Skip to content

Product

Every human intervention improves the next resolution

When your team steps in, learns what was missing and proposes changes so the next similar query is handled autonomously.
Get a demo See how it works

Mostbusinessesloseknowledgewhentheirbestpeopleleave.keepseveryanswertheyevergave.

When humans step in

The platform analyses what the autonomous system was missing

Three questions drive the analysis: what did the agent attempt, what did the human know, and how can the gap be captured as knowledge or policy. Not a post-mortem -- an automatic extraction that runs on every human-managed conversation.

On resolution1Inbound messageA customer sends a query2Autonomous resolutionThe platform attempts to resolve it3Human managesYour team resolves the query4Learning extractionThe gap is captured5ProposalsKnowledge and policy updates

Structured proposals

Not notes. Specific changes to your knowledge base and policies.

The system drafts proposals with rationale, source evidence, and confidence scores. Two types: new knowledge base entries and policy updates. Each proposal references the specific conversation that surfaced the gap.

Knowledge base entries

New entries drafted from real customer questions. Each one references the conversation that surfaced the gap and includes the evidence for why it matters.

Policy updates

Policies refined from actual business decisions your team made. Compensation thresholds, human review conditions, SLA terms -- captured from practice, not theory.

Proposal structure: conversation context feeds learning agent, which drafts KB proposals and policy proposals with content, rationale, evidence, and confidence, flowing to human review queue

Human judgement

Proposals require approval. Never auto-applied.

Your team reviews each proposal with the full conversation context. Three actions: approve, edit and approve, or reject. Approved changes take effect immediately on the next similar conversation. Rejections are tracked so the system avoids proposing the same change again.

Compounding improvement

Each approved proposal reduces future interventions

Humans spend more time on genuinely complex cases. Complex cases produce higher-quality learning signals. The knowledge base grows more precise over time. After six months: a knowledge base that reflects actual customer questions, policies that match real business decisions, and a declining intervention rate.

Approved updatesOVER TIMEFewerinterventionswith every cycle1Knowledge base & policiesSource of truth for every response2Autonomous resolutionThe platform resolves the query3Human managesYour team resolves the query4Learning extractionGap analysis and draft proposals5Human reviewApprove, edit, or reject

See the learning loop in action

Connect your systems and watch the platform learn from your team

Book a demo