Cognee Raises €7.5M to Build Persistent Memory for AI
European AI startup Cognee has secured a €7.5 million funding round to develop a persistent memory layer designed to dramatically reduce AI hallucinations in large language models. The investment underscores growing demand for safer, more reliable generative AI systems in enterprise and regulated sectors.
Tackling the Hallucination Problem in Generative AI
Modern large language models (LLMs) excel at generating fluent text, but they are prone to hallucinations—confidently producing incorrect, fabricated, or unverifiable information. For industries such as finance, healthcare, and law, these errors can translate into serious operational and compliance risks.
Cognee is building a persistent memory layer that sits between LLMs and enterprise data sources. Instead of allowing models to rely solely on statistical patterns learned during training, the platform continuously grounds responses in a curated, versioned knowledge base. This architecture is intended to ensure that AI-generated answers can be traced back to specific documents, records, and events.
How Cognee’s Persistent Memory Layer Works
The company’s technology focuses on three core capabilities: structured ingestion, long-term retention, and verifiable retrieval. Enterprise data is ingested, normalised, and indexed using advanced vector search and knowledge graph techniques. The system then maintains a persistent memory store that can be updated in real time as information changes.
When an LLM receives a user query, Cognee’s middleware retrieves the most relevant, up-to-date facts from this memory layer and feeds them into the model as context. This approach, similar in spirit to retrieval-augmented generation but designed as a durable data plane, aims to keep responses grounded while preserving the fluency and flexibility of state-of-the-art models.
Enterprise Use Cases and Market Potential
The funding will be used to scale engineering, expand integrations with popular LLM platforms, and deepen partnerships with early enterprise customers. Target use cases include AI copilots for internal knowledge bases, compliant customer support assistants, and decision-support tools where auditability and factual accuracy are critical.
As organisations move from experimentation to production deployment of AI assistants, the ability to minimise hallucinations and provide verifiable answers is becoming a competitive necessity. With its persistent memory layer, Cognee is positioning itself as a key infrastructure player for the next generation of trustworthy enterprise AI.