KyroDB offers a runtime layer that sits between AI agents and vector or relational stores, enforcing freshness, tenant isolation, and provenance on every retrieval. It returns a ContextPacket with the requested data plus cryptographic proof and trace metadata, and fails closed when guarantees cannot be met, integrating via connectors, SDKs, or HTTP API.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents that retrieve information from dynamic knowledge stores can inadvertently use stale, out‑of‑scope, or unauthenticated data, leading to incorrect responses and difficult debugging. Existing retrieval layers lack built‑in guarantees of freshness, tenant isolation, and provable provenance.
Solution
KyroDB provides a context‑correctness runtime that sits between AI agents and any existing vector or relational knowledge store. For each retrieval request, it validates that the source data meets a specified freshness policy, respects tenant and namespace boundaries, and generates cryptographic proof artifacts documenting the decision. If the required guarantees cannot be met, the runtime fails closed or degrades the response, returning explicit warnings and omission details instead of silent stale data. The service delivers a single ContextPacket containing the retrieved documents, proof of freshness and scope, and trace metadata that agents can inspect for debugging or compliance. KyroDB integrates with common connectors such as Qdrant, pgvector, and PostgreSQL without requiring changes to the underlying stores.
Target Audience
Primary customers are developers and teams building production AI agents—such as support bots, enterprise copilots, coding assistants, and real‑time workflow agents—that require reliable, up‑to‑date context from mutable business knowledge sources.
Features
- Runtime boundary that enforces strict freshness, scope isolation, and provenance checks on every retrieval
- Proof‑bearing ContextPacket output with status, generation ID, omissions, warnings, and trace identifiers
- Fail‑closed behavior: unsafe or stale context is blocked or explicitly degraded rather than returned
- Support for tenant and namespace policies to prevent cross‑tenant data leakage
- Connectors for popular vector stores (Qdrant, pgvector) and relational databases, preserving existing data infrastructure
- SDKs for Python and TypeScript plus HTTP API for easy integration into server‑side AI applications
- Evidence plane with durable traces, replay capabilities, and proof export for root‑cause analysis