ashashutosh

CEO @Pinecone, @GoogleCloud, Founder / CEO @Actifio, Founder / CEO @AppIQ , Founder / CTO @SeranoSystems

[ashashutosh @ashashutosh]

@Azure OneLake as the System of Record + @pinecone Nexus as the System of Knowledge = Enterprise AI in production, at scale.

[Pinecone @pinecone]

We just announced the integration of our knowledge engine, Pinecone Nexus, with @Microsoft OneLake at #MSBuild.

Want to build a reliable, production-grade knowledge layer directly over your structured data in OneLake or Fabric?

Staff Data Engineer Simon Lu shows you how it's done in this quick demo. Watch here and read the full announcement: https://t.co/OGvOHB0r0c

[ashashutosh @ashashutosh]

Your enterprise data, turned into task-scoped, governed, cited knowledge your agents can use directly. 95%+ token reduction. 30x faster execution. Completion rates above 90%.

[Pinecone @pinecone]

95% token reduction. 30x faster execution. 90%+ task completion.

Today at #MSBuild, we announced a major shift to move reasoning upstream: Pinecone Nexus now integrates directly with @Microsoft OneLake.

Traditional AI agents waste tokens stitching raw data together at runtime. At scale, completion rates plummet 60% because we are forcing reasoning engines to do the heavy lifting of data infrastructure.

Pinecone Nexus x Microsoft OneLake fixes the infrastructure wall, moving reasoning upstream.

❌ Naive approach: Agent queries raw data ➔ Stitches chunks ➔ Dumps massive context window into frontier LLM ➔ High latency + exploding token costs.
✅ Nexus approach: Nexus integrates with OneLake ➔ Pre-assembles task-specific artifacts ➔ Agent queries structured data via KnowQL ➔ Instant, cited responses.

The data your agents need already lives in OneLake. Stop rebuilding data pipelines. Enforce RBAC/ABAC instantly, and track tokenomics via a unified dashboard.

📣 Announcement: https://t.co/OGvOHB0r0c

[ashashutosh @ashashutosh]

Today at Microsoft Build: Pinecone Nexus now integrates with Microsoft OneLake.

Your enterprise data, turned into task-scoped, governed, cited knowledge your agents can use directly. 95%+ token reduction. 30x faster execution. Completion rates above 90%.

https://t.co/R7NcRerp3Y

[ashashutosh @ashashutosh]

The 92% token drop is the headline. The real story is the 3,690 questions answered. That's institutional knowledge that used to live in Slack threads and analyst memory, accessible to anyone. Something that actually changes how a company thinks. Exactly what Nexus is for.

[Pinecone @pinecone]

Data agents don't fail at writing SQL. They fail at knowing your business.

Schemas show you the columns, but they don't tell you which view is canonical for ARR, how often each metric updates, or which month you changed the pricing. That sits in dbt code, Slack threads, analyst memory, and old Notion pages.

Staff Data Engineer, Simon Lu built AskData, our internal data agent, on Pinecone Nexus earlier this year. Since then it's answered 3,690 questions and lets the team actually explore data instead of wrestling with dashboards. Token consumption dropped 92% compared to pointing Claude/Cursor directly at internal sources — and 38% vs. our previous custom implementation.

Full write-up + a side-by-side demo you can run yourself: https://t.co/HCzLWSxOT4

[Pinecone @pinecone]

We're hosting an agentic AI meetup in LA on May 28th — 5–7pm at Gulp in Playa Vista.

Builders, founders, engineers. Drinks, no fluff. RAG systems, agentic workflows, or just starting out — all welcome.

RSVP: https://t.co/62OBlrvFIf

[Pinecone @pinecone]

$20/month.

Builder: Pinecone's new plan for teams that have outgrown the free tier but aren't running production workloads at scale yet.

The prototype-to-production gap has always been an awkward place to be. Builder is built for it.

→ https://t.co/JJlaJUpmPa

[Pinecone @pinecone]

Our internal AI agent was a brute-force nightmare. 🛑

It burned 40,000 tokens, took 2 minutes, and only hit 68% accuracy just to answer a single question.

Why? Traditional databases are built for humans. Agents don't have that context, so they just guess inside expensive LLM prompts.

We realized we couldn't keep pushing raw data into the LLM. We needed to bring the context closer to the data itself.

That led us to build Pinecone Nexus, a knowledge engine that compiles the exact context a machine needs before it hits the LLM. 🧠🏗️

When we moved our agent to Nexus:
📉 Tokens: 40k → 2k (90% drop)
⏱️ Latency: 2 mins → <500ms
🎯 Accuracy: 68% → 90%+

Our CEO @ashashutosh sat down with @a16z General Partner Peter Levine to share our customer zero story and how we're rewriting the AI stack.

Stop burning tokens on search. Listen here:
👂Spotify: https://t.co/sI8KFylDs1
👂Apple: https://t.co/TtmiQwhNPR

[Techstrong.ai @Techstrongai]

A preview of Pinecone Nexus, a data retrieval engine available through an early access program, uses the declarative KnowQL language to improve accuracy while reducing token consumption by up to 90%. Say goodbye to brute-force vector loops.

Find out more 👉 https://t.co/aPmNPLHKGr

#Pinecone #KnowQL #AI #Data

[Pinecone @pinecone]

Two new Pinecone regions, available now.
AWS eu-central-1 (Germany)
AWS ap-southeast-1 (Singapore)

[Pinecone @pinecone]

Native full-text search in the Pinecone Database. Public Preview.

Years of building retrieval systems with users, plus the workloads under Nexus, surfaced what agent-scale query volumes actually demand of the core database. Full-text now lives natively alongside vector. Semantic breadth with exact-match precision, in one index, sized for those workloads.

https://t.co/xirtsaCvYn

[Pinecone @pinecone]

Full text search is in Public Preview on Pinecone.

Check out the bird search demo built by Senior Developer Advocate, Arjun Patel which indexes 2,000 Wikipedia bird articles in one index across four fields: name, intro paragraph, body paragraph, and a multimodal @GoogleDeepMind Gemini Embedding 2 vector generated from each bird's image.

See the repo: https://t.co/H0ssUg3Amf

[Pinecone @pinecone]

Builder tier. $20/month, flat.

Pricing should match the stage of building, not just the scale of it. For the stretch when free feels tight but you're not yet at usage-based scale. 10 indexes, 1,000 namespaces per index, 200 assistants per project, free support.

First month free through May 31.

https://t.co/tleKy1bOSr

[Pinecone @pinecone]

Launch Week Livestream https://t.co/S6B655bpqW

[Pinecone @pinecone]

Pinecone Marketplace, live today.

Until now, building an internal app that answers questions over a company's own docs, contracts, tickets, and wikis meant months of retrieval pipeline work: embeddings, ranking, citations, evals. Pick a template in Marketplace, connect the sources, publish. Every answer cites back to its source.

Templates for customer support, legal search, sales enablement, onboarding, and more.

Free on Starter: 1M input tokens/month through June 30 (2x the usual) so you can test it on real workloads.

https://t.co/GlKkoZBick

[Pinecone @pinecone]

Agents are now the primary consumers of knowledge infrastructure. Tomorrow @jennapederson, Arjun Patel, and @RoieSchwabco are going live here to talk about what that means for your stack, share this week's Pinecone announcements, and take your questions live.

Tuesday May 5 · 11:30am PT / 2:30pm ET / 6:30pm GMT

[Pinecone @pinecone]

Introducing Pinecone Nexus. A knowledge engine for agents.

The bottleneck for production agents isn't the model. It's the per-query work of searching, stitching, parsing tables, normalizing labels. Nexus compiles that work ahead of time into typed, governed artifacts. Agents query through KnowQL.

90% fewer tokens. >90% task completion. 30x faster.

Early access open.

https://t.co/FMAjWmQOMJ

[ashashutosh @ashashutosh]

The agentic era doesn't fit inside the old infrastructure category. So we built a new one.

This week we're unveiling the largest release in the history of @pinecone : a new language for agents, a new engine to compile context, a new marketplace, new economics, and new reach.

[Pinecone @pinecone]

What happens when the primary users of AI aren't humans, but agents?

This week: A new language, new engine, new marketplace, new economics, and new reach.

The agentic era needs its own infrastructure category. Our CEO @ashashutosh Founder @EdoLiberty on what it is and what comes next.

With perspectives from @Box, @llama_index, @LangChain, @Teradata, @ThoughtFocusTec, and @UnstructuredIO.

https://t.co/jHeyQI8g8z

[Pinecone @pinecone]

Million of recipes. Fuzzy ingredient data. One afternoon to solve it. 🍳⚡

Will Templeton, CTO and co-founder at Allspice, explains why Pinecone is their fundamental building block:

❌ No weeks of cluster tuning.
❌ No struggling with keyword matching.
✅ Set up in an afternoon.
✅ High-performance "fuzzy data" retrieval.

Stop building infra. Start building your product. 🛠️

📺Full breakdown on The Spoon Podcast: https://t.co/1qzbHXmoG7
📖 Case study: https://t.co/z95I0F0MAw

[Jason ✨👾SaaStr.Ai✨ Lemkin @jasonlk]

https://t.co/kMVd6ixeTD

[Pinecone @pinecone]

An LLM is a reasoning engine, NOT a knowledge base. It knows how the world works, but it doesn't know YOUR contracts, specs, or policies.

The RAG Fix:
✅ Grounding: Tie the model to actual facts.
✅ Accuracy: Stop hallucinations before they start.
✅ Context: Turn a genius mind into a knowledgeable expert.

"An LLM without a vector database is like a genius with short-term amnesia." 🧠💨

Watch the full DM Radio episode with our CEO @ashashutosh and host @eric_kavanagh here: https://t.co/dnulk9BEFw