Pacer AI helps Sales Leaders
build financial models
for Forecasting
Reconciled to Finance, Built for Operations, On-Pace to Plan.
Pacer AI exists to take the guesswork out of making investments to grow revenue.
Finance teams report Revenue. Sales teams generate Revenue.
Sales needs a model that shows how revenue is generated.
Sales needs a model that shows how revenue is generated.
Modeling how revenue is generated in the company — by market and by segment — removes capital allocation guesswork and creates alignment.
Operational data in. Revenue intelligence out.
Connect your systems. Pacer AI does the rest — on Microsoft’s enterprise stack, so your data never leaves your control.
Revenue Models built for Sales Activity, Behaviors and Context
Revenue Mix Modeling
- Variable Revenue (Professional Services)
- Recurring Revenue
- Transaction Revenue (Fintech)
- Usage-based Revenue (AI)
ARR Waterfall Modeling
- Churn, Product Churn, Downsell
- New, Upsell, Product Cross-sell
- Net New ARR, Gross & Net Retention
GTM Planning
- by Revenue Mix
- by Sales Motions
- by Market
- by Segment
Sales-Led Growth
- Audience or Prospects
- Lead
- Opportunities
- Sales Cycles
- Close Rates
Product-Led Growth
- Acquisition
- Activation
- Monetization
Capacity Modeling
- Headcount plan to target
- Rep ramp and productivity
- Attrition and backfill
Coverage Modeling
- Territory design
- Pipeline coverage ratio
- Account and segment coverage
Compensation & Quota Modeling
- Quota setting
- Territory and comp design
- Commission and payout
Master the financial narrative of the revenue growth plan
Direct questions require deterministic skills with canonical definitions. Pacer AI builds a semantic model, with defined calculations — so you get the same answer for the same question every time.
Revenue financial modeling requires a mix of data engineering, RevOps expertise, strategic finance experience, and sales strategy.
Built in days. Trusted in the boardroom.
“I spent over a million dollars on a data team and tools, and they couldn’t build an ARR Snowball like this in 12 months — and you did it in days.”
Adobe × Semrush — an M&A read
A public worked example of the Pacer AI diligence lens applied to a real transaction — ARR quality, retention, and the movement bridge.
Read the case study →Connects to the systems you already run
Pacer AI reads from your CRM, billing, ERP, HRIS, and warehouse — no rip-and-replace, no data leaving your tenant.
CRM
- Salesforce
- HubSpot
Billing
- Stripe
- Zuora
- Chargebee
ERP
- NetSuite
- Sage Intacct
- QuickBooks
HRIS
- Rippling
- Workday
- ADP
Warehouse
- Snowflake
- Databricks
- BigQuery
Your data never leaves your control
Built on Microsoft Fabric — your cloud, your tenant. Access is scoped, authenticated, and audited.
Two-factor authentication. Every login is protected with 2FA through your identity provider.
Row-level security. Access is scoped so each person sees only the accounts, segments, and metrics they should.
Built by an operator who has sat in the diligence seat
Former PwC M&A advisor, search-fund operator, and West Point graduate. $25Bn+ of technology M&A diligence and 50+ ARR waterfalls built for SaaS operators and their sponsors — the same models Pacer AI now builds in days.
An agent-and-human-in-the-loop approach
The Financial Modeling Agent accelerates the build — but we recommend working with an experienced advisor for model reviews, strategy considerations, and scenario planning. Every engagement comes with a human in the loop: dedicated office hours, Slack support, and ad-hoc meeting support. After implementation you run your own financial modeling, sales planning, forecasting, analytics, and reporting — DIY, with an expert alongside you the whole way.
Plan buy-in. Hit the number. Own the story.
A plan the room signs off on
Every model reconciles to Finance, so your growth plan survives the CFO, the board, and the sponsor — instead of being rewritten by someone else.
Days, not months
From kickoff to a reconciled model in days — not a 6–12 month internal build that is stale on arrival.
Senior modeling, on demand
Diligence-grade revenue modeling the week you need it — without hiring a data team or standing up a BI stack to keep it running.
Pricing Model
Pacer AI uses an alignment-based pricing model to align Pacer AI with its clients’ goals.
Discuss PricingFrequently Asked Questions
What size companies do you work with?
Pacer AI works with companies that have at least $10M in recurring or contracted revenue — including non-tech companies. Alongside B2B SaaS, we work with payroll providers, commercial and residential service companies, and healthcare companies.
How long does the initial setup take?
Most engagements go from kickoff to board-ready output in days, not months. The data cube build typically takes 2–4 weeks depending on source system complexity.
What systems do you connect to?
CRM (Salesforce, HubSpot), billing (Stripe, Zuora, Chargebee), ERP (NetSuite, Sage Intacct), and data warehouses (Snowflake, Databricks, BigQuery).
How is this different from FP&A tools?
FP&A tools start with the GL. We start with transactional customer data to build M&A-grade ARR decomposition, cohort retention, and expansion analytics that FP&A tools cannot produce.
What does pricing look like?
Pacer AI uses an alignment-based pricing model to align Pacer AI with its clients’ goals.
Do you work with PE funds directly?
Yes. We work with operating partners and deal teams on both pre-close diligence and post-close value creation across the portfolio.
Who should own revenue forecasting — RevOps or Finance?
Ownership sits with RevOps; the definitions sit with Finance. The common failure is a forecast owned by RevOps that never reconciles to Finance’s reported ARR. Pacer AI recommends building the forecast off the same governed data the finance number comes from, so there is one set of definitions and one answer.
Why don’t our CRM and finance ARR numbers match?
CRM and finance ARR numbers don’t match because they measure two different events: the CRM records bookings when a deal closes, while Finance records revenue as it is recognized — net of contraction, churn, and timing. Without a defined bridge between bookings and recognized revenue, both numbers are correct and neither reconciles. Pacer AI builds that bridge at the account-product grain, so pipeline ties to reported ARR to the dollar.
What does a VP of Revenue Operations actually own?
A VP of Revenue Operations owns four things: the revenue forecast and pipeline cadence, the governed revenue KPI set, quota and territory capacity, and the GTM tech stack. Across the RevOps job descriptions we’ve analyzed, all four are owned by a person, not produced by a system — which is why the role turns over.
Is Pacer AI just software, or do you help?
Both. The agent does the heavy modeling, and every engagement includes founder-led service, done-with-you or done-for-you, so the model is built, reconciled, and defended by a person. After implementation you run your own modeling, planning, forecasting, and reporting — DIY — with a human in the loop whenever you need one: dedicated office hours, Slack support, and ad-hoc meetings.
Schedule a diagnostic review
Model, Plan, Track Pace to Target.