Bindu Reddy

Excited to share that Pipeshub has been featured in GeekWire’s Startup Radar. As enterprises move beyond chatbots, the next wave of AI is about execution. The real opportunity is building AI agents that understand company context, work within permission boundaries, ground every response in source data, and actually get work done across enterprise systems. Thank you Taylor Soper for the mention. https://lnkd.in/gVrfKZSg

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Thrilled to add ✅ VertexAI Live Websocket API w/ cost tracking on LiteLLM (YC W23) s/o Sameer Kankute, Madhu Reddy - this enables developers to call VertexAI's realtime API in the native format with logging + cost tracking on LiteLLM (+4 more updates 👇) 🚀 AMD - NEW Lemonade model provider support s/o Eddie Richter 💪 Gemini - improved `/generateContent` bridge 👋 Prometheus - support custom metadata labels on key/team 🧹 UI - make custom logo visible to non-admins s/o Jack Temple

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Arvind Jain sees context graphs emerging from enterprise search and observability. I agree on the destination and disagree on the starting point. Arvind - a fellow IITD alum who's devoted his career to enterprise search - has built a tremendous business at Glean, and his "capture the how, learn the why over time" framing is sharp. But the foundation needs to be different: 1 - Search sits in the read path, not the write path. By the time activity data reaches the index, the decision context - why an exception was granted, what inputs were weighed, who approved - has already been flattened. 2 - Inferring intent from observed patterns is fundamentally different from capturing decision context in the operational flow. One reconstructs; the other records. 3 - The ~80% accuracy Arvind cites on task understanding is impressive, but when agents affect customers, contracts, and compliance, you need an authoritative record, not a probabilistic inference. (The math of compounding errors is brutal - I’ve argued for 99%+ accuracy in past editions of my Substack.) -- We believe orchestration-layer startups - where the context graph is built by actually executing work - are better positioned: 1 - They sit at the point of decision. When an agent triages an escalation or approves a discount, it pulls from multiple systems and applies policies in real time. That's the moment to capture the decision trace, not reconstruct it later from activity signals. 2 - They create decision traces as first-class artifacts. The orchestration layer captures the full picture: what inputs were gathered, what policies applied, what exceptions were granted, and what state existed at the moment of decision. That's not inferred; it's captured - which means enterprises can answer "why did we do that?" definitively. 3- They can learn across customers. Arvind notes enterprise data can't be aggregated for privacy reasons. But orchestration-layer startups focus on bounded workflows - which means they can refine ontologies within a customer and across deployments without ever sharing raw data. -- PlayerZero, Maximor, and Oliv are great examples of this. PlayerZero builds the context graph by automating L2/L3 support - sitting at the intersection where code, config, infrastructure, and customer behavior collide. Maximor AI captures decision lineage by orchestrating finance workflows where reconciliation logic and exceptions actually live. Oliv AI builds it for sales - starting as a co-pilot that performs specific tasks (update CRM, send follow-up) and capturing the decision traces along the way. There will be multiple context graphs within each org, and enterprise search can certainly be a starting point for one of them. Glean's approach creates value for knowledge retrieval and understanding how work flows. The question is whether the authoritative record of decisions will emerge from observability or from orchestration. We're betting on orchestration.

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When One Engineer Is 1000x At Sequoia Capital, early product risk comes down to people. In this clip, Pat Grady and Alfred Lin, with Jack Altman, explain why one exceptional engineer can define a company’s DNA. ▫️ ServiceNow went public with most of its core code written by Fred Luddy ▫️ Airbnb was largely built early on by Nate airbn alone These weren’t 10x engineers. They were closer to 1000x. That early technical ownership shapes everything that follows. I broke this down in the Sequoia Playbook, including 10 systems most firms never had the patience to build, here: https://lnkd.in/e9ubxeyM Where have you seen one builder change everything? Do you agree?

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Nvidia GTC presentation about structured & unstructured data being foundation for AI/LLMs used for business needs. We at OLake strongly believe the same and developing features around Iceberg. Apache Iceberg as per Feb 2026 release will soon be supporting unstructured data along side with structured and semi. https://lnkd.in/geYYF--6

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HIMSS is almost here, and I’m looking forward to catching up with so many of you. I'm especially looking forward to our session with UC San Diego Health. Karandeep Singh and Jeffrey Pan will showcase how they're using Notable's Platform and AI Agents to act as a critical workforce multiplier, transforming patient access, care operations, and RCM across the enterprise. Learn more about everything we have going on at HIMSS here: https://lnkd.in/gQifSd2A. And definitely come say hello if you're attending!

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📣 Announcing NVIDIA Grove: the #Kubernetes API for modern #ML inference workloads, now part of NVIDIA Dynamo. Here’s what Grove brings to your AI infrastructure: ✅ Orchestrate complex inference systems: prefill, decode, routing – using a single, declarative resource ✅ Coordinate startup ordering, gang scheduling, topology-aware placement, and multilevel autoscaling of your whole serving system in GPU clusters ✅ Unlock efficient scaling, lifecycle management, and role-based orchestration – from simple serving stacks to multi-node disaggregated serving systems or agentic pipelines with multiple models Grove is #opensource and built for robust, flexible deployments. Learn more now. ➡️ https://bit.ly/3LUQzT1

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