Vic.ai Financial Model
Fintech Startup Financials (Free Excel Download)
AI platform that automates accounts payable and broader accounting workflows for accounting firms and mid-market enterprise finance departments
professionals from Deloitte
Used by professionals from
About this model
Vic.ai automates accounts payable and broader accounting workflows for accounting firms and mid-market enterprise finance departments. Its AI-driven product reduces manual invoice work while giving accounting firms a way to serve multiple clients more efficiently.
The company sells through both outsourced accounting firms and direct enterprise accounts. Its reported MRR cohorts, strong net revenue retention, and zero historical churn indicate a subscription business where account expansion is a major growth lever.
The model should forecast accounting-firm and enterprise logos separately, with distinct ACVs, implementation periods, new ARR, expansion, and churn. Invoice-volume or usage pricing can be an overlay if validated, while sales capacity and customer concentration should remain visible in the ARR bridge.
A turnkey financial model
Live formulas, no hardcoded values
Outputs are driven by live formulas, so the workbook updates from its assumptions instead of relying on hardcoded results.
All assumptions in one tab
Inputs are clearly marked in the Assumptions tab and separated from calculations, making it clear what to change and what to leave intact.
Statements always balancing
For integrated-statement models, the balance sheet, cash flow, and supporting schedules tie through properly.
Distinct schedules for clarity
Debt, working capital, taxes, and cash flow can get messy quickly. We group calculations in clear schedules, not across disconnected tabs.
No hidden macros or external links
There are no unexplained external workbook links or macros to undermine auditability or portability.
Changes flow through the model
Update a key driver and see the impact carry through the forecast, financing, and return outputs. We never use hardcoded numbers in formulas.
About Vic.ai
vic.ai
How to build a detailed financial model for Vic.ai
A complete walkthrough of the business, drivers, and assumptions behind the downloadable Vic.ai model - distilled from its pitch deck and publicly available information.
Product & value proposition
- AI engine ingests invoices/documents, predicts coding/categorisation, routes exceptions to a human review interface
- Shifts accountant workflow from transaction entry to exception handling; improves speed, accuracy, and resource utilisation
- Perpetual learning loop: client usage trains the model, improving predictions across all clients - a classic network-effect data flywheel
- Integrations: Intacct, Dynamics GP, 247office, NetSuite, QuickBooks Online, Bill.com
- Beachhead: Accounts Payable automation; roadmap expands into accruals, reconciliation, revenue, bank/card transactions, expense reports, month-close
Market
- US accounting services total addressable market: ~$200bn
- Internal Accounting Spend: ~$125bn
- Bookkeeping Services: ~$78bn
- Accounts Payable (beachhead): ~$35bn
- Auditing: ~$100bn
- Accounting Software: ~$11bn
- T&E: ~$2.5bn
- B2B Payments (long-term adjacency): ~$1 trillion
Revenue model
- Subscription / MRR-based model implied by the MRR cohort table and ARR reference
- Pricing basis not explicitly stated (likely per-invoice volume or per-seat/firm); per-invoice transaction pricing is common in AP automation at this stage - requires confirmation
- Channel 1: Inbound marketing → outsourced accounting firms (resellers who deploy Vic.ai to their clients)
- Channel 2: Direct sales → enterprise clients with $100M–$1B revenue
- Accounting firms act as multipliers: one firm brings multiple end-clients onto the platform
Traction & metrics
- ARR target: "$x MM ARR by end of this year" - exact figure redacted in deck
- Churn: 0% (stated as zero churn)
- Penetration: 4 of top 10 accounting firms using the platform
- MRR Retention Cohort (Sep-18 → Apr-19) - all figures are % of cohort's starting MRR:
- Sep-18 cohort: 100% → 152% → 182% → 208% → 399% → 477% → 485% → 750% (Apr-19)
- Oct-18 cohort: 100% → 125% → 125% → 175% → 175% → 215% → 215%
- Nov-18 cohort: 100% → 121% → 174% → 187% → 186% → 210%
- Dec-18 cohort: no data (dashes across all months)
- Jan-19 cohort: 100% → 104% → 136% → 157%
- Feb-19 cohort: 100% → 191% → 450%
- Mar-19 cohort: 100% → 112%
- Apr-19 cohort: 100%
- Dataset scale: 40,000 companies' cloud accounting data; 200M+ financial documents; 10B+ ERP data points
- Data moat chart shows "total verified cost lines in dataset" growing steeply Jul-18 → May-19, with step-change inflections; axis values not legible
- 88% of accountants feel pressure to automate (market stat, no source cited)
Unit economics
- Net Revenue Retention: implied >100% across all cohorts (expansion MRR dominates - no cohort declines below starting MRR, Sep-18 cohort reaches 750%)
Competition / moat
- Competitive landscape: Not explicitly shown (no comps slide)
- Stated moat: proprietary dataset - 200M+ financial documents + 10B ERP data points acquired via partnership (partner redacted), plus ongoing daily usage accumulation
- Learning-loop moat: each accountant interaction enriches the model across all clients, making it harder for new entrants to replicate
- Positioning vs. offshore services and other automation threats: accounting firms use Vic.ai to protect their business model
Team & funding ask / use of funds
- Alexander Hagerup - Co-Founder & CEO; built KeepItSafe (cloud backup, exit to NASDAQ:JCOM 2014)
- Kristoffer Røil - Co-Founder & COO; grew 24SevenOffice 7x in 3 years leading to Stockholm IPO
- Rune Løyning - Co-Founder & CTO; 10 years CTO experience, big data & applied ML
- Antti Puurula - Head of ML; PhD AI, 10 years applied ML
- Joerg Joergensen - CFO; 15 years CFO experience, prior exit to NASDAQ:EQIX
- Neil Jacobstein - AI Strategy; AI/Robotics chair at Singularity University, Stanford MediaX
- Prior exits: KeepItSafe → JCOM; 24SevenOffice → Stockholm IPO
Recommended financial model
- Archetype + why: SaaS ARR / MRR model with account-level expansion revenue. The business is subscription-based, has zero historical churn, strong net revenue retention (NRR well above 100%), and two distinct customer segments (firms vs. direct enterprise) with different ACV profiles. A standard SaaS ARR waterfall (new ARR + expansion − churn) best captures the dynamics shown in the cohort table.
- Forecast horizon & granularity: 36 months monthly (Year 1–2 monthly detail; Year 3 annual summary). Series A audience will want to see path to next round milestone, typically 18–24 months of cash visibility.
- Key drivers & assumptions (each tagged):
- Starting ARR: redacted - mark as an input cell for the client to fill
- Number of accounting firm customers (cohort starts per month): model as a ramp from current base, growing ~15–25% MoM in Year 1, slowing to ~8–12% MoM in Year 2–3; calibrate to "4 of top 10 firms" as penetration anchor
- Number of direct enterprise customers: smaller initial cohort; direct sales cycle 3–6 months; ramp begins ~Q2 of model
- Average MRR per firm client at signing: $2,000–$5,000/month per firm (mid-market AP automation benchmarks); requires client input
- Average MRR per direct enterprise client: $5,000–$15,000/month; requires client input
- Net Revenue Retention (NRR): Sep-18 cohort at 750% after 7 months implies extreme expansion; model conservatively at 130–150% NRR annually for base case (firms upsell additional client seats / modules)
- Gross churn rate: 0% historical; model 0% churn in base, 3–5% in bear (stress test)
- Gross margin: 70–75% (cloud-hosted AI SaaS with infrastructure and ML compute costs; typical for vertical SaaS at this stage)
- S&M spend: 40–50% of revenue in Year 1 declining to 30–35% by Year 3 as inbound + firm-channel scales
- R&D spend: 25–35% of revenue (heavy ML investment)
- G&A: 10–15% of revenue
- Headcount growth: engineering/ML-heavy; model separately from revenue-linked spend
- Data moat cost: initial dataset acquired via partnership (redacted, likely non-recurring); ongoing data cost marginal (embedded in platform usage)
- Scenarios (Base / Bull / Bear - which variables flex):
- Base: NRR 140%, new logos per month at modelled ramp, gross margin 72%
- Bull: NRR 175% (closer to Sep-18 cohort trajectory), faster logo adds (+30% vs. base), gross margin 75%
- Bear: NRR 110%, logo growth stalls at half-rate, churn 5%, gross margin 65% (infrastructure pressure)
- Required sheets / outputs:
- Assumptions - all drivers in one place with scenario toggle (Base/Bull/Bear)
- MRR Waterfall - new MRR, expansion MRR, churned MRR, net new MRR, ending MRR/ARR by cohort and in aggregate
- Cohort Table - mirror the deck's retention table structure; model 12–24 cohorts forward
- P&L - Revenue, COGS, Gross Profit, S&M, R&D, G&A, EBITDA, Net Income (monthly)
- Headcount - by department, linked to opex
- Cash & Runway - opening cash, burn, ending cash; flag months-of-runway (critical for Series A narrative)
- KPI Summary - ARR, MRR, NRR, LTV/CAC (once CAC inputs provided), logo count, gross margin %
- Dashboard - ARR bridge waterfall chart, MRR cohort heatmap, burn/runway chart
Frequently asked
Is the Vic.ai financial model free?+
Yes. The Vic.ai model is a free Excel (.xlsx) download with live formulas. Sign up with your email and the workbook is yours to keep, review, and edit.
What's included in the model?+
A 5-year monthly forecast with P&L, cash flow and runway, valuation (exit multiple plus a DCF cross-check), MOIC/IRR returns, and unit economics, with live formulas throughout.
How was this model built?+
It was built from Vic.ai's pitch deck and publicly available information, then structured to investment-banking standards as a fully editable Excel model.
Can I change the assumptions?+
Yes. You can change assumptions and the live formulas will recalculate in the downloadable Excel model.
Have more financial modelling questions? Contact us
Created by ex-finance professionals
Hey, I’m Alex and I created Finamodel.
Over my years in the finance industry I kept building the same models over and over again. Same structure, same assumptions, different logo. So I started building frameworks to turn them into clean, reusable templates.
Every model here is one I’d actually use for a client, and I personally vet each one before it goes up.
I’m not an expert in every industry, but I’ve built enough models to know what belongs in one. And when something is completely foreign to me, I reach out to my network for experts to work on our models with us.
Having a template library on hand cuts a first build from hours to minutes.
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