Co-Founder and VP Product Strategy & Customer Experience
Enterprises have spent years, even decades, accumulating valuable information: engineering
documents, contracts, project records, financial reports, research, and institutional knowledge.
Yet unlocking its full value requires AI assistants and agents to be able to reach and reason over
it. The challenge has two dimensions.
First, historical enterprise knowledge often resides on expensive primary storage or in archives
that introduce barriers to accessibility and reachability for both users and AI.
Second, enterprise knowledge is scattered across file servers, object stores, and other
repositories spanning on-premises and cloud environments, including Microsoft Azure, making it
difficult for AI to reach and use that information.
CAEVES addresses these challenges through two complementary approaches: modernizing
historical data storage while preserving accessibility for users and reachability for AI, and
enabling AI to access enterprise knowledge across the entire data estate without requiring data
migration.
Historical enterprise information presents a persistent infrastructure dilemma. Organizations
must retain it, but much of it is rarely accessed. Keeping it on premium storage is expensive,
while traditional archiving can introduce retrieval delays and disconnect valuable knowledge from
AI workflows.
CAEVES Intelligent Deep Storage™ addresses this challenge by moving historical file data to cost
efficient Azure Blob Storage while preserving familiar file access, paths, metadata, and
permissions.
Users continue working with their files through standard file interfaces, without the complexity
traditionally associated with retrieving archived information.
The CAEVES Copilot Connector extends this value by connecting indexed historical content to
Microsoft 365 Search and Copilot, bringing previously underutilized enterprise knowledge into AI
powered workflows.
The outcome is twofold: historical information remains accessible to users and reachable by AI,
while organizations can significantly reduce the cost of retaining it - with potential storage
savings of up to 70%.
Not all enterprise information needs to move. Much of it already resides in appropriate systems,
distributed across Azure storage services, Azure-hosted file servers, and other repositories
spanning cloud and on-premises environments.
The challenge is enabling AI to reach and understand that information without introducing
another large-scale migration or data consolidation project.
And reachability alone isn't sufficient.
Finding a document is different from understanding its relevance, relationships, and business
context. AI assistants and agents need more than raw files or keyword search results: they need
meaningful, trustworthy context.
CAEVES Context LAKE™ addresses this through four complementary capabilities:
These approaches address different challenges, but their value becomes especially clear when
combined.
Intelligent Deep Storage™ helps organizations modernize historical data infrastructure while
preserving access to the valuable information it holds. Context LAKE™ extends AI’s reach across
the broader enterprise data estate while leaving the underlying data in place.
Together, they provide a flexible architectural approach: move data when storage modernization
delivers value; leave it in place when migration serves no purpose.
Enabling AI to reach enterprise data across different repositories introduces an essential requirement: information must remain governed throughout discovery, retrieval, and use.
CAEVES approaches this by preserving or respecting existing enterprise permissions and identity controls, alongside applicable classification and governance policies.
Within Microsoft environments, this includes alignment with Microsoft Entra ID identities and Microsoft Purview governance capabilities, according to the relevant integration.
Permission-aware retrieval helps ensure that enterprise knowledge is delivered within the user's authorized access boundaries. Giving AI access to enterprise knowledge should never grant users, or AI itself, access to information they’re not authorized to see. Trust isn’t a feature added after connecting data to AI. It is fundamental to the architecture.
Enterprise AI shouldn't require organizations to relocate their entire data estate.
With CAEVES, historical information can move to cost-efficient Azure storage while remaining accessible and AI-reachable. Meanwhile, distributed enterprise knowledge can be indexed, enriched, and delivered as governed context without migration.
These complementary capabilities support integration with Microsoft 365 Copilot, Copilot Studio, Microsoft Foundry, and other supported Microsoft AI services.
The result is a practical path from enterprise data to trusted Microsoft AI, combining storage modernization, cross-estate reachability, contextual intelligence, and governance.
Want to discuss the strategy for your Microsoft environment? Talk to our specialists.