James Governor and Alois Reitbauer on Building vs Buying Enterprise Software

This one hits close to home. Behind every new menu added, every menu update, every taxonomy fix, and every data correction, there's a team of content specialists who rarely get the spotlight they deserve. Noha Abozeid, Mohamed Hassan Fouad, and the entire content operations crew across 8 MENA markets, this milestone belongs to you too. You've been navigating fragmented menus and calaouges, inconsistent structures, and market-by-market complexity long before it made it to an engineering blog. Your resilience and ownership made us ready for this. And to the people who made sure the operational side had a seat at the product and tech table, Milos Marinkovic and Bakri AlBakri, thank you. This is what real cross-functional collaboration looks like: tech and operations moving together, not in parallel. When you manage content at scale across many GCC countries, Egypt, Iraq, and Jordan, you feel every taxonomy decision in your queues, your SLAs, and your partner experience. Seeing it solved at a global level is exactly the kind of progress that keeps the team motivated. 💪 Here's to fewer workarounds and more wins. 🚀

Intelligence is only as good as the foundation it’s built on—check out how we rebuilt the foundation and are scaling the Menu Intelligence™ to 65+ global markets at Delivery Hero . Read the full story on our tech blog:

How do you manage 4 million daily menu changes across 65+ global markets? 🍽️ Here is the challenge: seven separate food catalogs, each with its own taxonomy, database design, and business processes. This fragmentation cost us millions of lost menu updates and inconsistent partner experiences. In our latest engineering deep dive, Senior Engineering Manager Milos Marinkovic explains how we rebuilt our tech stack to: ⚡ Reduce app load times from 15 secs to just 2.5 secs. 📌 Eliminate the "4 million lost changes" per year. 🤖 Deploy Menu Intelligence™, an AI suite that digitizes physical menus and suggests optimal pricing. Read the full story on our tech blog: https://bit.ly/4vWDQBY

Great evening at the RavenPack Big Data Developer Meetup in London. Really enjoyed the hands-on workshop on building research agents for financial sector that actually scale, along with the live architecture walkthroughs — seeing how these systems are designed in practice was a standout. A lot of useful insights around production-ready agent design, deployment, and avoiding common pitfalls. Big takeaway: it’s one thing to build AI agents but it’s another to make them reliable and scalable in production. Huge thanks to the RavenPack team for hosting. #DataEngineering #AI #MachineLearning #FinTech #LLM #Agents #LondonTech #BigData #RavenPack

GoodData has been shipping headless, API-first, multi-tenant #EmbeddedAnalytics for years, which is exactly the architecture an ISV needs to roll out #AgenticAI across hundreds of customer environments without rebuilding an agent each time. That was the lens I brought to Eric Avidon's Informa TechTarget piece on the Agent Builder launch. Their #SemanticLayer has been running in production for nearly two decades, and GoodData's two-tier pricing with unlimited users keeps the per-agent math from breaking at scale. Link in comments. Roman Stanek Peter Fedoročko Natalia Nanistova

🚀 Excited to share that the Blix API is now live in Beta! Open-ended feedback is incredibly valuable, but it’s still too often analyzed manually, slowly, and outside the systems where teams actually work. We’re excited to start changing that. With the new Blix API, open-ended feedback can become much more operational. Early customers are already using it to: → Run automated tracker studies continuously → Update dashboards live from text feedback → Integrate Blix’s AI text analysis into survey software → Set up real-time alerts based on open-ended responses We’re still in Beta, but excited to start opening this up to more teams. Interested in testing the API? Let us know!

Really excited about this one! We just launched the Blix Text Analysis API. Over the last year, one thing became very clear: teams don’t just want AI text analysis and coding tools, they want text analysis embedded directly into their existing workflows. Survey platforms. Brand trackers. Live dashboards. Textual feedback mostly lives outside operational workflows because someone has to manually read and code it. Now it can become useful in many new ways: --> Large tracking studies automatically coded --> Live dashboards based on open-ended feedback --> Automated alerts for urgent customer issues hidden inside text responses It’s still early, but our first partners are already showing us what becomes possible when text analysis is embedded directly into existing workflows. I’m sure this is just the start. Excited to keep building.

I built the first version of Accountech4U on Lovable and Emergent. Fast to start. Genuinely impressive tools. But here is what they do not tell you. Once you are embedded — once your codebase lives in their platform, your workflows are wired to their AI calls, your users are signed up — the pricing changes. Suddenly you are paying per token, per API call, per output. The platform is "free." The usage is not. It is a smart business model. Hook on simplicity. Monetize on dependency. I figured this out later than I should have. So I changed the approach. Found ways to manage token consumption properly. Shifted the architecture so the compute I was paying for was actual work — not overhead the platform was generating on my behalf. Same output. Significantly lower cost. This is the part of the "build with AI" conversation that nobody posts about. Not the demos. Not the launch announcements. The quiet moment when you realize you traded launch speed for vendor lock-in. If you are building on AI-native or no-code platforms right now: understand the token cost model before you scale. What costs AED 200/month at 50 users can cost AED 8,000/month at 500 users on the same plan. Ask me how I restructured it.

Today we're happy to announce the release of QRData, a free to use QR code generator and data collection engine. More features coming soon, in addition to AI monitoring and report generation, API plugins for your KPI dashboards and management reports for those weekly standups. This micro saas helps you take the guess+work out of feedback data, so you can actually get insight from real world data and decide if or how to take action. QRData was built during the Replit 10-year anniversary buildathon alongside 1000s of other builders and has already garnered some nice attention from the crowd. To learn more, check the link below. Message us if you have any questions or want a personal demo 👍 Link to QRData: https://qrdata.org/

Long before Big Data was a buzzword, there was already a high-performance processor running the show. Think about it: Moms are the original predictive engines. They don’t need an algorithm to know when a crisis is brewing or to resolve the identity of the person who didn’t put the milk away. From managing unstructured human data to optimizing logistical chaos at scale, they’ve been delivering performance insights and resolving complex "household" IDs long before we had SaaS solutions for it. At Daasify, we build the tech that catalogs and predicts patterns, but we’re the first to admit we’re still playing catch-up to Mom’s intuition. To the original data scientists, the chaos-optimizers, and the world’s most reliable predictive processors: Happy Mother’s Day. #MothersDay #BigData #DataScience #Daasify #PredictiveAnalytics