Securedai is an enterprise-grade document intelligence platform for regulated industries, whose core features are full self-hosting, cited answers, and built-in governance and audit capabilities. Unlike general-purpose AI chat tools that send sensitive data to third-party SaaS, it lets models, embeddings, and documents all run within your own VPC or data center, so data does not need to leave your boundary.
I. Main Capabilities
- Intelligent document Q&A: Supports uploading documents in common business formats (policies, contracts, manuals, reports), asking questions in natural language, and streaming answers with real-time citations and source previews.
- Multi-turn conversations with contextual memory: Conversations can maintain topical coherence, and it provides controls to reset context and adjust the length of history used per workspace.
- Enterprise-grade access and isolation: Supports single sign-on (SSO), divides workspaces by team, and enforces strict isolation to ensure people can only see content they are authorized to access.
- Security and compliance controls: Provides demonstrable audit history, API keys stored in a vault, PII protection, prompt injection monitoring, and GDPR-compliant data rights.
- Autonomous operations: Supports self-hosted deployment, health status visibility, LLM provider selection, and administrator tools for managing compliance posture.
- Retrieval optimization: Uses hybrid search and reranking so answers come from the most relevant material, rather than relying only on nearest vector matches.
II. How to Use the platform summarizes the process into three steps:
- Upload knowledge: Upload documents such as policies, contracts, manuals, and reports, and the platform prepares them for search and chat.
- Ask questions in natural language: Colleagues can follow up, clarify, and ask new questions in the same thread like a chat.
- Act with confidence: View cited sources, control access by role, and meet audit and privacy requirements. Users can explore workspaces through login, converse with documents, and experience control capabilities different from general-purpose AI chat tools.
III. Applicable Scenarios and Target Users
- Compliance and security leaders: Demonstrate control, clarify who accessed what, how data is isolated, and how sensitive information is handled.
- Knowledge and operations teams: No longer search through shared drives, but instead get cited answers instantly from existing materials.
- Platform and engineering teams: Deploy locally or in a private cloud using open models and APIs, without rebuilding governance from scratch. The platform is explicitly aimed at organizations in healthcare, financial services, legal, and other regulated environments.
IV. Future Direction Officially, it is said that they are investing in the capabilities regulated enterprises need next, including: richer data connections (extracting knowledge from Confluence, databases, and cloud storage, not just uploading files); repeatable AI workflows (chaining retrieval, generation, and business steps into reusable templates for classifying tickets, summarizing documents, extracting fields, etc.); measurable quality (dashboards and experiments comparing chunking and models, so quality improvements are evidence-based); governed AI agents (specialized agents that use your knowledge and tools under guardrails, with cost boundaries, full traceability, and policies you define).
V. Limitations The materials do not state specific pricing, free quota, the list of supported document formats, the model list, or deployment hardware requirements; this information is not currently provided. The official website description does not mention smart contract security audit functionality, while the material title is document intelligence Q&A; actual functionality is subject to the official text.
