Instructed Intelligence provides a contextual knowledge layer for enterprise AI systems, enabling AI tools to answer from an organization's own cited, versioned knowledge rather than unverified general data. The platform, called Cornerstone, assembles in an afternoon and is designed to make AI outputs trustworthy by construction, with every claim traceable to its source. It is built for regulated industries where being wrong is expensive, such as banking, law, healthcare, and government.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprise AI deployments often fail at scale because organizational knowledge is scattered across systems built for human filing, not machine reasoning. AI tools confidently produce answers from outdated or out-of-context documents, and every output must still be manually verified by accountable staff, creating a bottleneck that prevents productivity gains. Additionally, token costs rise with every question, and the knowledge workers feed into AI sessions is lost when the session ends, fragmenting organizational understanding.
Solution
Instructed Intelligence provides Cornerstone, a contextual knowledge layer that gives AI systems access to an organization's knowledge as cited, versioned facts. The platform distills decisions, rules, and rationales into a shared source of truth that both AI and humans query, ensuring answers are provably correct and traceable to source documents. Cornerstone tells the AI when no fact exists rather than guessing, and it reduces token consumption by routing the model to the right fact efficiently. The platform also includes Coordinator, a work-tracking layer that records decisions and verifies work as it happens, enabling parallel AI agents without conflicts. This combination moves the verification bottleneck from individual heads into shared infrastructure, allowing one trusted operator to direct far more work than previously possible.
Target Audience
Primary customers are regulated enterprises in banking, law, healthcare, government, and manufacturing where being wrong is expensive, as well as teams at any scale that need AI outputs to be provably correct and traceable to source documents.
Features
- Cited-fact knowledge base that distills organizational decisions, rules, and rationales with provenance back to source documents
- Versioned knowledge management that tracks changes and identifies what relied on outdated information when corrections are needed
- Token-efficient retrieval that gets the model to the right fact with a fraction of the tokens, reducing AI infrastructure costs
- Coordinator work-tracking layer that claims, tracks, and verifies work as it happens, enabling parallel AI agents without stepping on each other
- Enterprise-owned knowledge layer that runs inside the organization's boundary, with data owned by the enterprise rather than rented from the vendor
- One shared knowledge base supporting multiple concurrent projects, with the platform running on itself in production across 28 projects