Apheris Financial Model
AI/ML Startup Financials (Free Excel Download)
Privacy-preserving federated ML platform enabling enterprises to train AI on distributed data without sharing raw data.
professionals from Deloitte
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About this model
Apheris provides federated machine learning that lets organisations train models on distributed data without moving raw information. Its Privacy Engine, Compute Engine, and Privacy Firewall support collaboration where GDPR, intellectual-property, or local-environment constraints prevent conventional data sharing.
The platform targets pharma, chemical, and industrial customers, with use cases spanning R&D, predictive maintenance, process optimisation, formulations, and contact tracing. The deck cites multi-year enterprise contracts, paid pilots, and a strong pharma and chemistry pipeline, while disclosing no revenue figures.
The forecast therefore begins with contract-based SaaS revenue and pilots, converting opportunities into deployed enterprise accounts. It should model implementation timing, expansion, infrastructure and privacy-engine delivery costs, the nine-to-ten-person technical team, commercial hiring, cash burn, and funding runway.
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 Apheris
apheris.com
How to build a detailed financial model for Apheris
A complete walkthrough of the business, drivers, and assumptions behind the downloadable Apheris model - distilled from its pitch deck and publicly available information.
Product & value proposition
- Federated ML platform: trains AI models on distributed, siloed data without data ever leaving local environments.
- Core components: Privacy Engine, Compute Engine, Privacy Firewall.
- Value prop: enables cross-company (and intra-company) data collaboration under GDPR/IP constraints - "collaborate on data securely without compromising privacy".
- Target use cases: pharma R&D, industrial AI (predictive maintenance, process optimisation, formulations), COVID-19 contact tracing.
Revenue model
- Model: SaaS ("proven and profitable SaaS business model"). No pricing tiers, seat counts, or ACV figures disclosed.
- Contract type: multi-year enterprise contracts.
- Channels: direct enterprise sales; verticals are pharma, chemical, and industrial.
- Additional revenue: paid pilots (e.g., edge-based computations).
Traction & metrics
- Team: 9 FTE, 6 PhDs at time of deck; slide 9 header says "team of 10".
- Customers: "multiple year contracts with leading enterprises" closed in <1 year; target >10 enterprise customers by Q4 2021.
- Pilots: multiple paid pilots active; "very strong pharma & chemistry pipeline".
- COVID-19: core privacy technology provider for large contact tracing initiatives.
- No revenue figures, ARR, MRR, or logo names disclosed.
Competition / moat
- Moat claims: unique positioning in multiple markets; world-leading team at intersection of data privacy, cryptography, and biomedical data science.
- No direct competitor names or competitive matrix shown.
- Technical differentiation: computations execute locally - data never leaves local environment.
- Team pedigree: BCG, BCG Digital Ventures, Google, VISA, AstraZeneca, Bayer, Imperial College London, TU Munich, RWTH Aachen, Fraunhofer, CISPA.
Team & funding ask / use of funds
- Co-founders: Robin (CEO) - Medicine, Philosophy, Mathematics, expert in mathematical theories for data privacy; Michael (CTO, PhD) - Physics & CS, distributed computations, BCG & BCG DV background.
- Raising: EUR 2m Seed.
- Use of funds: hire world-class team, build out data ecosystems, build flexible core product for enterprise.
- Milestones post-raise: >10 enterprise customers by Q4 2021; Series A by Q2 2022.
Recommended financial model
- Archetype + why: Enterprise SaaS ARR model. Revenue is multi-year subscription contracts with enterprise clients. ACV-based build is appropriate - track new logos, expansions, and churn separately. Paid pilots can be modelled as a conversion funnel into full contracts.
- Forecast horizon & granularity: 3 years (2020–2022), monthly in Year 1, quarterly in Years 2–3. Aligns with the deck's own roadmap milestones (Seed → >10 customers → Series A).
- Key drivers & assumptions:
- Starting ARR: EUR 0 at model start (pre-revenue or very early; no figure in deck) - flag as open question.
- ACV per enterprise contract: EUR 80–150k/yr - typical for early-stage federated ML/data infrastructure enterprise deals; no deck figure.
- New logos per quarter: 2–3 in Year 1 ramping to 4–6 in Year 2, targeting >10 total by Q4 2021.
- Pilot-to-contract conversion rate: 40–60% - paid pilots signal intent; no deck data.
- Net revenue retention: 110–120% - multi-year contracts plus expansion potential; no deck data.
- Gross margin: 70–80% - software-heavy delivery with some professional services; no deck data.
- Headcount: 9–10 FTE at raise; scale to ~20 FTE by end of Year 1 (engineering + sales ramp).
- Avg. fully-loaded cost per FTE: EUR 90k/yr (Germany; senior technical team).
- Seed proceeds: EUR 2m.
- Burn rate / runway: ~EUR 150–180k/mo post-hire-up; ~12–14 months runway from EUR 2m raise.
- Series A timing: Q2 2022; amount EUR 6–10m based on typical Series A for stage/sector.
- Scenarios (Base / Bull / Bear - which variables flex):
- Base: 2–3 new logos/quarter, 50% pilot conversion, 110% NRR, headcount per plan.
- Bull: faster enterprise closings (4+ logos/quarter), 70% pilot conversion, 120% NRR, earlier Series A.
- Bear: long sales cycles (1–2 logos/quarter), 30% pilot conversion, 100% NRR, extended runway needed or bridge required.
- Required sheets / outputs:
- ARR bridge (new ARR, expansion, churn, net new ARR)
- P&L (revenue, COGS, gross profit, OpEx by function, EBITDA)
- Headcount plan (by department: engineering, sales, ops)
- Cash flow & runway (monthly burn, ending cash, months to zero)
- Milestones tracker (>10 customers Q4 2021, Series A Q2 2022)
- Scenarios toggle (Base / Bull / Bear)
Frequently asked
Is the Apheris financial model free?+
Yes. The Apheris 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 Apheris'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
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