Vertical AI is moving from pilots to broader deployment in legal, finance, and other regulated fields. Surveys in legal report a large majority of attorneys using AI tools weekly, and finance platforms report tens of thousands of professionals across hundreds of institutions. Reach is wide. Depth is not. Industry analyses put the measurable gains in a narrow band of routine, high-volume work, and in some firms adoption is pushed by clients rather than pulled by the people doing the work. The reported challenges are consistent: showing impact on quality and economics beyond usage, knowing where client and deal content sits and who can retain, delete, or audit it, and meeting data residency and control requirements that some clients treat as non-negotiable. The more work an agent does, the more the knowledge boundary matters. We heard these concerns from enterprise users for over twelve months and worked closely with the largest global enterprises to build Pinecone BYOC, now generally available on AWS, Google Cloud, and Azure. The data plane runs inside the customer's own account and region. We manage it with no SSH, no VPN, no inbound access, and no standing cross-account IAM role. Toyota Motor North America was among the first users. Their manufacturing and R&D knowledge grounds their AI applications and stays within their environment. More BYOC capabilities are coming over the next few months. pinecone.io/blog/byoc-genera… #TrustedAIKnowledgePlatform #AgenticAI #VerticalAI

Every tool we now call basic was once called a threat. It is Friday, so treat this as a slow weekend read. A chance to sit with the question with an open mind, and hold back from reacting to how you feel right now. This is a open topic and am not advocating to settle it. Sharing history because of a lot of the present rhymes with the past. Socrates famously did not want to write his ideas down, because he believed it would weaken our memory. A famous composer warned that recorded music would destroy our souls. Once machines could play music, people would stop learning instruments and something "human" would be lost, he worried. Calculator was considered cheating, refrigerator was considered a threat to a proper household, because real families cooked fresh, and frozen food meant you had given up. Each of these was considered a moral failing. We would get lazier and lose a skill that made us "human". And each one slowly became something we use every day without a second thought. The heresy of one age becomes the orthodoxy of the next. Or, like the calculator and the record player, simply a thing of the past. Some of those concerns were right. Put us on a road without GPS and most of us would be late, if not lost. So the concerns were not unfounded. The tool just tends to win in the end. And frankly, choosing the tool is as "human" as the voices of resistance. We have been underestimating human resilience and ingenuity at every one of these moments. History shows we adapt better than we predict, even when the short term is rocky. Today, most of the reaction to using AI for writing is a reaction to genuinely bad output. Not unlike how you would react to an old, tinny recording on a shellac record. This weekend, maybe hold that reaction to an AI-written piece, and see if there is a message in there that enriches your knowledge and experience. Because that reaction fades over time and what stays is a new normal as the standard. Oh, and yes. This was written by a human. And refined by AI! #GenAI #AI #FutureOfWork #Writing

AI is only as useful as the knowledge it can reach. For most enterprises, the most valuable knowledge is also the most sensitive, and out of reach for AI offered as a service. Research and engineering data, customer records, internal policies, operational history... the crown jewels. Running AI against them left two choices: send the data to a vendor's cloud, which often fails security review, or DIY a full stack for ingesting, parsing, embedding, vector search, inference, reranking, and knowledge creation, with a platform team to run it. Most do neither, and limit the AI they apply and the transformation they can undertake. This week there is a third way: @Pinecone BYOC runs the Pinecone knowledge platform inside your own cloud account on AWS, Google Cloud, and Azure, and Pinecone operates it. Data security and sovereignty with a fully managed experience. Critical for every enterprise, especially regulated and security-conscious ones: Banks, Insurance, Healthcare, Manufacturers, Public Sector with data residency and governance rules. The case: - Workloads blocked by security review can now move to production. - No dedicated team to operate knowledge infrastructure. - Compute and storage sit in your own account, behind your firewall, under your existing cloud agreements. - Same API as the managed service, so one seamless path between BYOC and managed. @Toyota North America was one of the first to run BYOC in production, grounding AI in decades of manufacturing knowledge that mixes structured engineering data with unstructured process documents. That knowledge stays inside Toyota's boundary and is served only to models they have already vetted. When a company with that bar runs it on Pinecone in production, every other global enterprise has a reference it can trust. BYOC is one more piece of what we are building as the trusted AI knowledge company: knowledge wherever AI runs, in our cloud, in the customer's cloud, and next in self-managed and air-gapped environments. That work is already underway. pinecone.io/blog/byoc-genera… #Pinecone #VectorDatabase #TrustedAIKnowledgePlatform #BYOC #EnterpriseAI #AgenticAI #Toyota

"Agents don't need vector search" is the take of the month. Cursor and Windsurf switched from vector search to tool-search and their benchmarks validate the change. For coding agents, it's right. A codebase is a few thousand files. An agent can grep them directly cheaper and more accurately. You don't need a vector database to search a small, static set of files. (For some reason, a couple of coding tools started with vector db. At that size, it's an overkill.) Enterprise is a different animal. Hundreds of millions of docs. New ones daily. Access rules that change constantly. Millions of documents in play for a single search. At that size the simple stuff breaks. Grep can't scale. Plain top-k isn't enough either. The nearest-neighbor part was always the easy part and table stakes since 2023. The hard part is everything around it: keeping knowledge current, enforcing who sees what, resolving conflicts, doing it across tens of billions of vectors without the cost running away. That's the real job a vector database does. And we continue to research deliver enhancements with @pinecone database and @pinecone Nexus. And it gets harder as agents get better, not easier. A smarter agent on top of wrong knowledge just fails faster, and at greater cost. More coming in a few weeks as we double down on @pinecone vector database : deeper, broader. We pioneered this market. We intend to keep leading it. #RAG #AgenticAI #Pinecone #TrustedAIKnowledgePlatform

Was asked at a meetup this weekend: what's the difference between a context engine and @pinecone's knowledge engine? Enough people had the same question that it's worth sharing. TL;DR: Context is short-term memory reloaded into the model's window for every conversation. A @pinecone knowledge artifact is a durable, structured form of knowledge for a task that the knowledge engine actually learned, and the agent reuses it across conversations and sessions. Context engine: retrieve documents, rerank, compress, fit them into the window. The model reads it, answers, and it's gone. Next question rebuilds the same pile from scratch. Knowledge engine: the agent queries the knowledge directly and gets back a distilled answer with sources, instead of fifty raw passages to sort through. That understanding lives outside the window and persists. The gap shows up in cost. Rebuilding our internal agent AskData on @pinecone Nexus: 92% fewer tokens, 78% fewer query turns, 80% lower cost per question. Early customers saw 90%+ token reduction vs agentic RAG, with faster completion and higher accuracy. In the context model, spend scales with usage in a straight line and the knowledge evaporates after each turn. With knowledge artifacts, cost per answer falls as you use it, latency drops, and accuracy improves because the model reasons over a small, high-signal result. And in regulated industries, the one that matters most: a knowledge artifact is an asset you can govern. You can see where an answer came from, control who retrieves what, and audit it later. Context rebuilt on the fly gives you none of that. Knowledge is worth more than intelligence when the intelligence is rented by the token. The @pinecone Trusted AI Knowledge Platform is where it lives. #Pinecone #AgenticAI #TrustedAIKnowledgePlatform #EnterpriseAI

A customer asked me what our new engineering post means for their business. Here's a translation: Every AI app: search, RAG, agents, turns your data into vectors. Storing them at full precision is expensive. Quantization is the compression step that shrinks them. Get it right: answers stay accurate, your bill drops. Get it wrong: you overpay, or retrieval quality falls. The catch: dozens of methods exist and every paper measured itself differently. No fair comparison existed. So we built VQ-bench with 14 quantizers, same datasets, same metrics, open source. The takeaway for anyone running AI at scale: you don't have to trade accuracy for cost, and the right method depends on your workload, not on whoever published the flashiest number. This is the part of the stack you shouldn't have to build. We do the research so your team doesn't have to. There's no compression algorithm for experience. pinecone.io/blog/vq-bench/

A vector search returns a confident answer to every query. Including the ones it gets wrong. Ask it a question and embeddings do their job. Semantic search was built to retrieve the best meaning for it. Ask it for a SKU, an order ID, an error code, a case number, and the job changes. Those are literal strings, and an embedding model splits PROD-SKU-7842X into tokens and ranks it right next to 7842Y. User needed exact match, and search comes back with semantically close response. No errors. And the agent ships the wrong part or cites the wrong clause. Surprise comes in the form of unexpected support ticket, or a churned customer. For many teams, exact matching meant standing up a second keyword-search cluster next to the vector database: size the nodes, tune the shards, reconcile two result sets, own the upgrades. After months of validation with several key user partners, full-text search is generally available in Pinecone Database. BM25 ranking and text-match filters in the same index as your vectors. One query locks the result to the exact identifier and ranks everything else by meaning. What this means for the business: • The queries embeddings silently get wrong come back exactly correct. Fewer wrong parts shipped, fewer wrong clauses cited, fewer tickets you never saw coming. • No second cluster to run. Engineers build product instead of maintaining search infrastructure. • Same serverless economics, metered in the read and write units you already use. Nothing new to operate. Exact-match precision and semantic recall from one query, in the serverless index you already run. #Pinecone #VectorDatabase #TrustedAIKnowledgePlatform #RAG #AgenticAI #FullTextSearch

Most enterprise AI is sold from the top: one company-wide ontology, a grand model of how the whole business works, rolled out everywhere and tuned for months by a big outside team. It promises order. What enterprises actually want is close to the opposite. Proven ROI for one role at a time, personalized to the thing that makes them valuable: how their people work and the data behind it. A top-down model smooths over exactly the parts that make each company distinct. That's the moat you're paying to protect. What works is quieter. Pick one role. Clean the foundation underneath it first. Stand up an agent in hours, and let it earn trust and spread. And because the tech changes every quarter, keep the freedom to swap the model or the data sources without rebuilding everything. That's the requirement we built @Pinecone Nexus around, over seven months with global enterprises: trust to protect the moat, knowledge as the durable core you own, and flexibility to change everything around it. If you're working through enterprise AI adoption, sharing our observations. Looking forward to hearing yours. linkedin.com/pulse/ai-adopti…

All you need is attention. A Working thesis on a AI native company A capable model is something you rent, swap, and re-rent inside a quarter. What your company knows about its customers, its data, and its own work is the part that compounds. So the durable edge isn't which model you use. It's how fast your org can learn something true and act on it before the market moves. Here's the constraint that reorganized how we think about our operations as a AI native organization. It takes a team six to eight repetitions to internalize a strategy. That's about two months of saying it every week. Two months is also, right now, how often the ground moves: a new open-weight model, a new price floor, a capability that was research last quarter and is a default this one. By the time a traditional org absorbs a plan, the plan describes a market that already changed. Every handoff is latency. Every silo is where context goes to die. The paper that started this era of AI was called "Attention Is All You Need." A model gets good by learning where to focus, not by weighing everything equally. Companies work the same way now. The scarce resource isn't talent or capital. It's attention: where the organization points its focus, and how fast it can re-point it without a reorg. The moat that follows is loop velocity. How fast you can build, sell, learn, and build again, closing each loop on a number a customer would actually recognize. This is a working thesis, still forming. If you want the longer version of how we're running Pinecone this way, reach out to me directly. Glad to share what's working and what isn't. linkedin.com/pulse/all-you-n…