Apheris Pitch Deck: All 10 Slides + Teardown

Apheris presents a highly technical yet accessible 10-slide deck that focuses on the critical bottleneck of modern AI: the need for large, diverse datasets that are often locked behind privacy and intellectual property barriers. By positioning themselves as the infrastructure for 'privacy-preserving data ecosystems,' the founders—boasting PhDs and backgrounds at BCG and research institutions—successfully argued that they could unlock value in highly regulated sectors like pharma and chemicals. The deck is notable for its restraint, avoiding typical 'market size' fluff in favor of specific wor…

Key takeaways

• The executive summary explicitly states an ask of EUR 2m to build the core product and ramp up sales (Slide 1).
• The team emphasizes high technical density, noting 9 FTEs with 6 PhDs on the staff (Slide 1).
• Apheris identifies a core AI thesis: model accuracy depends more on the size of the training dataset than the algorithm itself (Slide 3).
• The product workflow demonstrates that data never leaves the local environment, using a 'Privacy Engine' and 'Compute Engine' to maintain security (Slide 5).
• Traction is evidenced by 'multiple year contracts with leading enterprises' and a role as a privacy provider for COVID-19 apps (Slide 6).
• The roadmap sets a clear target of reaching more than 10 enterprise customers by Q4 2021 (Slide 7).
• The founders leverage massive brand equity by listing logos like Google, BCG, AstraZeneca, and UBS as places where the team has 'learned from the best' (Slide 8).
• The deck lacks a traditional competition slide, choosing instead to focus on their 'unique positioning' in the executive summary (Slide 1).

Frequently asked questions

What is the core problem Apheris is solving?

Apheris addresses the 'data silo' problem. As shown on Slide 3, AI performance is limited by dataset size. However, companies cannot easily share data due to privacy and IP concerns. Apheris provides the infrastructure to run analytics on decentralized data without moving it, allowing for collaboration without compromising security.

How does the Apheris technology actually work according to the deck?

Slide 5 illustrates a workflow where a Data Analyst sends an untrained model into the Apheris 'Privacy Engine' and 'Compute Engine.' These engines interact with distributed data behind a 'Privacy Firewall.' Crucially, computations are executed locally; the data never leaves its original environment, and only the trained model (the 'insight') is returned.

What kind of traction did Apheris have at the time of this deck?

The company reported significant early momentum on Slide 6, including multiple-year contracts with leading enterprises and a 'very strong pharma & chemistry pipeline.' They also highlighted their role as a key privacy technology provider for COVID-19 contact tracing initiatives.

Who are the founders and what is their background?

The co-founders are Michael (CTO), a PhD in Physics and Computer Science with experience at BCG, and Robin (CEO), who studied Medicine, Philosophy, and Mathematics. Their combined expertise covers distributed computations and mathematical theories for data privacy, as detailed on Slide 8.

What was the specific funding ask in this deck?

Slide 1 and Slide 7 both state an ask of EUR 2 million. The funds were earmarked for hiring a 'world-class team,' building out data ecosystems, and creating a 'flexible core product for enterprise.' While the catalogue facts show a total of $11.7M raised, this specific deck was tailored for a EUR 2M seed milestone.

Apheris pitch deck: the facts

Company

Apheris

Year

2019

Stage

Seed

Slides

10

Sector

Data Privacy / AI Infrastructure

Deck type

Investment Pitch

Outcome

$11,700,000 Raised

Headquarters

Berlin, Germany (implied by +49 phone number)

Apheris pitch deck PDF

The full Apheris deck is embedded on this page and can be read slide by slide in the browser — no download or account required. Each slide is covered in the breakdown above.

What the Apheris pitch deck was used for

This is Apheris’s 2019 **seed-stage** pitch deck for a privacy-preserving data ecosystem and federated learning platform, reportedly used to raise around €2.5–$3m in seed funding led by LocalGlobe with Dig Ventures, Patrick Pichette and others. The company proposes infrastructure that lets enterprises, particularly in pharma, industrial and chemical sectors, train AI models across distributed datasets while keeping data local and private (slides 2, 5, 6). In the deck they state they are raising **EUR 2m** to build out their core product and ramp up sales (slide 2), with later slides framing this as building the software core and data ecosystems to empower R&D breakthroughs (slide 8). The deck positions Apheris at the intersection of data privacy, cryptography and biomedical data science, highlighting a 10-person expert team and early enterprise pilots (slides 7, 9).

Business model: Federated computing / privacy-preserving data ecosystems platform enabling organizations (especially life sciences and pharma) to train AI models on distributed data without moving or centralizing sensitive information.

Round

Seed

Lead investor

LocalGlobe

Investors

LocalGlobe (lead seed investor)., Dig Ventures, the family office of MuleSoft founder Ross Mason., Patrick Pichette, former Google CFO and later chair of Twitter’s board., another.vc., System.One., Angel investors including Charles Songhurst, Torsten Reil (NaturalMotion founder), Ian Hogarth (Songkick founder).

Founded

2019

Founders

Robin Röhm, Michael Höh.

Headquarters

Berlin, Germany

Year: 2019–2020 (deck dated 2019; seed round announcement timing around 2020 in public sources).

Raising: EUR 2m stated in the deck to build the core product and ramp up sales (slide 2), which corresponds to a publicly reported seed raise of roughly €2.5m–$3m led by LocalGlobe with other investors.

Raised: Approximately €2.5m–$3m seed round (public sources differ slightly on exact amount).

Industry: Federated computing / data privacy infrastructure for AI, with a focus on life sciences and pharma data collaboration.

Total funding: Approximately €8.7m seed extension (2022) plus earlier €2.5–$3m seed and a later ~€20.1m Series A, with total funding reported around $20.8m–$41.54m across sources.

Use of funds as presented: Build out Apheris’s core privacy-preserving data ecosystems product, hire a world-class team, and empower R&D breakthroughs via data ecosystems over 2020–2022 (slides 2, 8).

What happened after the Apheris deck

Following its 2019 seed pitch deck, Apheris successfully raised seed capital led by LocalGlobe and later a significant seed extension and Series A, growing into a leading federated computing platform provider for privacy-preserving, collaborative AI in life sciences and pharma, with named enterprise customers and a focus on drug discovery applications.

• Raised an initial seed round of approximately €2.5m–$3m around 2019–2020, led by LocalGlobe with participation from Dig
• Expanded funding with an €8.7m **seed extension** round led by Octopus Ventures, joined by existing investors LocalGlobe
• Announced a **Series A** round (~€20.1m, with total funding to date cited as $20.8m in one source) co-led by OTB Venture
• Developed a mature federated computing platform and data ecosystems architecture, described in later technical materials
• Shifted and sharpened product focus toward enterprise-grade AI applications for **drug discovery** running within pharma

What the Apheris deck got right

• Compelling problem-solution narrative around AI data bottlenecks and privacy. The deck clearly articulates a **data-centric AI problem**—that meaningful AI requires large, diverse datasets and current enterprises are constrained by siloed data—then presents federated, privacy-preserving data ecosystems as a direct solution (slides 3–6), a narrative later echoed in external descriptions of Apheris’s federated computing platform.
• Highly credible, interdisciplinary founding team aligned with the problem. Apheris’s founding team combines expertise in medicine, philosophy, mathematics, physics and computer science, with specialization in data privacy and distributed computations (slide 9); later profiles of Robin Röhm and Michael Höh confirm these backgrounds and their leadership in federated computing for life sciences.
• Early enterprise traction later validated by named flagship customers. The deck claims multiple paid pilots and year-long contracts with leading enterprises within less than a year (slide 7), and subsequent funding announcements list prominent customers and partners such as BASF, BMW Group, BCG and JLABS, supporting the deck’s portrayal of strong early traction.
• Clear explanation of complex federated computing concept. The product workflow slides (5–6) explain federated learning in accessible terms—data stays local, computations are executed within each environment, and only trained models or results move—matching later technical blog posts about Apheris’s compute gateway and IP-preserving computations.
• Strong alignment with regulatory and privacy tailwinds. The deck’s focus on **privacy-preserving collaboration** aligns with European regulatory trends (GDPR, data sovereignty) and with later positioning of Apheris as a solution to the data privacy dilemma in AI, strengthening the strategic rationale for the business.
• Coherent long-term vision from seed deck through later growth. The consistency between this deck’s vision of decentralized data collaboration and later descriptions of Apheris powering federated data networks for life sciences and drug discovery suggests the founding team maintained a coherent long-term product and market strategy.

What could have been stronger

• Limited specificity on enterprise traction in the deck. Despite positioning in "multiple markets" and mentioning pharma, industrial and chemical customers (slide 2), the deck does not specify concrete customer names, contract sizes, or detailed case studies on revenue impact; later sources name BASF, BMW, BCG and JLABS, but this detail is absent from the deck itself.
• Unsubstantiated "profitable SaaS" claim without metrics. The deck repeatedly emphasizes "proven profitable SaaS business model" (slide 2) but provides no financial metrics—no ARR, margins, churn, or unit economics—making it hard for investors to verify the profitability claim from the deck alone.
• Shallow technical and security detail relative to privacy claims. While slides 5 and 6 outline high-level product workflow (distributed data, privacy engine, compute engine, firewall), they lack a detailed architecture, deployment model, or security assurances (e.g., threat models, certifications), which is important for a product sold on privacy and compliance.
• Ambiguity in team slide about affiliations and roles. The deck lists a 10-person expert team and logos of prestigious organizations (BCG, Google, AstraZeneca, Visa, Fraunhofer, etc.) on slide 9, but does not clearly distinguish between employers, partners, advisors or customers, which may create ambiguity around the nature of those relationships.
• High-level use of funds and roadmap without quantified milestones. Although slide 8 includes a timeline (2020–2022) and mentions hiring and building data ecosystems, it does not break down concrete milestones (product releases, GTM motion, regulatory approvals), nor quantify expected runway or hiring plan tied to the EUR 2m raise.

How an investor would read this deck

What draws attention

• Strength of founding team with deep technical background in federated learning, data privacy, cryptography and biomedical data science, corroborated by external profiles of Robin Röhm and Michael Höh.
• Clear articulation of the **AI data bottleneck**—that AI is only meaningful with large, diverse datasets and data owners are constrained by privacy and governance—and a technical approach (federated computing) that directly addresses this problem (slides 3–6).
• Evidence of early enterprise interest and paid pilots with large pharma, industrial and chemical companies in under a year (slide 7), later supported by named customers such as BASF, BMW Group, BCG and JLABS.
• Strategic fit with regulatory and privacy trends in Europe (GDPR, data sovereignty), making privacy-preserving collaboration a secular tailwind for the business model.
• Subsequent successful fundraising (seed extension and Series A) indicates investor appetite and some validation of the original seed thesis.

Risks that stand out

• Execution risk in building and maintaining a complex federated computing platform that must satisfy stringent privacy, security and regulatory requirements across multiple large enterprises, with relatively modest initial seed resources.
• Competitive risk from both large cloud providers and open-source federated learning frameworks; TechCrunch notes Apheris originally aimed to compete with existing open-source approaches, implying a need for strong differentiation.
• Adoption risk: convincing conservative pharma and industrial companies to adopt new privacy-preserving collaboration models and integrate Apheris into existing IT environments may lead to long sales cycles and slower-than-expected revenue ramp.
• Dependence on a narrow but deep technical team; although the founders and early team are highly qualified, scaling beyond a small group of experts while maintaining quality and security is non-trivial.
• Market focus evolution: the company later narrows strongly into life sciences and drug discovery; if this focus was not fully clear at seed stage, there is a risk of strategic drift before product-market fit is solidified.

Questions this deck invites

• What specific pilots and contracts were signed with "the largest pharmaceutical, industrial and chemical companies" by the time of this seed deck, and what were their financial terms (ACV, duration, expansion potential)?
• How is "proven profitable SaaS business model" defined at this stage—what are Apheris’s gross margins, burn rate, and revenue composition from pilots vs. recurring contracts in 2019–2020?
• What is the precise technical architecture of the privacy-preserving compute engine (e.g., federated learning framework, cryptographic techniques, on-prem vs cloud), and how has it evolved since the deck (reference to later platform archite
• How does Apheris differentiate from open-source federated learning frameworks and other privacy-tech competitors, both technically and in go-to-market, given TechCrunch’s description of their original goal to compete with open-source framew
• What regulatory, security and compliance certifications (e.g., ISO, SOC 2, HIPAA where relevant) have been achieved since the seed round, and how do they underpin sales into highly regulated pharma and industrial customers?

What founders can take from the Apheris deck

• A technically complex product can still be pitched clearly if it is anchored in a simple, compelling problem narrative—the deck moves from AI usage (slide 3) to the data bottleneck (slide 4) to federated privacy-preserving ecosystems (slides 5–6), a structure later echoed in public descriptions of A
• Credible, well-documented founder backgrounds in relevant domains (medicine, mathematics, physics, computer science, privacy, distributed computations) are a major asset; the eventual success of Apheris’s fundraising underscores how deep expertise reassures investors when tackling regulated, high-st
• Early pilots and contracts with large enterprises—even if relatively small in revenue—can serve as strong validation for infrastructure startups, especially when later sources confirm that these relationships expanded into named customers like BASF, BMW, BCG and JLABS.
• Positioning around regulatory and privacy tailwinds (GDPR, data sovereignty) can differentiate an AI infrastructure company; Apheris’s later media coverage emphasizes federated learning where data never leaves the owner, validating the original deck’s focus on privacy-preserving ecosystems.
• Having a clear, consistent narrative from seed deck through later rounds (seed extension, Series A) makes it easier to attract follow-on capital; Apheris’s messaging about collaborative data ecosystems and federated computing remains coherent across its deck, blog posts and funding announcements.
• Explicitly articulating how technology enables **collaborative data networks** rather than just single-tenant deployments resonates with investors looking for network effects and defensibility, as reflected in later descriptions of Apheris powering data networks for life sciences and drug discovery.

Apheris pitch deck: common questions

What does Apheris do, and how is it described in this seed pitch deck?

Apheris builds a **federated computing / privacy-preserving data ecosystem** platform that lets organizations train AI models on distributed, sensitive data without moving or centralizing that data. The 2019 seed deck describes "privacy preserving data ecosystems" that empower companies to securely share data and train AI models locally across multiple organizations (slides 2, 5, 6).

How much was Apheris raising with this deck, and who invested in the seed round?

According to multiple funding reports, Apheris raised about **€2.5m–$3m seed** funding around 2019–2020, led by UK seed investor **LocalGlobe**, with participation from **Dig Ventures** (Ross Mason’s family office), former Google CFO **Patrick Pichette**, and others such as another.vc, System.One and several angel investors. The deck itself states they are "raising EUR 2m" to build out their core product and ramp up sales (slide 2).

What funding rounds has Apheris raised after this 2019 seed deck?

The deck (2019) targets **Seed** stage, asking for EUR 2m (slides 2, 8). Subsequently, public sources report a **€2.5m–$3m seed round** led by LocalGlobe with Dig Ventures, Patrick Pichette and others. Later, Apheris raised an **€8.7m seed extension** led by Octopus Ventures with existing investors LocalGlobe, Dig Ventures, Another.VC and Patrick Pichette, and then a **Series A (~€20.1m)** co-led by OTB Ventures and eCAPITAL with participation from Octopus Ventures and Heal Capital.

Who founded Apheris and where is the company based?

Apheris is based in **Berlin, Germany** and was **founded in 2019**. The co-founders are **Robin Röhm** (CEO) and **Michael Höh** (CTO), both with backgrounds in medicine, mathematics, physics, computer science and privacy-preserving computations; the deck’s team slide matches these profiles (slide 9).

What early traction and customers does Apheris highlight in the deck, and is any of it externally confirmed?

The deck claims early traction with "the largest pharmaceutical, industrial and chemical companies" (slide 2) and "multiple paid pilots" and "year contracts" with leading enterprises within less than a year (slide 7). Later public sources confirm major customers and partners such as **BASF, BMW Group, Boston Consulting Group, and Johnson & Johnson’s JLABS** as part of their collaborative data ecosystems platform.

Sources

Funding and outcome facts on this page were researched on 2026-08-21 from the pages below.

• Apheris raises €8.7m to power development of smarter AI and...
• Apheris: Funding, Team & Investors | Startup Intros
• This German startup with cutting-edge privacy-preserving data ecosystem draws €2.5M - Silicon Canals
• Apheris Pitch Deck to Raise $3m Seed Round - Alexander Jarvis
• eCAPITAL invests in Apheris' Series A round to foster federated...
• Apheris rethinks the AI data bottleneck in life science with federated...
• Apheris receives 8.7 million euros - Startbase
• Ross Mason Single Family Office Invests In Apheris €2.5M Funding Round

Apheris pitch deck slides

What each slide of the Apheris pitch deck says

Slide 2

Executive Summary apheris Al is building privacy preserving data ecosystems that empower companies to securely and privately share data P o El ] ZEPra. & PhDs = 5"'\ I I8 o, by W — —= « Leaoding Start-up focusing on « Provide technical sclufions that = Unique positioning in multipie federated and privacy empower companies 10 share maorkets } P preserving mochine leaming dala while preserving the (F o Pioven profifable Saas « Team of world leading « Customers include the lorgest business modeal enfreprenaurs, engineerns and phamoceutical, industial and 3 interciisciplinary scienfists chemical componies Sunning ecxty kackon! We are raising EUR 2m to build out our core product and ramp up Sales

Slide 3

Al usage on the rise, fransforming every aspect of the modern enterprise s A ™ Distribution & Research & Development Production Logistics g » Component design + Resource * Supplier Risk * Process + Predictive * Process v ¢ Structurcl timization management opfimization Maintencnce opfimization g properties = Prize optimzation Stotegic Sourcing + Robokics + Llilecycle * Robofics * Mixtures & Reguictory . chain + Quality inspection management + Driveriess 2 Formulations assessment opfimizafion * Industry 4.0 / loT distribution analytics « Targeted Soies

Slide 4

Al is only meaningful if you have enough data The unreasonable effect of data: size of data matters far more than the algorithm itself Companies 5 need large and R IS diverse datasets e 7 P to do Al Learning curves for different Al algorithms - final accurccy of model in dependence on size of raining dataset

Slide 5

We are building privacy-preserving data ecosystems that empower companies to unlock new Al opportunities Today: data analysts are Future with apheris: privacy preserving data ecosystems limited to own centralized data within one company or across several companies Dala sharing within and ocross companies b apheris Al .""/ - - .'" - - - / Data Own cenfral Data Privacy-preserving Analyst data Analyst data ecosystem

Slide 6

Product workflow apheris Al empowers companies to frain Al models on distributed data while fully preserving data privacy e e e e Distributed Data i 4 apheris Al Unftegined Al model Privacy Englne . "u/ Trgined Al model -/j & - s S E— Compute Engine @ b Privacy : { Flrewcll 1 Data Analyst I Computations are executed locally - data never leaves the local environment and data privacy is preserved throughout the entire process

Slide 7

Our biggest achievements: our technology already empowers R&D breakthroughs for the world's largest enterprises 2 s N al In less than 1 year, we closed multiple We develop core privacy technology E.g.. poid piot for adge-based year confracts with leoding for lorge contact tracing initiafives. computations. Viery strong phama & enterprises, chemistry pipeline. &

Slide 8

We are raising EUR 2m to build our software core and empower R&D breakthroughs via data ecosystems (¥ Hire world class team [ Build out data ecosystems (¥ Flexible core product for enterprise =0 b 2020 2021 9 2022 4

Slide 9

We are a team of 10 leading experts at the intersection of data privacy, cryptography and biomedical data science Qur Co-Founders: Michael, PhD CT0 & Co-Founder Robin CEO & Co-Founder Qur team has leamned from the best: Michael studied Physics and Computer Science and is an expert in distributed computations. He built digital solutions with BCG & BCG DV, Robin studied Medicine, Philocsophy and Mathematics and is an expert in mathematical theories for data privacy. BCG & UBS Google - o cIspA o . o impetlCotee AstrazZeneca O'— VISA "\Ai'j' : Rt 353 = Fraunhofer &

Slide text above is read directly from the Apheris deck PDF embedded on this page.

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