Capture, structure, and recall customer truth across every agent and workflow.
Personize turns scattered customer history into a persistent, governed memory layer so AI agents stay accurate, consistent, and useful across time and tools.
Enterprise-grade accuracy
AI memory fails when it is inconsistent, unstructured, or not grounded in record-level truth. Personize is designed to keep memory accurate across extraction, recall, and ongoing updates.
Memorization
Extract every insight that exists in your data—nothing skipped, nothing lost.
Recall
Surface the right memories at the right time for each task.
Governance
Built-in PII redaction, tiered data access, and full audit trails for compliance.
Record Consistency
Same customer = same facts, no matter how many memories exist.
Agent Consistency
Every agent sees the same truth, every time, across every task.
Deduplication
Same insight, one record — no matter how many sources mention it.
Structured for workflows. Rich for personalization.
Most customer insights belong in structured fields that workflows can act on. The rest belongs in high-signal context that improves relevance.
Schema-Enforced
- •Schema-enforced attributes with types and allowed values
- •Queryable fields for segmentation and filtering
- •Predictable formats for workflow automation
Free-Form
- •Details that don't fit a schema
- •High-signal context that improves relevance
- •The human stuff AI usually forgets
Memory Quality — Where We Stand
We combine an industry-standard benchmark with proprietary experiments to prove — not claim — that our memory works.
LoCoMo Benchmark
LoCoMo (Snap Research, ACL 2024) is the industry's most rigorous benchmark for long-term conversational memory. It tests whether a system can recall specific facts, connect information across separate conversations, understand temporal order, reason beyond what was explicitly said, and know when to say "I don't know" — the way a trusted colleague would over dozens of sessions.
Our overall accuracy is ~75% and improving. We already exceed human-level performance on open-ended inference. Comparable systems score 42–67%.
A detailed paper with final results will accompany our formal launch.
LoCoMo Overall Accuracy
What Only We Measure
Beyond LoCoMo — 13 controlled experiments with no industry equivalent.
Fact Extraction
Across call notes, documents, chat, transcripts, and email.
Combined Recall
Structured + unstructured memory working together.
Cross-Entity Leak
Client A's data never appears in Client B's context.
Governance Routing
Right rules applied to the right situation, every time.
Token Savings
Progressive context reuse in multi-step workflows.
Deduplication
Memory stays lean and accurate as data grows.
Memory Economics
See the difference structured, compressed, intelligently retrieved memory makes to your AI costs.
Traditional Approach
Every interaction re-processes full unstructured context—verbose, redundant, and expensive.
Costs scale linearly with every interaction
Personize Memory
Structured, compressed memories retrieved adaptively—only relevant context, in the right format, at the right depth.
Costs stay flat as knowledge deepens
Dramatically Lower Costs
with higher output quality—because less noise means better answers
Optimized recall, not just storage
Reflection-expanded recall
Personize expands a simple question into a complete, expert-level retrieval plan so agents do not miss what matters.
Guided usage
Return the relevant policies and best practices alongside memory so agents know how to apply context correctly.
Continuous quality
Monitor memory accuracy, detect drift, and tune extraction rules — before it impacts customers.
Delivered to any agent
MCP and API delivery so any AI tool or framework can use the same customer memory.
Turn customer history into action
Capture insights once. Use them across every GTM workflow.