How it works

Your entire CX team. In AI.

One manager. Three specialists. Resolves 50% of tickets in 30 days. at $0.30 per conversation vs. $2–$10 for a human. Or every dollar back.

Boss AI

CX Manager AI

Sets up agents, updates policies, monitors quality

Customer AI

Your frontline. Resolves conversations, follows SOPs, escalates when unsure.

70–80%

autonomous resolution

Agent AI

Your team's copilot. Auto-research, draft replies, execute actions.

5x

agent throughput

QA Agent

Independent evaluator. QCs every conversation, sends findings back to the CX Manager.

100%

conversations reviewed

Day zero · the boss AI builds the team

How CX Manager sets up your AI team.

Before Customer AI takes a single ticket, the boss AI spends the first 20 minutes ingesting your past tickets, your Trustpilot reviews, your Reddit threads, and your help center. Then it drafts SOPs for Customer AI, configures escalation logic for Agent AI, and writes the quality rubric for the QA Agent. By the time the team takes its first ticket, every agent below already knows your business as well as your best human.

app.richpanel.com/cx-manager

CX Manager · researching your business

Importing from your existing helpdesk

386,412past tickets imported

#2847Where is my order?Tracking

#2846Pause my next boxSubscription

#2845Refund please, arrived damagedRefund

#2844Wrong flavor in this deliveryLogistics

#2843Cancel my subscriptionRetention

Analyzing Trustpilot reviews

1,247reviews · brand voice extracted

★★★★★ 4.6 average rating

Sarah M. ★★★★★

“Worth every penny, felt like talking to a real person who actually listens.”

Marcus T. ★★★★★

“Hands-down the best support I’ve ever had. Refund processed in two minutes.”

Scanning Reddit threads

84threads · objections + pain points

u/anon_2847 · r/category

“Anyone else find the chocolate flavor too earthy? Considering switching brands…”

▲ 142 upvotes

u/wellness_dad · r/category

“Pro tip: their vanilla bean blend is way better than the original chocolate.”

▲ 87 upvotes

u/jamielee · r/category

“Is the auto-renew confusing for anyone else? Got billed twice last month.”

▲ 53 upvotes

Ingesting your help center & product guides

24articles · all your policies

📦Returns & refunds

🚚Shipping windows

🔄Subscription pause

💳Payment issues

🥦Product FAQ

⏳Order changes

Four research sources, twelve seconds. The briefing every agent below reads before its first ticket.

Under the hood

How we make "never hallucinate" actually true.

Other AI tools hand-wave about safety. Here's what we actually do:

Strict evals before go-live

Every model goes through thousands of battle-tested simulations before serving a single customer.

Second AI pass for quality

A separate QA layer reviews every response before it reaches your customer. Two systems checking each other.

Deterministic tool execution

The AI decides what to do. A separate system executes it. Never touches PII or credentials.

Escalation, not guessing

When uncertain, it routes to a human. Every time. No "best guesses" that cost you a customer.

app.richpanel.com/customize/tools

Tool permissions, every tool the AI can touch, with explicit allow or require-approval per action

Every tool the AI can touch, read-only or write, with explicit allow / require-approval rules.

For cautious buyers

Start in copilot mode. Go autonomous when you're ready.

Three modes, switched per channel on your timeline. Copilot: AI drafts, your team approves before send. Supervised autopilot: AI sends, your team reviews after. Autonomous: AI handles, escalates edge cases. Most teams ramp from copilot to autopilot in 2–3 weeks, channel by channel.

Collaborative mode three-stage rollout in Richpanel: Copilot (AI drafts, human approves), Supervised Autopilot (AI sends, human reviews), Autonomous (AI handles, escalates edge cases). Toggle per channel.

Every dollar of risk stays on us, not you. 50% resolution in 30 days, guaranteed. Or every dollar back.

For portfolio operators

One AI team. Every brand in your portfolio.

Two in five Richpanel customers run multiple brands or stores. The CX Manager builds a per-brand voice profile, per-brand SOPs, per-brand escalation logic for each one. Same team, same harness, same QA layer. Different brands. No re-training, no separate accounts.

SwiftFin Brand 1

47

SOPs · trained

Apparel. CX Manager ingested past tickets, reviews, and product catalog. Wired Shopify + Recharge + Loop. Own brand voice.

LuxeSkin Brand 2

62

SOPs · trained

Skincare. Different voice, different policies, different tools. Same QA harness reviews every reply against this brand's rubric.

FitNation Brand 3

12/wk

CX-Manager auto-updates

Fitness. The CX Manager ships SOP tweaks every week from yesterday's conversations. Per-brand, no shared bleed-through.

Real account in this dataset: 7 brands. 50 agents. 1M+ historical tickets ingested by one CX Manager. One AI team across all of them.

Continuous Learning

Your AI gets smarter from every conversation.

Other AI tools launch, then flatline. Richpanel learns from every session. Autonomously and from your team's corrections. Every correction, every SOP gap, every missing integration is captured, surfaced, and turned into new behavior the next week.

01

AI Missteps

"Had instructions, didn't follow them."

Your SOPs were clear. The AI just missed the mark. We flag the deviation so you can monitor or retrain.

02

SOP Gaps

"Customer asked something with no playbook."

No SOP covers this scenario. We surface the gap and suggest what to write, so next time the AI knows.

03

Missing Tools

"Agent checked a system the AI can't access."

Your agents had to look something up manually. We flag the integration opportunity so you can extend your AI's reach.

app.richpanel.com/customize/learning

Learning insights: AI found 23 unnecessary escalations, one click to wire a tool and gain +9% auto-resolution

Where every learning lands: tracked, scored, and ready to apply.

Detection

Two signal sources. Zero blind spots.

Agent-corrected

Your team rejects, modifies, or overrides the AI. Each correction becomes a learning signal.

Auto-detected

Our quality engine spots customer friction, resolution gaps, and hallucination risks, even when no human is watching.

The loop

Detect. Surface. Fix. Measure.

Detect

→

Surface

→

Fix

→

Measure

Every insight is tracked for 14 days after implementation. You see before/after signal counts. No guesswork.

Confidence scoring built in.

More signals of the same type = higher confidence. A pattern that shows up across multiple sessions and agents is real. A one-off is noise. You always know how sure we are.

Low

High

Single signal Multiple signals

Two products, one platform

You've seen the AI team. Now see the helpdesk underneath.

Unified inbox, every channel, macros, SLAs, real reporting, multi-brand. The platform humans operate when AI escalates, and the same surface your AI agents work from.

See the Helpdesk →

The payback

You've seen how it works. Now see what it saves.

Plug in your numbers. Every dollar, every model, every resolution rate is transparent.

See pricing & ROI →