Today marks a special milestone! 🎉 We’re elated to share that we’ve raised $10M in Series A funding led by Accel. Our mission at e6data is to help companies tap into the full potential of analytics and AI.

We love the builders who challenge assumptions because when your team starts saying, “Yep, all good” to basically everything, eventually you develop trust issues, so why are we trusting AI agents who grade their own work this way too? This is why Jev is getting so much attention. Our teammate Prakalp’s post breaks down how Jev grades itself with more confidence. Jev returns a probability you can test against your own data. You decide how sure the agent needs to be before it acts, and that bar can differ from one action to the next. In one benchmark, putting Jev in front of the agent’s tool choice cut wrong picks from 16.8% to 7.3%. His advice for anyone building agents is to stop asking yours if it’s sure. Like a ‘yes man’ coworker, it will always say yes.

Can AI data readiness be avoided? Actually, yes. We’ve seen this before. You’re eager to bring AI into your enterprise, and ROI sounds incredible… until your tech diligence uncovers the overhaul your data platform needs to achieve the outcomes you want. The reasons are common: - Context is scattered. - Freshness varies. - Access gets complicated. - Governance adds constraints. ... and more. The reality is, most data platforms have blockers for you to get AI right. If this sounds familiar, and you or someone you know has taken on AI work that created infrastructure modernization and possibly migration requirements that will take quarters to deliver, we have the playbook to skip the delay and leapfrog your readiness to faster ROI. If you start with one workflow, the one you think matters most. Our stack gives it the evidence, context, and controls it actually needs, on your existing environment. We work around the gaps blocking that outcome, and leave your data platform alone. Join us at Gartner Data & Analytics Summit, Mumbai, where Vignesh Ganesan is hosting a session to pull back the curtain on how enterprises can take a lean approach to leapfrog unnecessary stages that block you, and prove ROI from your agents in production in weeks. ‘ROI from agents despite gaps and imperfections in data platforms.’ 21 September | 3:50 PM Theatre, Exhibit Showcase And if you can’t make it, you can find us at Booth 305. See you there!

Agent querying lives in a different league. If tomorrow, your CEO required every team to run agentic analytics on your data, how much of your infrastructure would break? Find us at Booth H68 at Big Data LDN next week for the playbook to scale without breaking. If you ask ten people how to get AI agents to query your data directly, you’ll get ten different answers involving caches, indexes, and a warehouse that struggles to perform under concurrency spikes. Most data infrastructure was built for analyst teams running dashboards, not agent swarms firing off hundreds of queries a minute, at all hours, with no pattern to predict. We built e6data so you can handle 1,000+ queries per second with under one-second latency, bring streaming data into queryable Iceberg tables in about 15 seconds, and run it all in your existing cloud or on-prem environment, so your data never leaves your systems. Can’t make it? Feel free to schedule a time with us earlier here: calendly.com/d/dz84-xmy-bsr/…

One week to Mumbai! We’ll be at the Gartner Data & Analytics Summit on 21-22 September, at Booth #305. Most folks we talk to have the same problem. Data keeps growing, and so does the compute bill. Not because anyone’s doing anything wrong, but because most engines still scale in blocks: add 20% more load, and you're often paying for 100% more capacity. We built e6data to scale by the vCPU instead of the tier. Here’s the short version, in numbers: - 100 million queries a day - 1,000+ queries per second, under 1 second latency - Up to 50% lower compute cost - Streaming data queryable in 15 seconds None of it requires touching your existing stack. e6data runs alongside Databricks, Snowflake, or Trino, directly on the tables you already have. No migration, no rewrites. Can’t make it to the summit? No worries. Here’s a Calendly link to book some time with us before the Summit: calendly.com/d/dz84-xmy-bsr/… But if you’re at the Summit, meet us at Booth #305. See you there.

Congrats to Ramp! This will make the token layer boring (in the best possible way). Once everyone stops bleeding token cost, the next storm is going to hit: agent query volume. We’ve been quietly building for it at e6data. Router solves tokens, we're solving the compute engine.

Nobody files a ticket for a slow dashboard. They just stop opening it and start asking you in Slack. That's how you lose a data product without a single alert firing. Read more here: e6data.com/problems/customer…

Your compaction job fails nightly with a commit conflict. Losing the race to the streaming writer isn't the first fix; reconciling delete files at scan time is. Rewrite those first, short job, commits before the stream can invalidate it, split compute and then fix the race.

The SQL warehouse is stuck starting, and the first query just waits. Your dashboard isn't slow. It hasn't started yet. Read more here: e6data.com/problems/sql-ware…

We’re thrilled to introduce e6data’s native MCP. Connect MCP-compatible agents like Claude Code, Codex, Cursor, and custom AI workflows directly to your lakehouse, without extra infra or custom integrations. Huge shoutout to Arnav and Tanay for bringing this to life.

There’s more hiding inside open lakehouse architectures than most teams realize. Catch @vishnuv9248's talk on June 18 at 11:30 AM PDT, then drop by booth 548 to meet the e6data team. Cost, governance, portability, and the freedom to run anywhere.

Next stop: Databricks Data + AI Summit. We’ll be there talking about how teams can scale lakehouse analytics without moving data, reworking their stack, or letting costs climb as usage increases. Zero migration. 60% cost savings. Run anywhere.

That’s a wrap on Snowflake Summit. Big thanks to everyone who stopped by the e6data booth, signed up for a demo, and joined the conversations. Loved the chats on Iceberg v3, adoption, and where open table formats go next. See you at the next one.

Query optimizers assume good stats. Lakehouses often don’t have them. At Rootconf Special Edition on Databases, Renu Sumam will talk about building feedback-driven optimizers that learn from executed queries, handle missing stats, and avoid BI workload plan cliffs.

Heading to Snowflake Summit 2026? Meet e6data at Booth #1605. Bring the workloads you’re trying to scale: high concurrency, rising compute costs, customer-facing analytics, or new AI use cases. Book a demo and enter for a chance to win 1 of 3 Meta Glasses. #SnowflakeSummit

What’s putting pressure on your data team right now? More concurrency? Customer-facing analytics? Cloud costs? If you’re at the Moscone Center, come meet e6data at booth 1605. Let’s talk bottlenecks and a scalable path forward.