Enclave provides the critical infrastructure for the "memory" or knowledge retrieval layer of the AI agent stack in high-security environments. While most agentic workflows rely on third-party vector databases or SaaS RAG layers, Enclave allows agents to access sensitive internal company data without that data ever leaving the customer's AWS perimeter.
For developers building agents for regulated industries, Enclave solves the primary hurdle of data access. It serves as a secure middleware that manages permissions and sourcing, ensuring that an agent's knowledge base is both comprehensive and compliant. In the broader ecosystem, Enclave is a proponent of "sovereign AI," pushing for a shift where enterprise agents operate within private infrastructure rather than centralized vendor clouds.
Enterprise AI has a physics problem. Large language models require massive amounts of context to be useful as internal knowledge assistants, but the most valuable data in a regulated company is exactly what security teams refuse to send to a third-party SaaS provider. This tension creates a vacuum often filled by shadow AI, where employees paste sensitive documents into public models because official, sanctioned tools do not exist. Enclave attempts to solve this by reversing the traditional deployment architecture: the model comes to the data, rather than the data moving to the model.
Enclave is an internal knowledge platform that installs directly inside a customer's AWS account. It is not a dedicated instance managed by the vendor, but a sovereign deployment that lives within the user's Virtual Private Cloud (VPC). The platform uses the customer's own S3 buckets for storage, PostgreSQL for databases, and KMS for encryption. By design, Enclave excludes its own principals from the customer's key policies, meaning the vendor cannot access the data even if compelled by a subpoena. This architecture is intended to pass security reviews that typically stall or kill standard SaaS AI projects.
Most enterprises face a choice between three paths for AI adoption. The first is SaaS, which is fast but often blocked by compliance. The second is building an internal Retrieval-Augmented Generation (RAG) system, which Enclave estimates takes 8 to 12 months and costs roughly $1.5 million in engineering time. The third path, which Enclave occupies, is providing sovereign infrastructure that can be live in weeks. It offers the isolation of a custom build with the maintenance and feature set of a product.
The system functions as a "Company Brain" by connecting to common work tools including Slack, Google Drive, GitHub, Confluence, and Jira. Content from these sources is indexed within the company's perimeter. When an employee asks a natural language question, the system performs a permission-aware search, ensuring that users only see answers derived from documents they already have authorization to access. Every response is sourced and logged, providing an audit trail that compliance teams can verify directly through AWS CloudTrail without vendor intervention.
Enclave is led by Shashank Bhardwaj and is currently in a phase of engaging with design partners. The company specifically targets regulated sectors like finance, healthcare, and defense, where data residency and vendor risk are the primary blockers to AI adoption. By focusing on verifiable privacy—claims that can be audited inside a customer's own AWS console—Enclave differentiates itself from the many AI startups that rely on "trust us" marketing. It competes primarily against the inertia of internal builds and the incumbent SaaS providers that are currently blocked by enterprise security teams.
Enterprise AI for internal knowledge that deploys inside your AWS account.