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RavenDB is the best multi-model NoSQL database that delivers full ACID transactions, auto-indexing, and secure clustering out of the box.

Fully equipped for modern workloads with native support for document, time-series, vector search, and GenAI features. RavenDB is a NoSQL Document Database for applications operating on the cloud or on-premise. RavenDB serves over 1,000 organizations from startups to Fortune 500 companies. It is designed for a distributed environment and is fully transactional(ACID).

22/10/2026

Many AI projects stall on data. Not the model. The data.

Quill lets you build on the SQL database you already run. No migrations, no rebuilds.

Your agent gets the live data it needs. Nothing more.

Register now and get 90 days free: https://ravendb.net/quill/start/

11/10/2026

The bones of an AI agent can be put together in a weekend. But getting it to production can take 6-12 months.

Because?

Because the data the agent needs to perform is usually locked in a system of record. And making that data accessible (without letting the agent get its hands on every index) is what takes time.

That’s why we built Quill. So you can build AI agents on top of your SQL data in weeks instead of months.

Without letting AI agents run loose on critical or sensitive data.

Click the link below to start your 90-day free trial of Quill, today: https://ravendb.net/quill/start?utm_source=facebook&utm_medium=organic&utm_campaign=quill_2

29/09/2026

We sure have been busy this year at RavenDB. New hires. New features. Even a new product 😍! Somewhere in between all that, we also revamped a lot of our website. Consider this your invitation to check it out and poke around.

For starters, be sure to visit our new Developer Hub page and get your hands dirty - well, sort of 👉 https://ravendb.net/developer-hub

Happy building 🐦‍⬛

24/09/2026

Checking accounts. Savings. Loans. Credit cards. Four systems that don't talk to each other. Ask AI to reason across all of them and you've found the enterprise AI project nobody wants to admit takes two years.

RavenDB CEO, Oren Eini, sat down with Swapnil Bhartiya at TFiR to talk about Quill, a context layer that sits on top of existing SQL databases and gets them AI ready without a rip and replace migration.

Thanks to Swapnil and the TFiR team for having us on.

Watch the full interview here: https://www.youtube.com/watch?v=RzXeIYxqdp8

15/09/2026

IoT sensor data, stock prices, application metrics. The timestamps add up fast and most databases fall apart under the weight.

RavenDB's built-in time series support was designed for exactly this. Compressed storage, server-side aggregations in milliseconds, and native rollup policies that handle long-term data automatically.

Watch it in action and grab your free developer license here: https://ravendb.net/license/request/dev

10/09/2026

The RavenDB team got together this week for our annual end of summer lunch, a tradition that's been going strong since the company's early days.

This year's table looked a little different. We welcomed four new faces to the team, and said a warm goodbye to Nola, who's retiring after years with us. Having people stay with RavenDB long enough to retire from it was one of the original dreams our CEO, Oren Eini, had when he started this company, so today felt like a milestone worth marking.

As the team keeps growing, this might be the last time we all fit around one table for this particular tradition. All the more reason we're glad we got this one in, especially with Quill on everyone's mind lately. It's shaping up to be one of our most exciting launches yet, and the whole team can feel it.

Here's to new beginnings, well earned endings, and everything in between.

08/09/2026

Build AI in 2-3 weeks instead of 12-18 months. Today we're launching a 90-day free trial of Quill: a live context layer for AI. Quill makes your SQL data accessible so you can build AI agents, AI assistant, and AI search.

Here’s why that matters:

Most AI projects fail to go beyond the POC.

Because while you can build a prototype in a weekend, getting that prototype to production takes months: standing up a vector database, building data pipelines from scratch, wiring up live sync, adding access control, and then getting all that OK’d by security and compliance.

Quill does that hard work that you would have had to do yourself. Simply point it at your database and it creates a fully governable, live-synced copy of your SQL data, with embeddings, vector indexes, full-text and semantic search included, and keeps it current via CDC.

Your original database stays exactly where it is and remains your authoritative system of record.

And yes, Quill is secure by design. Every interaction with the model is scoped to only what it should see. The AI is treated as an agent of the user, never trusted with privileged access. And your data never leaves your servers.

Go here to learn more about Quill and sign up to activate your 90-day free trial today: https://ravendb.net/quill?utm_source=facebook&utm_medium=social&utm_campaign=quill

03/09/2026

The countdown starts today. Watch this space because we’ll be launching a brand new product soon.
Follow along to find out what it is when we make it official 👀

03/09/2026

Too many joins. Too many mechanics. Too many points of failure.

That was the reality for Adept Concept, running SQL Server plus a bolted-on Python RAG stack to power AI-driven compliance filtering.

The fix: consolidate onto RavenDB, with built-in vector search replacing the parallel infrastructure entirely.

The result: 60% faster performance and a far simpler architecture.

Sometimes the biggest performance win isn't a bigger server. It's fewer systems talking to each other.

Find out how CEO, Arild Havnen, cut the mechanics and sped things up by 60% in the full case study here: https://ravendb.net/use-cases/adept-concept

26/08/2026

Full-text search finds the exact clause.
Vector search finds the risk nobody phrased the same way twice.

In finance, missing either one has consequences: an audit that can't be proven, or a risk pattern that stays invisible until it's too late.

Most teams pick one. The ones building for compliance and risk don't have that luxury.

We broke down why, and what it means for RAG pipelines handling investment analysis, credit risk, and claims data.

👉 https://ravendb.net/articles/vector-search-vs-full-text-search-what-devs-in-finance-need-to-know

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