Cognigy
https://www.cognigy.com/product-updates/an-ultimate-guide-to-ai-agent-memory
Enterprise contact centre (voice + chat). Short-term covers current session; long-term persists across sessions and is shared by every agent a customer might encounter. Most explicitly engineered memory model in the segment.
At a glance
- Type
- Two-tier short + long-term, shared across agents
- Tier
- T1
- Created
- 2022-01
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- Enterprise only
- Funding
- $100M total Series C · 2024-06
Taxonomy
- storage
- kv
- retrieval
- exact-match
- persistence
- cross-session
- update
- append-only
- unit
- episode
- governance
- inspectable
- conflict
- none
When to use
Optimised for: cross-channel customer graph + agent handoff + CRM integration
Anti-fit: not for non-customer-facing use cases
Pros & cons
Pros
Mature CCaaS-integrated agent platform with memory tuned for compliance and audit in regulated industries.
Cons
Heavyweight CCaaS positioning means memory is one capability among many; pricing is enterprise-tier.
Claims & capabilities
1B+ annual interactions. Gartner Magic Quadrant Leader 2025. Customers report 30% AHT reduction, 3× self-service.
Technical surface
- API surface
- searched not found
- Backend storage
- searched not found
- Deployment
- Both
- Embedding model
- searched not found
- Multi-tenancy
- Granular RBAC with separation of duties; multi-region deployment (EU/US); dedicated single-tenant available
- MCP
- via official adapter — Cognigy MCP
- A2A
- no A2A protocol support advertised — vertical product, no A2A integration documented
- OpenTelemetry
- first-class
Similar systems
Other vertical / domain-specific ai memory in the catalog, ranked by inbound references.
- NVIDIA ReMEmbR T3
Builds long-horizon memory by captioning video segments with VILA, storing captions with timestamps + 3D position coordinates in MilvusDB. At query time, LLM iterates retrieval across text, time, and position modalities. Deployed on Nova Carter robot (Jetson Orin).
- Abridge T1
Clinician-assist ambient documentation. Source mapping: every AI-generated summary element traced back to the source utterance. Audit-and-trust layer over episodic memory. Built on proprietary 1.5M+ medical-encounter dataset.
- ASAPP GenerativeAgent T1
Treats memory as first-class architecture. Captures the digital footprint of every interaction; retrieves preference and history at engagement time. Public example: airline knowing a frequent flyer wants aisle seats with her son — preference-aware, not just history-lookup.
- BenevolentAI T1
Target identification / drug repurposing / mechanism tracing. 85+ data sources, petabyte-scale, rebuilt every few weeks. Wet-lab results re-enter the graph and shift downstream predictions — institutional experimental memory closing a feedback loop.
- Causaly T1
Drug discovery / target identification / causal mechanism tracing. The graph is the memory: 7 years of curated biomedical cause-effect relationships compounding with each new ingestion. Scientific RAG retrieves from a versioned causal substrate.
- Character.ai T1
Chat Memories (user-defined facts), auto-memories for c.ai+ subscribers, pinned memories, in-context retention. PipSqueak 2 model (April 2026) reduces in-conversation drift. Memory Visualization meter forthcoming.