TigerGraph

Tokenmaxxing is lazy. If your AI strategy is “use more tokens,” you’re not scaling intelligence, you’re scaling waste. More context does not equal better answers. It just burns compute and hides weak retrieval. The real bottleneck isn’t the model. It’s bad context at inference. And no amount of GPUs fixes that. Graph changes the equation: → Stop retrieving “more” → Start retrieving what’s actually connected → Cut tokens. Increase signal. Eliminate guesswork Less compute. More truth. That’s the difference between AI that demos well and AI that actually runs in production. Rajeev Shrivastava calls it out clearly, see here: https://lnkd.in/dGMETDwu #GraphRAG #EnterpriseAI #AIInfrastructure #TigerGraph #Tokenmaxxing #Inference #AI

Enterprise automation is moving fast. But most systems are still making decisions on disconnected data and that’s where things break. Agents can execute. But without context, they can’t reason. So decisions look right in isolation, but fail when you consider the broader system. Because enterprise reality isn’t flat. It’s connected across customers, accounts, suppliers, and systems. That’s the shift. When automation runs on connected data, it sees dependencies, understands impact, and can actually explain why a decision was made. This is the difference between faster automation and smarter outcomes. Read Paige Leidig's blog here: https://lnkd.in/giN2tujM #GraphAnalytics #EnterpriseAutomation #ConnectedData #ExplainableAI #DataArchitecture

Enterprises aren’t missing data. They are missing visibility into how it connects. Most analytics still answer: what happened? Very few can answer: how does it spread? That is the gap. A supplier issue doesn’t stay contained. It cascades across products, regions, customers. The impact follows the network, not the dashboard. If you are only looking at metrics, you are only seeing outcomes. Not the structure driving them. When you model connections, you see what actually matters, where risk concentrates, how dependencies stack, and how disruption moves. That is the difference between reporting and understanding. Read Rajeev Shrivastava's blog here: https://lnkd.in/gqbuYrtu #GraphAnalytics #NetworkAnalysis #RiskManagement #DataAnalytics #EnterpriseData

Most teams keep trying to fix entity resolution by tuning match rules and they still get it wrong. Because identity is not in the fields, it is in the connections. Names change, emails get reused, and attributes overlap. Similarity becomes a guess. But structure doesn’t behave that way. When you look at shared devices, transactions, and networks, identity becomes clear and confidence becomes real. That’s the shift: from matching attributes to validating relationships. Stop guessing. Start proving. Read Victor Lee's blog here: https://lnkd.in/gFu55asb #GraphAnalytics #EntityResolution #FraudDetection #RiskManagement #DataQuality

The constraint isn’t GPUs. It’s wasted compute. The ~19GW power gap is already forcing real tradeoffs across AI systems. And most teams are solving it the wrong way, by trying to scale infrastructure. The issue is inference. Every oversized prompt, every “just in case” token, multiplied by super-linear attention, burns through power fast. Precision fixes it. With GraphRAG, we don’t send more data to the model. We send the right data: → Up to 90% fewer tokens → Dramatically lower GPU load → Relationship-aware, explainable answers This is the shift. Not bigger models. Not more spend. Better architecture. If energy is the bottleneck, graph is the advantage. Building on Rajeev Shrivastava’s post yesterday. https://lnkd.in/dwM8TpH5 Very much worth the read. #ArtificialIntelligence #GraphRAG #EnterpriseAI #AIInfrastructure #EnergyEfficiency #TigerGraph

Most graph projects don’t fail at scale. They fail before they ever get there. Not because of technology, but because of modeling. Teams load everything, mirror tables, connect foreign keys and call it a graph. What they get is complexity, not clarity. The shift is simple: start with structure, not data. 1. Define the entities, 2. Define the relationships, 3. Test how the system behaves, before you scale it. Because once ambiguity is in the graph, it compounds fast. Graph is not about connecting data. It is about modeling how your business works. Get that right and everything downstream gets easier. Read Paige Leidig's blog here: https://lnkd.in/gDdAPA9f

Most machine learning still learns from rows. But fraud, risk, and identity don’t live in rows, they live in connections. That is the gap. Two accounts can look identical on paper. Same attributes. Same behavior. Until you see one is embedded in a fraud ring,and the other is not. Flat models miss it because they cannot see structure. Graph exposes it. When you shift from records to relationships, patterns that were invisible become obvious and outcomes change. This is how you move from incremental accuracy to real signal. Read Rajeev Shrivastava's blog here: https://lnkd.in/gxm-uknh

Most organizations still analyze data as records. Reality doesn’t work that way. Airline networks make it obvious. It’s not volume that matters. It’s structure. • A low-traffic node can break the system • A single connection can expose risk across layers • A disruption does not spread randomly—it follows the network That is exactly how enterprise systems behave. If you are not analyzing connectivity, paths, and clusters, you are missing how risk moves. This is where graph analytics changes the game by turning structure into something measurable, not assumed. Read Victor Lee's blog: https://lnkd.in/g3Z4xkhH #GraphAnalytics #NetworkAnalysis #RiskManagement #DataScience #ComplexSystems

Great post by Anurag Pola! Thank you Anurag for being part of Learning on Graphs Conference. You went in thinking graphs = fraud, risk, recommendations and and came out realizing they’re everywhere. That’s the unlock. This line nailed it: We’re underusing graphs because they require a mindset shift. Most systems still think in rows. The real world runs on relationships. And once you model that everything changes!

This is what actually matters. Building and learning how to think differently. The teams that get exposed to real mentorship early, how to connect data, how to see patterns, how to go beyond flat tables, that’s what changes the trajectory. Love seeing TigerGraph show up here in a meaningful way. Not as a logo, but as something builders can actually use to think in relationships, not rows. And Devanshu Saxena, this is what great looks like. Leaning in, helping teams get it, not just get through it. Thank you to everyone who made #Devcation what it was!