RavenDB has launched Quill, a new context layer designed to help enterprises deploy production AI agents using their existing SQL databases without migrating systems of record or building custom AI infrastructure. Quill connects AI agents to live operational data while providing a governed, model-agnostic foundation intended to accelerate enterprise AI deployment.
With AI becoming a board-level mandate, CTOs and VPs of engineering are under pressure to ship AI capabilities fast. But for organizations whose mission-critical data sits in legacy SQL systems, built years before embeddings or agents existed, AI can’t access their data. Modernizing or replacing the systems is expensive, risky, and time-consuming. By the time the system is updated, nobody remembers what the project was supposed to achieve or how ROI was measured.
Recently, a Gartner survey of infrastructure and operations leaders found that one in five AI initiatives fail, and only 28% report a positive ROI, which is linked to how well the technology is integrated, governed, and aligned with operational needs, not to the sophistication of the model. As AI becomes the industry standard, organizations have been left without a clear path to deliver, until now.
“Anyone can stand up an AI demo in an afternoon, but getting that demo into production with data pipelines, semantic search, security, governance, all the plumbing a small proof of concept doesn’t need until it has to run at scale, is the hard part,” said Oren Eini, founder and CEO of RavenDB. “Quill exists because we’d rather hand teams that plumbing already assembled than watch them rebuild the same project after project. You get access to the live data you need, decide the scope on day one, and change it as you go, instead of building everything from scratch.”
Quill connects directly to an organization’s existing SQL database and puts a context layer on top of it, making it possible to launch production-ready agents in weeks rather than the 18 to 24 months of a typical in-house build. The source system stays exactly where it is and remains authoritative, and the full AI stack, search, retrieval, and agents that can answer questions, are included. Agents built on Quill support web chat, WhatsApp, Telegram, Slack, and Discord out of the box.
“With Quill, the plumbing was already there, so we spent our time building the actual feature,” said Hagay Albo, CEO at Albos Technologies and Holdings, an early adopter of Quill.
By default, Quill is governed, sitting between the AI and the source system, and it is built on the assumption that the model itself cannot be trusted with unrestricted access, so organizations decide exactly what an agent can and cannot see, independent of the source database’s own permissions.
In a healthcare setting, for example, an agent can answer a patient’s question about an upcoming appointment, while prescription data is never part of the dataset it can query. What is usually a custom security project becomes a configuration choice. Quill is also model-agnostic, so teams can use any AI model, switch providers, or run entirely on their own hardware.
Quill is now available for organizations running PostgreSQL, SQL Server, or MySQL, with more databases to be supported in the future, and can be deployed in the cloud or on-premises to meet data-residency or regulatory requirements.
To start using Quill today, visit: https://ravendb.net/quill
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