Vaultiscan eliminates data silos across the enterprise by directly integrating with business-entranped databases, ERPs, and document stores to create one searchable knowledge layer without migration or duplication and seamlessly adds agentic AI data analytics on top to empower any team to pose a question in plain English and receive a governed answer with a response time of seconds. That layer is Vaulti Lake, and it’s there to overcome the slow cycle of requesting a report, waiting for a business intelligence (BI) team to provide it, and then reading a dashboard that is outdated by the time it ships.
Most companies operate their real, decision-ready data on systems designed to do a specific job yet never designed with the ability to communicate with one another: an ERP (enterprise resource planning) system for operations, a CRM (customer relationship management) system for pipeline, a warehouse system for reporting, and a shared drive to fill the gaps between them. If you’re able to answer a question that involves more than one of those systems, you’ll likely end up in the BI team and wait for a report, or maybe you’ll create another report that answers a question you have this week, but not the next.
Vaulti Lake is built to be a unified data platform that sits over the systems already in place, not another silo to feed: Vaultiscan’s AI data lake connects directly to existing databases, so the data itself never has to move.
The global data lake market has been projected to reach $59.9 billion by 2030, growing at a CAGR of 23.8%, according to Grand View Research, with more enterprises moving towards consolidating data within the data lake rather than converting to yet another data warehouse.
Vaulti Lake connects directly to the databases and systems of record a business already runs, including ERP platforms, without disrupting the processes built around them. Nothing is copied into a separate warehouse, and nothing has to be re-modelled first, which is what turns fragmented systems into one connected source of insight instead of another integration project. Because it sits over existing infrastructure, it can be extended with the VaultiScan SDK, Vaultiscan’s software development kit, embedding the same agentic query layer directly into an existing internal application or portal, wherever the business already works.
Once connected, nobody needs to write SQL (structured query language), request a report, or learn where a metric lives. Teams query data with natural language, the agentic AI interprets the question, retrieves the right data across whichever systems hold it, and returns an answer in seconds, reasoning across multiple documents and sources at once so a question that once needed three reports comes back as one (more on how this compares to traditional search in RAG vs Traditional Enterprise Search). With Vaulti Lake, you ask questions to your data directly, and any answer worth keeping can be pinned as a live chart, table, or trend line instead of rebuilt from scratch the next time.
Every team does not need the same access to the same data. Vaultiscan lets a business configure separate agents for specific teams, use cases, or data domains, each with its own permission controls, so sensitive information is scoped at the agent level rather than left to whoever has a login. A finance agent can be scoped to financial systems and a logistics agent to operational data, without either one seeing the other’s information by default.
Once asked, a question shouldn’t be twice the price. Every query that is executed against Vaulti Lake is stored as a reusable SQL query to be processed, not as a single “AI response”, and thus repeated queries are answered based on the SQL query saved, rather than being re-processed. This reduces the need to use tokens multiple times for repetitive queries and accumulates a backlog of reliable, repeatable queries rather than creating the same prompt repeatedly.
The practical difference between a governed data layer and a conventional dashboard stack shows up fastest in how each one handles a new question:
| Factor | Traditional BI Dashboard | VaultiScan’s Data Layer (Vaulti Lake) |
| Getting a new answer | Request a report and wait on the BI team | Ask in plain English, get an answer in seconds |
| Where the data lives | Copied and re-modelled into a warehouse | Stays in the existing databases and ERPs |
| Repeat questions | Rebuilt or re-run each time | Saved as a reusable query, not reprocessed |
| Access control | Often a shared login or broad dashboard access | Scoped per agent, per team, and per data domain |
| What happens to an answer | Lives in one dashboard, hard to reuse elsewhere | Pinned as a live chart, table, or trend line |
Vaulti Lake runs inside the customer’s own Azure environment, under a direct Microsoft contract, so data is stored and processed within infrastructure the business already controls. Data is never used to train the underlying model and never has to leave that environment to reach one, which matters for any organisation that cannot risk sensitive information leaking into a third-party model or a shared index. See why enterprises choose Vaultiscan for the fuller picture.
Because Vaulti Lake sits over whatever a business already runs, the same layer works across very different functions:
The same layer extends to ecommerce, rail, agriculture, and environmental data: wherever the real question is connecting systems that were never designed to be asked together.
Vaulti Lake is not the right starting point for a business whose data still needs basic cleanup: duplicate customer records, inconsistent field names across systems, or no clearly defined system of record at all. An agentic query layer surfaces those inconsistencies quickly, but it does not fix them on its own; that groundwork runs alongside a rollout, not instead of one.
The trade-off is the same one that applies to any governed deployment: it needs a proper rollout, not a quick sign-up. Connecting agents to real systems of record means data and IT teams are involved in scoping access from the outset, which is a different buying motion to switching on a hosted dashboard over a weekend.
AI data analytics is the process of using an AI system to understand a simple question, pull the relevant data out of the system and provide an answer without needing a predetermined dashboard. A BI dashboard answers the questions it was designed for; Vaultiscan’s AI data analytics layer answers whatever is asked next.
No. Vaulti Lake connects directly to the databases, ERPs, and other systems of record a business already runs. Nothing is migrated, duplicated, or re-modelled into a separate warehouse first.
Yes. VaultiScan’s multi-document reasoning synthesises information across multiple sources at once, so a question that would normally need a separate report from each system comes back as one answer.
Access is governed at the agent level, not the login level. A business configures separate agents for specific teams or data domains, each scoped to only the data it needs, on top of role-based permissions and audit logging.
The point of Vaulti Lake is not another interface to check. It eliminates the abstraction of reports, exports and dashboards that had been part of the journey between a question and an answer. Map the systems that already contain the truth and allow agentic AI data analytics to extract it; and let each team determine what it truly wants to know and when it wants to know it. That’s conversational analytics the right way: Ask once, get a governed answer, and get what’s important so people don’t ask the same question again. Vaulti Lake is built for enterprises ready to make that shift, not just look at another dashboard.
Ready to see what one searchable data layer looks like inside your own systems? Get in touch with the Vaultiscan team.
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