Frequently asked questions
Is Living Intelligence fully autonomous AI?
No. It supports continuous learning and proactive proposals, while high-impact actions remain under permissions, limits, review, and rollback rules.
Does memory evolution keep every piece of data?
No. Follow data minimization and retain only authorized information that is useful to an agreed business purpose.
How are vector memory and knowledge graphs different?
Vector memory finds semantically related content. Knowledge graphs express relationships among goals, decisions, actions, and outcomes.
Can pattern recognition be wrong?
Yes. It should present explainable prompts and evidence, then improve through thresholds, review, and calibration.
Can the system change production settings automatically?
It should not by default. Production configuration, permissions, finance, and customer commitments require authorization and review.
Does communication adaptation mean collecting private feelings?
No. Use explicit feedback and interaction rhythm carefully; do not treat sensitive inference as fact or use it for high-impact decisions.
How do we measure whether evolution is working?
Look for clearer evidence, fewer repeated issues, faster review, earlier detection, and safer rollback.
Where should a team start?
Begin with a frequent, bounded workflow such as customer questions, content operations, or documented operational review.
Does it replace people?
The purpose is to reduce repetitive work and improve coordination. Responsible people retain professional judgment and accountability.
What happens when knowledge becomes stale?
Use validation dates, expiration, outcome feedback, and weight decay to reduce influence and flag it for review.
Can it connect with CRM, websites, or workflows?
Yes, after planning data flow, fields, permissions, failure handling, and human handoff points.
Do public pages expose internal architecture?
No. Public pages explain principles only and must never include secrets, customer records, internal prompts, or infrastructure details.
Implementation guidance
Start deep and stable in one scenario, then extend to more systems and teams.
Week one
Define pilot scope, sources, permissions, and success metrics.
Month one
Build baseline memory, human review, and reporting rhythm.
Afterward
Add insight, proposals, and controlled execution based on evidence.
Give every team a continuously improving AI company system.
Begin with one bounded, measurable scenario. Build memory, insight, and controlled evolution gradually.
Bring Living Intelligence into real work.
Book a demo and find the right first scenario for your team.