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Embed, don't build💡 Your engineers should be focused on your core product instead of building complex reporting modules. Join the market leaders using Flexmonster to provide real-time data visualization. It’s seamless, fast, and enterprise-ready from day one. Upgrade your platform today!

Shift from building custom reporting from scratch to delivering enterprise experience your clients expect If your platform sets the industry standard, your reporting needs to match. Leading software providers rely on Flexmonster for real-time data visualization, and seamless UI consistency.

🚀 𝐍𝐞𝐰 𝐟𝐫𝐨𝐦 𝐃𝐚𝐭𝐚𝐁𝐮𝐜𝐤: 𝐑𝐞𝐜𝐨𝐧𝐜𝐢𝐥𝐞 𝐌𝐚𝐢𝐧𝐟𝐫𝐚𝐦𝐞 𝐃𝐚𝐭𝐚 𝐭𝐨 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 Operational data often lives on the mainframe. Analytics happens in Databricks. Now, you can ensure both are in sync—with confidence. DataBuck helps you validate, compare, and reconcile data between your mainframe and Databricks so you can trust your data and make better decisions. ✅ Validate accuracy ✅ Identify & explain differences ✅ Resolve gaps faster Trusted data. Confident decisions. That’s the DataBuck difference. #DataBuck #DataTrust #Mainframe #Databricks #DataReconciliation #Analytics

Every 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭 needs a trust layer AI agents do not pause when something looks suspicious. That is exactly why they are powerful. And exactly why they are risky. A human analyst might notice that a table looks off. An agent will often continue, summarize, recommend, and act with the same confidence it would have used on clean data. That is why “better models” is not a complete answer. Before you trust an agent in production, ask: Is the source data validated? Are cross-layer totals reconciled? Is freshness monitored? Is the data correct for this use case, not just technically present? Is there a clear approval path when remediation is needed? That stack of controls is the trust layer. Without it, the agent is guessing on your behalf. What is the one trust control you refuse to deploy agents without? #AIAgents #AgenticAI #TrustedData #EnterpriseAI #DataQuality

FlyGuys delivers fast, reliable data capture across your infrastructure so you can respond quickly and keep operations on track.

What if every interaction your customers have could speak directly to you? You capture real-world feedback across the country, then watch it organize into clear, instant insight you can actually act on—without spreadsheets or guesswork. You get the power to move fast, stay human, and make decisions that feel good in your gut and look smart on the dashboard.

The scarce resource in AI isn't compute anymore. It's data worth training on. The free corpus is exhausted and the authoritative sources are closing fastest. The teams that win from here source deliberately. We acquire and verify that data. Talk to us.

Novo Nordisk. AstraZeneca. Lilly. The world's top pharma companies use FounderNest to identify partners before competitors even know they exist.

Novo Nordisk. AstraZeneca. Lilly. The world's top pharma companies use FounderNest to identify partners before competitors even know they exist.

Is this the start of AI data wars? The Spirit Airlines story has been making the rounds for a while, but for those who don't yet know, Google bid $10 million for Spirit Airlines' internal data- a bankrupt airline's data. Another AI startup, Micro1, has topped it with a $12.5 million late bid. Mercor was already involved with a $7.5 million bid. What are they actually bidding for? 100 million emails, 500 million Teams messages, 30 million lines of production code, and deep records covering pricing, operations, and audits. All de-identified. This deal is shaping the future of the data industry in so many ways! 1) This isn't "content" in the traditional sense. They want to see how companies run. Three years ago, no one would have even called this training data. The big AI labs have already scraped the public web. Now they're hunting for the raw, messy reality of how a real company operates day to day. 2) Almost every mid-sized company is sitting on a similar data mine. What set Spirit apart was how it was packaged: de-identified, offered as one lot, something a buyer could actually walk away with. That's rarer than it sounds and quite valuable. I can see more AI labs going down this route - finding deeper, hidden, more valuable data that gives insights traditional web data cannot. In some ways, it's already happening. 3) We finally have a benchmark. Executives have spent years talking up the value of internal data without a number to back it up. Now there is: $10 million for an entire airline's operating history, with a competing bid of $12.5 million. This will also encourage people to look at data through a different lens. 4) A court-appointed ombudsman will oversee the de-identification before it gets the data. The flight attendants' union, AFA-CWA, has objected anyway, arguing the deal's privacy protections were built for passengers, not employees, and demanding flight attendant data be excluded entirely. This one is being watched closely, because it could set the precedent for how companies sell internal data to AI firms, and for what actually counts as adequate de-identification. The story isn't over yet. Court hearings are still pending; bankruptcy debtors are resisting Micro1's bid. However, the underlying point this deal reinforces is that if you want your models or AI products to pull ahead, you have to go deeper and source the hidden data others don't have access to. That doesn't mean you need to bid millions on a bankrupt company's servers; it means knowing exactly what you need and going after the long-tail 20% of data that's hard to get. https://lnkd.in/dcHbA_jC

Manual data sharing and disjointed integrations are hurting your partners, teams, and growth. Get less friction and more transparency with Flume.

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L'analyse annuelle EXPLORE - Intescia Group des principaux chantiers des métropoles françaises est terminée. Une qualification précise des projets par les équipes : 12 métropoles passées au crible, 60 projets d'ampleur détaillés, un palmarès d'acteurs valorisés (architectes, BE fluide, BE structure) Ce que je retiens : ✅ Paris reste unique : la seule métropole avec une diversité de projets extraordinaires ! Résidentiel, bureaux, santé, logistique, infrastructures ferroviaires, data centers... les autres villes se "limitent" à 8-10 typologies. ✅ Le neuf recule, la réhab' s'impose : 60% des plus grands chantiers intègrent de la rénovation. ✅ Nouveauté 2026, on ajoute en plus des tops acteurs par métropole : • Lyon, on retrouve sur le podium des architectes : SOHO ARCHITECTURE, ARCHIGROUP, L'Atelier 127 • Nantes, parmi les tops BE Fluide : ISOCRATE - Bureau d'Études Techniques, SoLab Ingénierie & Environnement, NRGYS • Bordeaux, parmi les tops BE Structure : + intersections +, CESMA, Bureau d'études OCis Et vous, sur quel territoire êtes-vous actifs ? Qui sont vos concurrents/partenaires locaux ? 📥 Téléchargement : https://hubs.ly/Q041gMf40

Think your B2B data is good enough? Ryan did too, until he tried Explorium. Better data means: → More real conversations → Fewer bounced emails → Higher-performing outbound If you are still questioning the data you work with every day, it’s time to see the difference. 👉 Request an Explorium demo.

GTM companies live and die by the quality of their data. Whether you're powering outreach for clients, enriching CRM records, or building scoring models - stale data kills results before the campaign even starts. Explorium gives you fresher signals, deeper enrichment, and coverage that actually moves the needle on pipeline. See what better data looks like. 👉 Request a demo.

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