The Spotlight: Autonomous Support

Ideas, playbooks, and real lessons from building autonomous support. Learn what works, what breaks, and how top teams scale CX with AI.

Product

Automated Ticket Resolution | True Resolution vs. Deflection

Evaluate AI agents for autonomous resolution, not just deflection. See benchmarks, platform criteria, and what separates real resolution from productivity tools

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Engineering

Buy vs Build AI Customer Support | Clear Framework

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Product

LLM vs SLM for AI Agents: Notch’s Hybrid Playbook

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Product

Enterprise AI Supervision

See how Notch delivers enterprise-grade AI supervision with deterministic guardrails, full audit trails, RBAC, SSO/SCIM, versioned rollouts and rollback, encr..

Insights

AI Agents vs Agentic AI

Vendor demos blur "AI agents" and "agentic AI," but the difference decides whether you automate replies or resolve customer issues end-to-end. Learn the 2026...

Product

RBAC for AI Agents

AI agents are now acting autonomously across enterprise systems. Learn how Role-Based Access Control (RBAC) ensures safe automation, limits risk, and enables...

Platform

The Future of AI in Customer Service

How AI is transforming customer service into proactive, autonomous resolution. Discover what separates productivity tools from platforms that really solve pr...

Insights

Customer Service AI Metrics

Learn which AI customer service metrics actually matter in 2026, from Automated Resolution Rate to Agent Score, and why tracking wrong KPIs hurts your support.

Insights

How AI Is Transforming Claims Handling

How can AI transform FNOL from a friction-filled bottleneck into an automated claims launchpad? What separates true FNOL resolution from basic automation?

Product

Why Using AI Insurance Claims Processing Makes Sense

Learn why AI insurance claims processing delivers faster resolution, cost savings, and higher straight-through rates, and how to evaluate platforms...

Product

Versioning AI Agents in Production

Notch treats AI agents like software: branch changes, run smart conversational tests, validate updates, and merge to production. Every configuration is versi...

Platform

Escalation Pathways in Conversational AI

A practical guide for regulated industries (insurance, healthcare, banking) on when conversational AI should reply safely, offer handover, or force escalation.

Platform

Guardrails and Escalations for AI Agents in Regulated Industries

How we build layered safety systems in insurance and finance. Insurance and financial services operate inside dense rulebooks, Here is how Notch builds layer...

Engineering

Challenges & Risks of AI in Insurance Customer Support

The key challenges insurers face and the guardrails that distinguish reliable automation from liability. Insurance AI carries unique risks from claims handli...

Product

Conversational AI in Insurance (Evaluation Guide)

Framework for evaluating conversational AI platforms in insurance. Compare resolution rates, compliance capabilities, and integration requirements for automat..

Voice

Tackling Turn Detection in Voice AI: Overcoming Noise and Interruption Challenges

Turn detection is a key challenge. This article discusses a practical, engineer-focused approach to voice AI detection.

Company

60 AI Disruptors

We’re excited to share that Notch has been named one of the AI Disruptors 60, a new honoree list curated by Greenfield Partners and unveiled live at TechCrunch.

Company

We Just Opened Notch’s New York Office - Here’s Why That Matters

Notch opens a new office in New York to accelerate AI support solutions in finance, insurance, and travel.

Platform

What Does Flexibility Mean in CX AI Platforms? How to Evaluate True Flexibility

"Flexibility" means different things to different vendors, and what works brilliantly for one organization may require significant adaptation for another.

Engineering

Buy vs Build AI Customer Support | Clear Framework

Should you build your own AI agents? Notch's CLEAR framework breaks it down across confidence and compliance, edge cases, effort, TCO, and real-world mistakes.

Product

LLM vs SLM for AI Agents: Notch’s Hybrid Playbook

Notch product team breaks down why SLMs are ideal for repeatable agent tasks and how a hybrid SLM plus LLM pattern improves speed, cost, and reliability.

Product

Automated Ticket Resolution | True Resolution vs. Deflection

Evaluate AI agents for autonomous resolution, not just deflection. See benchmarks, platform criteria, and what separates real resolution from productivity tools

OUR SOLUTIONS

The Support AI Library

Gaming

Built for fast-paced support where every second counts. Resolves rage-quit moments, detects player intent, and keeps gamers in the game.

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SaaS

Understands your product, plans, and workflows. Automates onboarding, upgrades, and tech issues without ever missing context.

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eCommerce

Tailored for high-volume retail. Resolves order issues, automates returns, and personalizes product help faster than humanly possible.

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Built for You

For Support Teams

Playbooks and practical lessons for running support with AI - deflection, resolution, QA, escalations, and day to day workflows that keep quality high.

EXPLORE CONTENT FOR SUPPORT

For Engineers

Deep dives on building reliable support agents - integrations, tool calling, guardrails, evals, monitoring, and shipping safely in production.

EXPLORE CONTENT FOR ENGINEERING

For Innovation Leaders

Strategic guidance on adopting autonomous support - ROI, risk, security, change management, and how to scale automation across the business.

EXPLORE CONTENT FOR LEADERSHIP

The Spotlight: Autonomous Support

Automated Ticket Resolution | True Resolution vs. Deflection

Buy vs Build AI Customer Support | Clear Framework

LLM vs SLM for AI Agents: Notch’s Hybrid Playbook