Product
One system handles storage, structuring, and resolution.
Your agents and your LLM stay the same.
Live in production
Running today
~200ms retrieval
Global, regardless of scale
Current by next turn
Resolved before your agent reads again
Where it sits
Your Sources
- Conversation streams
- CRM events
- API payloads
- User messages
Cerebell
- Extract
- Structure
- Resolve
- Serve
Your Agents
- Current answers
- Customer scope
- Clean context
- No stale carryover
Integration
Ingest
response = cerebell.ingest( org_id="org_1", entity="entity_42", text="New address is 450 Oak St, moved last week.",)# {# "records_extracted": 2,# "resolved": 1,# "downstream_updated": 3,# }Query
state = cerebell.query( org_id="org_1", entity="entity_42",)# [# {"fact": "Address: 450 Oak St",# "since": "2026-05-31",# "source": "call_4821"},# {"fact": "Plan: Premium",# "since": "2026-03-12",# "source": "crm_sync"}# ]What happens at ingest
New information arrives
“Customer called back. They are now on the enterprise plan and need priority support.”
Records extracted
Plan: Enterprise · Support tier: Priority
Existing state checked
Existing record · Plan: Standard · Support tier: Normal
Verified and resolved
New record confirmed current. Prior record preserved with lineage.
Dependent context updated
Routing, eligibility, personalization, and agent instructions update automatically.
What Cerebell handles
Ingest
Backfill history and real-time conversations through the same write path. Bulk-loaded records stay current as new information arrives.
Extract
Transcripts, notes, tickets, and messages become agent-readable records automatically. No manual profile or schema-building step.
Structure
Every write is checked against existing state before agents read again. Current truth takes precedence; prior records remain available for audit and lookup.
Store & Serve
Agents read the current state by default. The serving layer returns what matters now, so stale context stays out of the primary path.
Guarantees
Current by the next turn
Every write triggers resolution. It completes between turns, so the next time your agent reads, the state is already resolved and clean. No nightly batch. No manual reconciliation.
Verified before applied
Every resolution is checked before it takes effect. If the new information doesn't clearly replace the old, both records coexist. Nothing is overwritten silently.
Nothing deleted
Old records preserved with full lineage: what they said, when they were active, what replaced them. Queryable.
Isolated by default
Per-org and per-entity isolation, native to the architecture and validated with zero retrieval degradation. One organization's state never reaches another.
Sub-second for recognized patterns
Recurring resolution patterns are cached. Previously verified patterns resolve on the fast path automatically. Gets faster with use, no tuning required.
Deployment
Org- and entity-level isolation
Native to the architecture, not bolted on.
Any LLM provider
Your keys, your model. No provider lock-in.
On-prem or cloud
Deploy in your environment or ours.
No migration
No schema changes. No data import required to start.
What Cerebell isn't
Not an agent framework
Feeds your agents correct context.
Not a fine-tuning service
No model training. Your LLM is unchanged.
Not a batch pipeline
Resolution is triggered per write, not on a schedule.
Not passive storage
Cerebell is the agent-facing state layer: it stores records, keeps them current, and serves the resolved state your agents read.
See it run on your data.
Demo