Live assistance
Surface relevant knowledge, required disclosures, recommended actions, and workflow prompts based on live context.
↗AI ENABLED AGENT TOOLS
AI Enabled Agent Tools
Give service and sales teams timely summaries, trusted knowledge, and useful guidance inside the tools they already use.
Enterprise Perspective
Agent assistance is a knowledge, workflow, and evaluation product
Assistance is only as reliable as the policies, procedures, permissions, and customer context it can retrieve. Knowledge quality and provenance are core product requirements. Agents should receive summaries, next actions, drafted responses, and policy checks at the right moment, without moving between disconnected tools. Measure factuality, policy adherence, escalation quality, handling time, resolution, agent adoption, and customer impact. Human accountability remains explicit.
01More confident teams
Capabilities
Augment agents throughout the interaction lifecycle
Automated after-call work
Generate editable summaries, dispositions, follow-ups, and system updates with confidence checks and auditability.
↗Supervisor intelligence
Detect coaching opportunities, emerging issues, quality risk, and knowledge gaps across interactions.
↗How Gatestone works
A grounded path from high-friction work to trusted in-workflow intelligence
Baseline the work
Identify high-volume decisions, failure modes, search effort, after-call work, and moments where expertise is scarce.
Prepare trusted knowledge
Structure approved content, ownership, permissions, freshness rules, citations, and feedback paths for retrieval.
Design the assistance
Define when the system should retrieve, summarize, draft, recommend, ask for confirmation, or escalate.
Connect the workflow
Integrate customer context, CRM, case history, knowledge, and approved actions with least-privilege access.
Evaluate with experts
Test representative cases, edge conditions, policy constraints, and model behavior with frontline and risk specialists.
Operate improvement
Monitor quality and adoption, capture corrections, refresh knowledge, and expand autonomy only when the evidence supports it.
Illustrative scenarios
How the approach could work.
Illustrative service situations with intended outcomes.
Related insights
Ideas for operators building what comes next.
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