Frequently asked questions

Shinydocs Pro connects to a wide range of content sources including SharePoint Online, OneDrive, Microsoft Teams, Exchange Online, network file shares, iManage, NetDocuments, Box, OpenText Content Server, Laserfiche, and Azure Files. During setup, you configure each source individually through the Control Center, and Shinydocs walks you through permissions and access for each one.

New connectors are being built and released on an ongoing basis. If you do not see your system listed, please ask your account team, as support may already be in development.

Shinydocs Pro runs on your own infrastructure. It is installed on a dedicated Windows Server within your environment. Your content never leaves your control. It can connect to both on-premise repositories like file shares and cloud sources like SharePoint Online or OneDrive from that single server.

A single Shinydocs Pro server can manage up to 40 TB of data. The recommended specs are a 12–16 core processor, 64 GB of RAM, a gigabit network connection, and SSD storage with at least 10% of your data volume free. The initial scan is the most resource-intensive phase. After that, Shinydocs only processes changes, so demands drop significantly. If you're also using Private AI features, a second server with a compatible NVIDIA GPU is required.

Yes. Once installed and configured, your team can add new repositories, adjust rules, run reports, and manage disposals through the Control Center, with no ongoing involvement from Shinydocs required. The platform is designed for day-to-day operation by your own staff, with documentation and support available when needed.

Shinydocs Pro uses a dedicated service account with read access to scan your content. It works within your existing permission structure and does not alter or override any access controls. Write or delete access is only required if you want Shinydocs to action disposals directly. Your content's security model stays intact throughout.

No. Shinydocs analyzes and classifies your content in place, and nothing is moved, copied, or stored externally. Files stay exactly where they are, and your team keeps working as normal. When a file comes up in search results or the catalog, clicking it takes you directly to the original location rather than creating a copy.

Shinydocs Pro automatically flags two categories of low-value content. Duplicates are exact copies of a file that exist in more than one location, identified by comparing file hashes. Redundant files match built-in rules for content that's typically safe to remove: files with "Copy" in the name, large disk images, archived email folders, compiled code files, and similar. Both categories are surfaced in the catalog for your team to review before any action is taken. You can also add custom rules to match your organization's specific needs.

Disposal in Shinydocs Pro is a deliberate, controlled process. You filter the catalog to the content you want to remove, review the list, provide a written reason, and confirm before anything is deleted. You can also enable file hash verification. If a file has changed since it was last scanned, it will be skipped automatically. Every disposal generates a full audit report showing what was removed, what was skipped, and why. Nothing is deleted without your explicit instruction.

No. Shinydocs Pro runs entirely within your own infrastructure. It reads file content to extract metadata and text for analysis, but does not copy or transmit your files externally. Only metadata and extracted text are stored, in the search index on your own server. Your content stays where it is, under your control.

Yes. Shinydocs Pro's Search module includes role-based access controls that determine what each user can see and do. Administrators can limit which content is discoverable in search results, configure roles with different levels of access, and ensure users only surface content they're already permitted to view in the source system.

Yes. Shinydocs Pro ships with a built-in library of PII and PSI detection rules covering passports, social security and insurance numbers, credit card numbers, email addresses, IP addresses, driver's licences, and more. Files containing these patterns are automatically tagged during scanning and can be filtered, reported on, or actioned through the catalog. If you need to detect something not covered out of the box, custom rules can be added quickly.

This is one of the most common concerns we hear, and it is a legitimate one. Shinydocs' approach to AI is built around keeping your content private and under your control.

With Shinydocs Private AI, the AI model runs locally on your own infrastructure using Ollama, which lets you run large language models entirely on-premise, without sending any data to a third-party cloud service. Your documents are never transmitted externally, never used to train a shared model, and never made available to anyone outside your environment.

This matters because many cloud-based AI tools process your content on external servers, where it may be used to improve their models. Shinydocs gives you the intelligence of modern AI without that tradeoff. Everything stays inside your walls.

Shinydocs uses AI in two practical ways, neither of which requires sending your content outside your environment.

The first is document enrichment. As Shinydocs scans your content, the AI reads each document and applies structured tags automatically, such as department category (HR, Legal, Finance), suggested retention timelines, whether a document contains sensitive information, and plain-language summaries. These tags flow into your search index and make your content far easier to find, filter, and act on.

The second is document chat. You can select one or more documents and ask questions in plain language, such as "What decisions were made in this contract?" or "Summarize the key dates in these files." The AI reads the documents and responds directly, saving you from opening and reading through every file manually.

Both capabilities run on the AI model of your choice, either a local model via Ollama or OpenAI if you prefer a cloud option. You decide where the AI runs.