Promethium

Context graphs are having a moment. Foundation Capital: "AI's trillion-dollar opportunity" Gartner: "essential infrastructure for agentic systems" So what exactly is a context graph — and why does it matter for enterprise AI? Part 1 of our 3-part series: hubs.ly/Q047kbQ60

50% of orgs have AI agents in production. Only 27% use them for analytics. The gap? Agents can't access distributed data or organize the metadata needed for accurate insights. The BARC report lays out the architecture that fixes this 👇 hubs.ly/Q045zM4c0

"Embed architecture into everything before you roll anything out." At Rubrik, @AjaySabhlok made architecture the decision-making layer — not a review function. The result: 700 employees to IPO, zero shadow IT. Full episode 👇 hubs.ly/Q045zXYb0

Everyone in enterprise AI is talking about context graphs right now. Foundation Capital called them "AI's trillion-dollar opportunity." Gartner says 50%+ of AI agent systems will use them by 2028. But what actually is one? We break it down: hubs.ly/Q047jMvn0

Knowledge graphs capture "what" and "who." Context graphs capture "how" and "why." Gartner says by 2028, 50%+ of AI agent systems will leverage context graphs for decision tracing, guardrailing, and continuous learning. New report breaks it all down 👇 hubs.ly/Q045zGmX0

60% of AI initiatives fail to meet business SLAs. It's not the model. It's the foundation underneath — scattered data, fragmented context, outdated architecture. Is yours ready? Take the checklist 👇 hubs.ly/Q045zHb30

How do you get Claude to talk to all your enterprise data? Not fine-tuning. Not CSV dumps into a prompt. You need an agentic analytics fabric underneath — handling data access, metadata, context, and governance. CEO Prat Moghe breaks it down 👇 hubs.ly/Q045zCZb0

What if you could just talk to all your enterprise data? Not dashboards. Not report requests. A real conversation — across every source, with context built in. 5 scenarios showing how it works 👇 hubs.ly/Q045zGLW0

An AI agent that can forecast revenue, optimize pricing, and investigate return rates — across distributed data? BARC breaks down 4 use cases where an agentic analytics fabric makes it real. It's not about a smarter model. It's about what's underneath. 📥 hubs.ly/Q045zLN50

From distributed data to trusted insight in three layers: → Data Access (query anywhere) → Context Engineering (understand everything) → Analytics Agent (deliver to anyone) Built-in governance throughout. The agentic fabric stack, explained by BARC: hubs.ly/Q045z3nq0

Self-service data management isn't optional anymore — Gartner says it's inevitable. Most orgs aren't ready. Data scattered, context fragmented, architecture outdated. This report breaks down what needs to change 👇 hubs.ly/Q045zlTl0 Come meet us at Gartner D&A Booth 101

Gartner D&A starts Monday. Talk to ALL your data — Snowflake, Salesforce, Oracle, everything. Requires 3 things: Agent engineering Context engineering Distributed access Booth 101 hubs.ly/Q041V4F90 #GartnerDA

Most governance programs were built for traditional data risks. Now they need to cover two new domains: models and agents. Models hallucinate. Custom agents can have different intentions than humans. These risks compound. Watch the full panel: hubs.ly/Q044dN5g0

Full episode: - YouTube: youtu.be/m0jr4EBulvo - Spotify: open.spotify.com/episode/4aHelo… - Apple: podcasts.apple.com/ar/podcast/ep-…

"Before I go into any function, I create my own preconceived biases of what I think are important. Then I spend the first few weeks trying to validate those assumptions." Generac's Neil Bhandar on cross-functional data work: don't lead with dashboards. Lead with hypotheses.

Less than 1 week to Gartner D&A. "POCs looked great. Production accuracy failed." Curated demos ≠ real business questions. See how unified context fixes this. Booth 101 & Theater 1 | Tue 2:35pm Book a dedicated slot ahead of time: hubs.ly/Q041V4HG0 #GartnerDA

Before you scale AI, answer these 15 questions. 60% of AI initiatives fail to meet business SLAs — not because the models aren't good enough, but because the data underneath isn't ready. We built a quick self-assessment. Here's what it covers 🧵

Semantic layers handle definitions and KPIs. But user intent? Role-specific vocabulary? Query-level governance? They fall short. The agentic analytics fabric closes every gap. See the full comparison from BARC's new Spotlight Report 👇 hubs.ly/Q044dbtx0

What does it actually take to break the metadata bottleneck for contextual AI insights? We unpacked it with BARC's Kevin Petrie and Promethium CEO Prat Moghe. Five key takeaways 👇 hubs.ly/Q044j4lX0

Talk to all your enterprise data — directly from Claude. Promethium's MCP integration lets you ask business questions, get trusted answers from your actual data, and keep full lineage and governance — without switching tools. See it in action 👇 hubs.ly/Q044dLtJ0