in the AI Readiness Report, with concrete benchmarks across data readiness, governance, skills, and execution. If you want an honest view of where your organization stands (and what actually separates AI-leaders from laggards), this will help. 👉 Find the AI Readiness Report in the comments