Have Your AI Agents Learn on the Job
Memory layer that updates automatically with production data
Tired of manually updating your AI Agents?
80% of the work comes after AI Agent goes live – filling up knowledge gaps, resolving edge cases, and prompt optimization.
Plug and Play: Give AI Agents the Ability to Self-Improve
Features
Instant Set Up in 10 Minutes
Instead of spending weeks setting up RAG pipelines and memory graphs, simply upload your documents (.pdf, .txt, etc.) and the system intelligently extracts and connects related information across multiple documents without any manual configuration. Then, retrieve memory easily with a single API.
Human-Like Responses with Smart Fallback
Unlike typical RAG systems that sound robotic or cause hallucination when information is not available, Cognell
- Returns information in natural, conversational language
- Recognizes when it doesn’t have the answer and gracefully escalates to humans
- Prevents the common problem of hallucinations or incorrect responses
Self-Updating Memory
Connect your conversations and Cognell intelligently learns from every human-escalated conversation by:
- Identifying gaps in knowledge and adding new information to the memory base
- Detecting when existing information conflicts with updated policies or changes
- Keeping the AI agent’s knowledge constantly up-to-date without manual intervention
FAQ
Do you store my customers’ personal information?
No. Cognell is not designed to store PII or end-user personal data. The memory layer is focused exclusively on enterprise knowledge such as product information, pricing, SOPs, and policies.
You control what goes into memory. If the agent ever encounters PII, it is processed transiently and not written into the memory graph.
Do you use my data for training?
Absolutely not. Cognell never uses your data for model training, fine-tuning, or improving third-party models.
All enterprise knowledge stays within your environment and is only used for runtime retrieval and reasoning inside your agents.
Does using Cognell increases latency?
Cognell is optimized for enterprise agent workloads and adds minimal overhead.
Typical memory retrieval adds 500ms, depending on your region and deployment mode.
Because Cognell replaces long context windows with concise structured memory, many teams actually see lower overall latency and token usage.
How is Cognell different from memory layers like mem0?
mem0 focuses on storing ad-hoc conversational facts and prevent context window overflow.
Cognell is purpose-built for enterprise operational knowledge with structured, schema-aware memory – Not just embeddings; memory is organized into product knowledge, SOPs, rules, policies, and pricing structures.
Cognell is also the only memory solution in the market that can learn from human conversations, allowing your AI agents to learn from humans in production.
Can I build a self-improvement pipeline myself?
Yes, but maintaining one at enterprise scale is non-trivial. A full self-improving agent system typically needs:
Knowledge extraction from documents, tickets, and logs
Deduplication, versioning, and structured memory updates
Approval flows for SOP/policy modifications
Safety + governance to avoid storing PII
Automated evaluations and regression testing
Drift detection and rollback of memory changes
Audit logs and compliance reporting
Cognell provides all of this out of the box, so your team can focus on agent behaviour and business use cases instead of building infrastructure.