Pace.ai

Use case · B2B software and SaaS

Cut churn by 21% and let every CSM handle 34% more customers.

Pace reads usage, tickets, CRM and conversations across every account, flags slipping customers with the likely cause, and runs the follow-ups and renewal check-ins. CS teams report 21% less churn and 34% more customers per CSM (vendor-reported market benchmarks).

What we automate
HubSpot · Intercom · PostHogSlack · email · WhatsAppAccount managers · CS · founders
HomeThursday · 17 September

Thursday morning

Last opened Monday

1 Defended figures

ARR$18.6MDirect $11.2M · Partner $7.4M
Paying accounts312+9 new · −4 churned · 3 in grace
Net new ARR 30d+$214KExpansion $340K · Contraction −$126K
At risk17$1.84M ARR exposed · 6 unreviewed

2 What changed since Monday 14 events

Invoice failed — $24K, second attempt · 2h agoBillingVerdant Co →
Active seats fell 84 → 61 over 7 days · 1d agoApp DBNorthstar Cloud →
Contradiction — CRM says Enterprise, billing charges Growth · needs a decisionCRM + BillingCorvid Analytics →
New signup — upgraded to Growth within 3 days · 1d agoProductKettle Labs →
Renewal conversation due in 14 days · owner MariamCRMMeridian Health →

Illustrative product interface

5–25×more expensive to acquire a new customer than to keep an existing oneHarvard Business Review
25–95%profit increase from a 5-point improvement in retentionBain & Company / HBR
103%median net revenue retention for B2B SaaS, flat year over yearSaaS Capital, 1,000+ companies
91%median gross revenue retention; the best teams hold 100%SaaS Capital

Today

Every account manager sees their own accounts. Nobody sees the pattern.

The same issue, raised by twelve customers, looks like twelve tickets. Nobody notices it is one problem until a big account leaves.

A usage drop shows up at renewal. By then the champion has left or the team has moved to something else.

Health scores say “green” until they say “churned”. They are built on logins, not on what customers actually say in tickets, emails and calls.

Follow-ups depend on who is busy. Check-ins, QBR prep and renewal outreach happen when there is time, which is never.

With Pace

One view across every customer, and an agent that acts on it.

Issues grouped across all customers. Tickets, chats, emails and call notes clustered into themes, with the accounts and revenue attached to each.

Usage drops explained, not just flagged. The agent connects the drop to what it finds: an open bug, a missing feature, a champion who left, or a seasonal dip.

Risk from evidence. Sentiment in conversations, response times, open items and usage combined, with the evidence one click away.

Follow-ups that happen on time. Check-ins, renewal outreach and win-back messages drafted and sent on schedule, with your approval where it matters.

Solutions

Eight ways in. One intelligence foundation.

Start with the customer problem that carries the strongest commercial value. Every solution shares the same customer context and can expand as your operating model matures.

01

Prevent Customer Churn

Detect churn early, explain the risk, and coordinate action before recurring revenue is lost.

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02

Grow Existing Accounts

Find and qualify growth opportunities using adoption, need, and account change.

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03

Increase Product Adoption

Guide customers toward value and intervene when meaningful adoption stalls or declines.

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04

Build Revenue Generating Features

Prioritize features and fixes using customer evidence, product behavior, and revenue impact.

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05

Scale Customer Success

Give every account the right attention without growing headcount at the same rate.

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06

Unify Customer Intelligence

Turn fragmented structured and unstructured data into a complete, queryable customer view.

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07

Make AI Commercially Useful

Build governed agents that understand the customer and complete commercially useful work.

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08

Custom AI Deployment

Design and deploy controlled AI capabilities around complex enterprise operations.

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What we automate

Two agents on one understanding of your customers.

Success

Customer success agent

Connected to your CRM, support desk, product analytics and communication channels, it maintains a living picture of every account and acts on changes.

  • Common issues across customers, ranked by accounts and ARR affected
  • Usage-drop alerts with the probable cause and suggested action
  • Churn risk scored from conversations, tickets and usage together
  • Renewal and check-in outreach on schedule, in your voice
  • Weekly brief per account manager: what changed, what to do
  • Product feedback themes sent to your product team with evidence
Sales

AI sales agent

Answers inbound leads from your website, pricing page and campaigns on chat, WhatsApp or a call, qualifies them on company size, use case and timing, books the demo, and follows up with the ones who go quiet.

  • Qualification against your ICP
  • Demo booked in the right rep's calendar
  • Follow-up sequence for unresponsive leads
  • HubSpot updated after every conversation
Support

Customer support agent (optional)

Resolves routine tickets from your documentation and past answers, routes the rest with full context, and feeds every conversation into the same picture the success agent uses.

The platform

A trusted intelligence layer for people and agents.

Every solution above runs on the same five capabilities. They connect the evidence, the recommendation, the human decision, the system action, and the final outcome.

01

Customer graph

A shared model of accounts, people, products, outcomes, needs, commitments, signals, and commercial events.

02

Signal intelligence

Continuous detection of meaningful changes across behavior, relationship, service, and commercial context.

03

Evidence linked reasoning

Every recommendation stays connected to the source evidence, confidence, and relevant business rules.

04

Governed action

Automations and agents use approved tools, human decision points, permissions, and complete audit history.

05

Outcome learning

Retention, expansion, adoption, capacity, and product results improve future models and playbooks.

Market benchmarks

What the market reports for this kind of automation.

Market benchmarks for customer-success and customer-intelligence automation. These are vendor-reported figures from published market benchmarks, shown so you know what a good pilot should aim for.

Reported resultWhat was measured
95%accuracy identifying churn risk from conversation signals, tested against two years of churn history; forecast error cut from 40% to under 5%
54%reduction in at-risk revenue, and 13× more accounts covered per CSM
34%more customers per CSM, 21% less churn, 300+ hours saved per person per year
40%fewer support tickets after clustering customer feedback into themes and fixing the top ones
76%of support conversations resolved by an AI agent, averaged across 12,000+ companies
20%churn reduction comparing first and second half of a year on a CS platform

Pilot target we set with you: every account with a usage drop over 20% flagged within 48 hours with a cause, the top ten cross-customer issues with ARR attached in week two, and 100% of renewals due in 90 days with an outreach on record.

Worked example

A SaaS company with 300 accounts, $6M ARR and four account managers.

Without Pace

Accounts reviewed each month per AM
~30 of 75
Usage drops noticed before renewal
some
Cross-customer issues visible
no
Gross revenue retention
90%
ARR lost to churn per year
$600K

With the success agent

Accounts monitored continuously
300 of 300
Usage drops flagged with a cause
within 48h
Cross-customer issues ranked by ARR
weekly
Gross revenue retention
93–95%
ARR lost to churn per year
$300–420K

Illustrative figures for the demo. A 3 to 5 point GRR improvement sits at the conservative end of the published results above.

Works with

Your stack, read in place.

HubSpotSalesforceIntercomZendeskPostHogMixpanelSlackMicrosoft TeamsWhatsAppGmail · OutlookZoom · MeetStripe

Questions SaaS teams ask

Before you book.

01Why not connect our data to a general AI assistant and ask it questions?+

A chat assistant answers one question at a time, from whatever you paste in. Pace keeps a structured, always-current model of every account, watches for changes on its own, and acts on them. The question “which customers are at risk and why” is answered before you ask it.

02Does Pace keep a copy of our customer data?+

No standing copy. Pace connects to your systems with scoped access and keeps only the working index it needs to detect changes. You can see and delete it at any time.

03Our tech team is busy. How much of their time does this take?+

Usually a few hours: approving integrations and confirming access scopes. No SDK, no data pipeline to build.

04Can we start with just the issue-clustering and usage-drop alerts?+

Yes. Most teams start there, then add outreach once they trust the signals.

Book a demo

We will show you your own accounts' patterns on the call.

Connect a support and product-analytics export, or just bring your questions. 30 minutes.

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