Faception Pitch Deck (2014): 11-Slide Series A Deck

Faception’s 11-slide deck from 2014 is a masterclass in high-impact, low-detail storytelling. By opening with the 2015 Paris attacks and claiming to have classified 9 out of 11 terrorists, the company immediately establishes a high-stakes value proposition for homeland security. The deck is light on technical architecture but heavy on validation, citing a $750,000 purchase order and a 93% accuracy rate in specific application tests. While it lacks traditional financial projections, a business model, or a competitive landscape, it relies on the 'world-class' credentials of its team—including a…

Key takeaways

• The deck leads with a massive traction milestone: a $750,000 purchase order displayed on slide 4.
• Faception claims a high predictive accuracy of 93% (25/27) in application tests on slide 9.
• The technology is positioned as a security tool, claiming to have classified 9 of the 11 terrorists from the Paris November 2015 attacks on slide 3.
• The team slide (10) highlights significant experience, including a CEO with 6 startups and 2 exits, and a CTO with 32 years in computer vision.
• The deck uses a 'Wow Me!' interface preview on slide 8 to show diverse profiling categories like MMA fighters and white-collar offenders.
• Specific personality metrics shown on slide 7 include 'High IQ', 'Extrovert', and 'Brand Promoter' on a -5 to 5 scale.
• The deck completely omits a formal 'Ask' slide, financial forecasts, and a breakdown of the business model.
• Scientific backing is implied via a DNA diagram on slide 6, though the specific biological link to facial features is not explained in detail.

Frequently asked questions

What is the primary value proposition of Faception?

Faception proposes that facial features are a direct reflection of personality and DNA. According to slides 5 and 6, their technology uses computer vision to analyze these features and reveal traits in real-time. The primary use case demonstrated in the deck is security and public safety, specifically identifying 'potential terrorists' or individuals with specific behavioral tendencies before they act.

How does the company validate its technology's accuracy?

Validation is presented through two main lenses: historical back-testing and live field tests. Slide 3 claims the software correctly classified 9 out of 11 terrorists involved in the Paris attacks. Slide 9 cites a '500 Application Test' with 93% accuracy and a poker tournament where it successfully predicted 2 out of 3 winners based on facial profiling.

Who are the key members of the Faception team?

The team, detailed on slide 10, includes CEO Shai Gilboa (6 startups, 2 exits), CTO Itzik Wilf (32 years in computer vision), and Chief Profiler David Gavriel (30 years in non-verbal communication). It also features Dr. Michal Kosinski, a Stanford Professor and expert in psychology and organizational behavior, providing academic weight to the profiling claims.

What market sectors is Faception targeting?

While the deck leans heavily on Homeland Security (HLS) and public safety, slide 8 suggests a broader range of applications. The interface shown includes tabs for 'Pro Poker Players', 'MMA Fighters', 'White-Collar Offenders', and 'Brand Promoters'. This indicates a target market spanning security, professional sports, recruitment, and marketing analytics.

What critical information is missing from this pitch deck?

The deck is missing several standard Series-A components. There is no slide detailing the business model (how they charge), no competitive analysis, and no financial projections. Most notably, there is no 'Ask' slide specifying how much capital they are raising or how the funds will be allocated, which is unusual for a fundraising document.

Faception pitch deck: the facts

Company

Faception

Year

2014

Stage

Series-A

Slides

11

Faception pitch deck PDF

The full Faception 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 Faception pitch deck was used for

This deck is a **2014 Series-A pitch** by Faception, an Israeli startup developing facial personality analytics using computer vision and machine learning to infer traits and behavioral tendencies from facial images. The deck positions the technology primarily for high-stakes **security and public safety** scenarios, including identifying potential terrorists and other high-risk profiles, with secondary commercial applications in customer intelligence. It highlights validation claims such as correctly classifying 9 of 11 Paris attackers, a 500-application test with 93% accuracy, and a poker-tournament prediction, alongside a prominently featured **$750,000 purchase order** as proof of market demand. The round is presented as a Series A targeting institutional investors following earlier seed and accelerator funding, though external funding databases show the official Series A being recorded around mid-2016, indicating this deck may have been used in an earlier push toward that milestone.

Business model: Developer of a **facial personality analytics** platform that uses computer vision and machine learning on facial images to infer personality traits and behavioral tendencies, sold as predictive screening and customer intelligence tools to security, public safety, smart cities, and commercial clients.

Investors

Early investor names appearing in startup profiles and platforms include 500 Startups (500 Global), FoundersX Ventures,

Headquarters

Tel Aviv, Israel

Round: Series A (as labeled in the deck and pitch-deck collections, though external databases record the official Series A in mid-2016).

Year: 2014 (deck year as reported by pitch-deck libraries and the original source page, not by funding databases).

Raised: Pitch-deck libraries and the original source page consistently state that this 2014 Series-A deck was associated with a **$625,000 raise**, but no independent transaction record confirming a 2014 Series-A close for that exact amount is available in major funding databases.

Industry: AI, computer vision, facial recognition / facial personality analytics for security and marketing.

Total funding: Externally reported totals conflict: PitchBook reports $5.62M raised across seed, accelerator, Series A and later-stage VC rounds through 2022, while Tracxn reports $125K total over three rounds with an undisclosed Series A in June 2016. SnapMunk reports $125K seed prior to 500 Startups Demo Day in 2016.

What happened after the Faception deck

The 2014 Series-A deck is reported in pitch-deck libraries as having supported a raise of about $625,000 on the back of a $750,000 purchase order, but public funding databases instead document a sequence of seed, accelerator, and Series-A rounds concentrated around 2015–2016, with a later $5M round in 2022; the company remains a private Israeli startup active in facial personality analytics for se

• Participation in accelerator programs including iHLS Security Accelerator and 500 Startups, with at least $125K in seed
• Completion of seed and accelerator rounds in late 2015 and early 2016, followed by a recorded **Series A round in mid-20
• Securing later-stage VC funding of $5M in September 2022, bringing total reported funding to around $5.62M in at least o

What the Faception deck got right

• Large, specific purchase order as traction. The deck centers on a **$750,000 purchase order** from a government or enterprise customer, providing a concrete, high-value traction signal that suggests early market validation and willingness to pay for a controversial capability.
• Compelling, high-value security use cases. Faception’s technology addresses high-stakes **security and public safety** use cases (e.g., identifying potential terrorists and other risky profiles) where decision-makers value predictive screening tools and may accept unorthodox methods if they deliver operational advantages.
• Multi-angle validation narrative. The deck presents multiple **validation scenarios**—Paris attack back-testing, a 500-application test with 93% accuracy, and predictive success in a poker tournament—creating a narrative of robust, cross-domain performance.
• Positioning in a hot AI/computer-vision niche. Externally, Faception is positioned at the intersection of **AI, computer vision, and behavioral analytics**, a space that attracts significant investor interest, and the deck reinforces this by emphasizing proprietary algorithms and real-time personality inference.
• Funding continuity beyond the deck. Subsequent participation in accelerators and later rounds (seed, Series A, and later-stage VC) indicates that the company continued to secure capital over time, lending retrospective credibility to the early investment case framed in the 2014 deck.
• Concrete and vivid persona-based storytelling. The deck’s clear focus on **specific, named personas** (terrorists, child predators, smart people, brand promoters) makes the use cases vivid and easily understood, which can help investors quickly grasp the potential impact, even if they remain skeptical.

What could have been stronger

• Sparse technical and scientific detail. External descriptions of the deck note that it is **light on technical architecture**, offering limited detail on model training data, feature engineering, statistical methodology, or safeguards against bias and false positives, which makes it harder for investors to assess scientific robustness and regulatory risk.
• Headline validation claims without methodological transparency. While the deck presents striking validation claims (Paris attack profiling, 500-application test at 93% accuracy, poker winners), these are described at a **headline level** without experimental design, sample sizes beyond top-line numbers, or independent verification, leaving a gap in methodological transparency.
• Under-addressed ethical and regulatory risk. The core proposition—inferring personality traits, including terrorism and child predation risk, purely from facial features—is **highly controversial** and flagged as ethically problematic in independent reporting, yet the deck’s framing focuses mainly on opportunity and accuracy rather than ethical, legal, or societal risk management.
• Reliance on a single uncorroborated purchase order. The deck leans heavily on a single **$750,000 purchase order** as de-risking traction, but external sources do not corroborate this specific contract, and the deck (per the teardown) does not present a broader pipeline, customer concentration analysis, or terms (duration, renewals), leaving revenue durability unclear.
• Risk-skewed positioning toward sensitive security use cases. Given the controversial nature of personality profiling from faces, the deck’s focus on high-stakes security use cases without parallel emphasis on more benign commercial applications or clear guardrails may narrow the investor audience and heighten perceived regulatory and reputational risk.

How an investor would read this deck

What draws attention

• Clarify the **exact funding history** around the 2014–2016 period, including how the 2014 Series-A deck, the reported $625,000 raise, and the later-documented seed, accelerator, and Series-A rounds fit together.
• Examine in detail the **$750,000 purchase order** highlighted in the deck: counterparty, contract duration, milestones, scope (pilot vs. production), and how much revenue was actually recognized.
• Assess the **scientific validity** of the claimed 93% accuracy and terrorist classification results, including datasets, labeling methodology, statistical testing, and external audits.
• Evaluate **ethical, legal, and regulatory exposure** of facial personality profiling for sensitive categories (terrorists, child predators), including alignment with privacy laws, anti-discrimination regulations, and public-sector procurement standards.
• Understand the **go-to-market strategy** beyond early pilots: target segments (security, smart cities, commercial analytics), pricing, scalability, and how the company plans to mitigate reputational risk while selling into government and enterprise.

Risks that stand out

• **Scientific and technical risk**: Personality inference and security-risk classification from facial features is at the frontier of what is empirically supportable; if the underlying science does not hold up under scrutiny, the product’s core value proposition could be invalidated.
• **Ethical and regulatory risk**: Profiling terrorists, child predators, or other sensitive personas from facial images raises substantial privacy, discrimination, and civil-liberties concerns, which can trigger regulatory bans, litigation, and reputational damage.
• **Data and bias risk**: If training datasets are skewed or insufficiently diverse, the models may exhibit biased error patterns across demographic groups, leading to harmful misclassification in high-stakes contexts and potential legal liability.
• **Commercial concentration risk**: The deck’s traction narrative relies heavily on a single large purchase order, and without evidence of a diversified pipeline, future revenue may be fragile and dependent on a few early adopters.
• **Public and media backlash risk**: Independent press has already framed the technology as controversial, which may limit adoption by risk-averse governments and enterprises and constrain exit opportunities.

Questions this deck invites

• What is the documented timeline and structure of Faception’s funding from 2014 through the mid-2016 Series A, and how (if at all) does the $625,000 associated with this deck relate to later reported rounds?
• Can the company provide contracts and performance reports for the **$750,000 purchase order**, including revenue recognized, renewals, and customer feedback?
• What datasets (size, source, demographics) underpin the models used for terrorist/child-predator profiling, and what independent validation or red-teaming has been performed to test for bias and error rates in real-world conditions?
• How does Faception ensure compliance with privacy, surveillance, and anti-discrimination regulations in each geography where its software is deployed, and what legal opinions or regulatory clearances has it obtained?
• What is the current mix of use cases (security vs. marketing/customer analytics vs. other), and how diversified is the customer base beyond the early high-stakes security pilots referenced in the deck?

What founders can take from the Faception deck

• A single **large purchase order** (here, $750,000) can be a powerful centerpiece in a Series-A pitch for a frontier technology, but investors will still demand detailed contract and performance evidence, not just a headline number.
• Controversial technologies, such as facial personality profiling for security, benefit from early and explicit framing of **ethical, legal, and societal safeguards**; omitting these can become a perceived weakness even when technical validation appears strong.
• Validation stories (terrorist classification, high-accuracy tests, tournament predictions) are compelling, but without methodological transparency, they may be viewed as **marketing anecdotes rather than scientific evidence**.
• Positioning a technology across both **high-stakes security** and more commercial customer-analytics use cases can broaden the market narrative, but the deck must balance risk-heavy applications with safer domains to avoid over-indexing on regulatory exposure.
• Relying on external accelerator and seed funding (e.g., 500 Startups and other early backers) while preparing a Series-A deck underscores the importance of **clear, consistent funding history and cap table narratives** in investor materials.
• Pitch-deck libraries and teardowns are useful for understanding how founders frame a story (e.g., 11 slides, 2014 Series-A, $625,000 raised), but those narratives may diverge from later-reported funding data, highlighting the need to **cross-check deck claims against independent transaction records*

Faception pitch deck: common questions

What does Faception do, according to the pitch deck and external sources?

Faception develops **facial personality analytics** software that uses computer vision and machine learning on facial images to infer personality traits and behavioral tendencies (e.g., security risk, customer potential) without requiring personal identification. The 2014 Series-A pitch deck frames this as a predictive screening tool for security and public safety, as well as for commercial customer intelligence.

Did Faception actually raise a Series-A round around 2014?

External funding trackers show Faception running a **seed and accelerator sequence in 2015–2016**, followed by a **Series A in mid-2016** and later-stage VC funding in 2022. The pitch deck library entries and teardown describe a **2014 Series-A deck that reportedly raised about $625,000**, supported by a highlighted $750,000 purchase order as traction, but public databases do not list a clearly labeled 2014 Series-A close.

What traction and validation does the Faception Series-A deck claim, and how much of it is externally verified?

The deck emphasizes a **$750,000 purchase order** from a government or enterprise customer as core traction, plus validation metrics like correctly profiling 9 of 11 terrorists in the Paris attacks, a 500-application test with 93% accuracy, and predicting 2 of 3 poker tournament winners. External sources independently confirm Faception’s participation in 500 Startups, prior seed funding of $125K, and positioning in security and marketing analytics, but they do not directly corroborate the specific purchase order or the deck’s experimental statistics.

How does Faception’s technology work and what applications are highlighted?

According to the company site and press coverage, Faception’s technology encodes facial attributes (ratios and key-point geometry) and uses machine learning models to predict personality traits and behavioral categories such as security risk profiles or marketing-relevant personas. The deck itself is reported to show applications in **homeland security, public safety, smart cities, and customer analytics**, but is light on detailed technical architecture or model design.

Is the $625,000 amount attributed to this deck a confirmed funding transaction?

The deck collection sources state that the **2014 Series-A deck is 11 slides and associated with a $625,000 raise**, with external pitch-deck libraries and teardowns repeating this figure. However, major funding databases list seed, accelerator, and Series-A rounds from late 2015 through mid-2016, and none independently verify a discrete $625,000 Series-A close in 2014, so the $625,000 figure should be treated as **deck-library metadata rather than a confirmed transaction record**.

Sources

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

• Faception 2025 Company Profile: Valuation, Funding & Investors | PitchBook
• Faception Startup Profile | Updated May 8, 2026 - Claw & Talon
• Faception - Company Profile - Tracxn
• Faception - F6S
• FACEPTION | Facial Personality Analytics
• Faception | Tel Aviv-Yafo, Israel Startup
• Faception - Pitchdecky: Every Startup Pitch Deck

Faception pitch deck slides

What each slide of the Faception pitch deck says

Slide 6

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

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