Nand AI offers a deterministic context engine that supplies enterprise AI agents with verified, proof‑based information rather than probabilistic guesses. By anchoring context across documents, chats, tickets, and systems, it prevents workflow failures caused by missing or fragmented data, enabling agents to operate reliably in high‑stakes environments. The platform automates context validation, reducing the hours teams spend manually checking and correcting AI outputs.
Funding
$1M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Enterprise AI agents often produce unreliable results because they operate on fragmented, probabilistic snippets of information pulled from disparate sources such as documents, chats, tickets, and system records. Missing relationships and incomplete context cause multi‑step workflows to fail, requiring extensive manual validation.
Solution
Nand AI offers a deterministic context engine that first reconstructs a complete, connected enterprise knowledge graph before any reasoning occurs. By ingesting data from a wide range of enterprise systems and mapping authorship, hierarchy, timelines, and cross‑system dependencies, the platform delivers verified, provenance‑backed context to AI agents. An execution layer called Magus consumes this graph to generate answers, drive agents, and automate workflows with built‑in validation and confidence scoring. The result is AI behavior that is traceable, auditable, and reliable, eliminating the need for post‑hoc human verification and reducing hallucinations in high‑stakes applications.
Target Audience
Primary customers are large enterprises that deploy AI agents for critical workflows—such as customer support automation, RFP generation, security compliance, and internal knowledge retrieval—and need provable, context‑rich outputs.
Features
- Ingest pipeline that securely connects to documents, CRMs, ticketing platforms, and collaboration tools (e.g., Salesforce, ServiceNow, SharePoint, Slack, Jira, Confluence)
- Automated discovery engine that maps relationships, hierarchies, authorship, versions, and timelines into an evolving Enterprise Knowledge Graph
- Graph‑powered reasoning that lets AI agents reason over explicit dependencies rather than isolated keywords
- Multi‑stage traceability with full provenance for every output, including pre‑generation filtering and post‑generation validation against source material
- Deterministic governance that rejects any reasoning path lacking a verifiable source, providing confidence scores for uncertainty
- Dynamic orchestration that re‑synchronizes workflows in real time as underlying enterprise data changes
- Enterprise‑grade security: tenant isolation, zero training on customer data, encrypted pipelines, role‑based access controls, and complete audit trails
- Seamless integration layer that respects existing permissions, requires no data migration, and avoids shadow copies