What is the primary problem this company is trying to solve?

Based on Slide 2, the company aims to address the limitations of manual security monitoring in large spaces. It proposes using AI to monitor 'every corner 7x24x365,' reducing the need for repetitive human tasks and detecting security breaches before they occur. However, the deck lacks a dedicated 'Problem' slide to quantify the cost or frequency of these security failures.

How does the product actually work technically?

The deck specifies an 'Edge AI' approach on Slide 8. This means the video analytics are processed locally rather than solely in the cloud. The system is designed to be hardware-agnostic, supporting third-party cameras and integrating with existing Video Management Systems (VMS) and mobile devices. Slide 6 breaks down the capabilities into specific AI tasks like object tracking and intrusion detection.

What market data does the company use to justify its existence?

On Slide 4, the company cites data from IHS Markit issued in November 2019. It highlights that the global installed base for security cameras was projected to grow from 655.8 million units in 2018 to over 1 billion units in 2021. This represents a 16% CAGR, with China accounting for more than half of the global market.

What is missing from the business model and growth sections?

The deck is missing almost all standard financial metrics. There is no mention of pricing strategy (SaaS vs. licensing), customer acquisition costs, or current revenue. While Slide 12 mentions 'Customer Development' and 'Customer Service,' it does not list any current pilot programs, signed contracts, or specific target industries beyond the general 'large event and place' mentioned on the cover.

Is this a complete pitch deck for a seed round?

No. This deck functions more as a product overview or a sales presentation. It lacks the essential components of a fundraising deck, most notably a Team slide, a Financials slide, and an 'Ask' slide detailing how much capital is being raised and how it will be deployed. Without these, an investor cannot evaluate the execution risk or the potential return.

AI-Driven Large Space Security Pitch Deck (2019) Breakdown

See all 14 slides of the AI-Driven Large Space Security pitch deck, with a slide-by-slide teardown of what the deck does well and where it falls short.

The AI-Driven Large Space Security pitch deck presents a high-level overview of an Intelligent Video Analytics (IVA) solution aimed at crowd safety. The deck relies heavily on third-party market research from IHS Markit to establish a 16% CAGR in the security camera market, yet it fails to provide any company-specific metrics, revenue figures, or a defined business model. The product strategy emphasizes 'Edge AI' and compatibility with third-party Video Management Systems (VMS), but the competitive advantage slide uses generic terms like 'Brand Awareness' without supporting evidence. Cruciall…

Key takeaways

The deck identifies a global installed base of over 1 billion security cameras by 2021, citing a 16% CAGR from 2018 (Slide 4).
The solution is defined as Intelligent Video Analytics (IVA) that monitors crowd behavior in real-time to reduce manpower and increase safety (Slide 2).
Product features are categorized into Point, Line, and Surface levels, ranging from Face Recognition to Crowd Behavior (Slide 6).
The technical strategy focuses on Edge AI, supporting third-party cameras and Video Management Systems (VMS) to integrate with mobile devices (Slide 8).
Market validation is the strongest section, utilizing IHS Markit data to show China as the largest market with 567 million units by 2021 (Slide 4).
Competitive advantages are listed as Brand Awareness, System Integration, and R&D Capability, though no specific competitors are named (Slide 10).
The growth strategy includes future support for security drones and robots, alongside a customer advisory board (Slide 12).
The deck completely omits a team slide, financial projections, unit economics, and a specific funding ask (Slides 1-14).

Executive Summary: A Product-First Presentation Lacking Financial Rigor

The pitch deck for AI-Driven Large Space Security, presented by Jonathan Chen, focuses heavily on the technical capabilities of Edge AI and the massive growth of the global surveillance market. While the deck successfully identifies a large and growing Total Addressable Market (TAM) using reputable third-party data, it fails to transition from a product showcase to a business investment opportunity. The absence of a team, financial history, and a clear funding request makes it difficult for potential investors to assess the company's viability.

Slide 1: Title and Contact

The cover slide introduces the company's focus: "AI-Driven Large Space Security." The subtitle, "Ensure crowd health and safety in large event and place," sets a broad mission statement. Contact information for Jonathan Chen is provided, including a Gmail address and a phone number. The design is minimalist, using a dark textured background with yellow and white text. Notably, there is no company logo, which may indicate an early-stage or pre-incorporation status.

Slide 2: The Solution Overview

Slide 2 defines the core offering as "Intelligent Video Analytics (IVA) that can automatically aware crowd behavior in large space in real-time." The value proposition is broken down into three pillars: More Effective (7x24x365 monitoring), Save Money (reducing manpower), and More Safety (detecting breaches before they happen). While these are standard benefits for AI security, the slide lacks specific metrics or case studies to prove these claims.

Slide 4: Market Validation and Global Trends

This is the most data-dense slide in the deck. It utilizes a table from IHS Markit (issued November 2019) to show the "installed base for security cameras by region." The data points are specific: the global market was expected to grow from 655,815.9 thousand units in 2018 to 1,017,441.4 thousand units in 2021. This represents a 16% CAGR. The slide highlights China as the dominant market, growing from 348 million to 567 million units in the same period. This slide effectively validates that the underlying hardware infrastructure for the company's software is expanding rapidly.

Slide 6: Video Analytics Product Features

Slide 6 provides a functional hierarchy of the software. It categorizes features into three levels: Point (Face Recognition, Object Detection), Line (Object Tracking, Intrusion Detection, Perimeter Security), and Surface (Crowd Counting, Crowd Density, Crowd Movement, Crowd Behavior). All of these are powered by "Edge AI." This slide is useful for understanding the technical scope of the product, moving from simple identification to complex behavioral analysis.

Slide 8: Product Strategy and Integration

This slide illustrates the workflow of the technology. It shows images of crowds and events feeding into a central box labeled "Edge AI Video Analytics." The output is then sent to "3rd Party VMS" (Video Management Systems) and "Mobile Devices." The stated strategy is to "Start from edge" and "Support 3rd-party camera and VMS." This indicates a software-only or software-plus-gateway model that avoids the need for proprietary camera hardware, which is a scalable approach.

Slide 10: Competitive Advantage

Slide 10 lists three competitive advantages: Brand Awareness , System Integration Capability , and Research & Development Capability . On the right side, there are small, blurred images of what appear to be awards, a brand map, and a command center. However, the claims are generic. Claiming "Brand Awareness" as a competitive advantage without showing market share or recognizable logos is a weak point in the deck. There is no direct comparison to competitors like Hikvision, Avigilon, or newer AI startups.

Slide 12: Growth and Future Roadmap

The final analyzed slide outlines the growth plan. Customer Development focuses on learning from potential customers. Customer Service promises dedicated reps for each account. Product Development mentions staying competitive through a customer advisory board and expanding to support "security drones or robots." While these are logical steps, they lack a timeline or specific milestones (e.g., "Reach 100 enterprise customers by Q4").

What Works in This Deck

Market Data: The use of IHS Markit data provides a credible foundation for the pitch. It shows the founder understands the scale of the industry and where the hardware is being deployed. · Technical Clarity: The breakdown of Point, Line, and Surface features (Slide 6) clearly communicates what the AI actually does without getting bogged down in incomprehensible jargon. · Integration Strategy: The decision to support third-party hardware and VMS (Slide 8) is a smart business move that lowers the barrier to entry for customers who have already invested in camera infrastructure.

What is Missing

The Team: There is no mention of who is building this. Investors invest in people, especially in AI where specialized talent is a major differentiator. The absence of a team slide is a significant red flag. · The Business Model: How does the company make money? Is it a per-camera monthly fee? A one-time license? The deck is silent on unit economics and pricing. · Traction: There are no mentions of current pilots, revenue, or partnerships. The images used appear to be stock photos or generic event photos rather than screenshots of the actual software in use at a specific venue. · The Ask: The deck does not state how much money is being raised, the valuation, or what the funds will be used for. · Financial Projections: There are no forward-looking statements regarding revenue growth or profitability.

Founder Recommendations

If you are modeling your deck after this one, you should focus on filling the massive gaps in business logic. While the market data is strong, a pitch deck must prove that this specific company can capture that market. You must include a team slide highlighting technical and commercial expertise. Furthermore, replace generic advantages like "Brand Awareness" with specific moats, such as proprietary datasets, patents, or exclusive distribution partnerships. Finally, always include a clear financial slide that shows your path to revenue and exactly what you need from an investor to get there.

Frequently asked questions

What is the primary problem this company is trying to solve?: Based on Slide 2, the company aims to address the limitations of manual security monitoring in large spaces. It proposes using AI to monitor 'every corner 7x24x365,' reducing the need for repetitive human tasks and detecting security breaches before they occur. However, the deck lacks a dedicated 'Problem' slide to quantify the cost or frequency of these security failures.
How does the product actually work technically?: The deck specifies an 'Edge AI' approach on Slide 8. This means the video analytics are processed locally rather than solely in the cloud. The system is designed to be hardware-agnostic, supporting third-party cameras and integrating with existing Video Management Systems (VMS) and mobile devices. Slide 6 breaks down the capabilities into specific AI tasks like object tracking and intrusion detection.
What market data does the company use to justify its existence?: On Slide 4, the company cites data from IHS Markit issued in November 2019. It highlights that the global installed base for security cameras was projected to grow from 655.8 million units in 2018 to over 1 billion units in 2021. This represents a 16% CAGR, with China accounting for more than half of the global market.
What is missing from the business model and growth sections?: The deck is missing almost all standard financial metrics. There is no mention of pricing strategy (SaaS vs. licensing), customer acquisition costs, or current revenue. While Slide 12 mentions 'Customer Development' and 'Customer Service,' it does not list any current pilot programs, signed contracts, or specific target industries beyond the general 'large event and place' mentioned on the cover.
Is this a complete pitch deck for a seed round?: No. This deck functions more as a product overview or a sales presentation. It lacks the essential components of a fundraising deck, most notably a Team slide, a Financials slide, and an 'Ask' slide detailing how much capital is being raised and how it will be deployed. Without these, an investor cannot evaluate the execution risk or the potential return.

Cover slide of the AI-Driven Large Space Security pitch deck — 2019 — AI-Driven Large Space Security pitch deck, slide 1 (2019)

AI-Driven Large Space Security pitch deck: the facts

Company: AI-Driven Large Space Security
Year: 2019 (based…
Stage: Unknown (likely Seed or Pre-Seed)
Slides: 14
Sector: AI / Security / Video Analytics
Deck type: Product/Sales Overview
Headquarters: Unknown (Contact name Jonathan Chen / 陳昭斌 suggests Taiwan or East Asia)

AI-Driven Large Space Security pitch deck PDF

The full AI-Driven Large Space Security 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 AI-Driven Large Space Security pitch deck was used for

AI-Driven Large Space Security is presented in a 14‑slide 2019 pitch deck hosted on Slideshare for an AI‑driven video analytics platform targeting large physical spaces and security use cases. The deck positions the company as a provider of AI video analytics software and hardware, sold via integrators, focused single‑step providers, and consulting services. The fundraising stage is not explicitly stated but appears consistent with a Seed or Pre‑Seed round aimed at building out product, go‑to‑market through integrator partnerships, and consulting capabilities. The teardown notes strong market validation content but limited disclosure of financials, funding ask, and team credentials.

What the AI-Driven Large Space Security deck got right

Defined go-to-market via integrators and consulting services. The deck presents a clear multi‑channel business model slide, outlining roles for integrators, focused single‑step providers, and consulting services in bringing video analytics technology to market, which helps investors understand how the product will reach customers.
Robust market validation content. The teardown notes strong third‑party market validation, suggesting the deck includes external data or references that substantiate demand for AI‑driven video analytics in large space security applications.
Compelling problem focus on large space security. Positioning around AI‑driven video analytics for large physical spaces taps into a well‑understood and growing problem domain in physical security, making the core value proposition intuitively appealing to investors.
Service layer to support adoption and value realization. The inclusion of consulting services alongside SW/HW solutions indicates an awareness of customer adoption challenges and a willingness to provide strategic recommendations, potentially improving early deployment success and customer satisfaction.
Single-source solution positioning through partners. Framing the offering as giving customers a single source for surveillance technology needs through integrators and focused providers suggests an integrated solution approach that can simplify procurement and deployment for end customers.

What could have been stronger

Absence of financial metrics and projections. The deck does not present specific financial data such as current revenue, pipeline value, unit economics, or projected financials, leaving investors unable to quantify the commercial opportunity or assess execution performance.
Limited disclosure of team experience. The teardown explicitly notes a lack of clear team credentials in the deck, suggesting that investor‑critical information about founders’ backgrounds, technical expertise, and prior execution history is either missing or too cursory.
No clear fundraising ask or round definition. The deck does not state a concrete funding ask, target round size, use of proceeds, or implied valuation, making it difficult for investors to understand what capital is needed and how it will be deployed.
Under‑specified business model economics. Although the business model slide outlines integrators, focused providers, and consulting services, it does not quantify pricing, margins, or how these channels combine into a coherent, scalable revenue model, leaving questions about eventual profitability and focus.
Limited articulation of technological differentiation and moat. The deck appears to rely heavily on third‑party market validation and generic descriptions of AI video analytics for security, without clearly highlighting proprietary technology, defensibility, or differentiated performance benchmarks.

How an investor would read this deck

What draws attention

Channel strategy built around security and surveillance integrators, including how integrator partnerships are sourced, structured, and scaled
Real‑world deployments of the AI video analytics platform in large spaces and evidence of accuracy, false positive rates, and operational impact
Depth of the founding and technical team’s background in computer vision, real‑time video analytics, and enterprise security sales
Clarity and focus of the revenue model across integrator partnerships, product licensing, and consulting services
Market size and urgency of demand for AI‑driven video analytics in large physical spaces, and how the company positions against established vendors

Risks that stand out

Highly competitive AI video analytics and physical security market with many established vendors and integrators, increasing the risk of commoditization and price pressure
Dependence on integrator partnerships for distribution could slow growth if partner adoption is limited or if integrators prioritize incumbent vendors
Lack of detailed financial and traction data in the deck may indicate an early stage with limited commercial validation, heightening execution risk
Regulatory and privacy concerns around AI‑driven video analytics and surveillance in large public or semi‑public spaces could constrain deployments
Complex deployments across large physical environments may require substantial consulting and support, potentially weighing on margins and scalability if not productized effectively

Questions this deck invites

What specific AI and computer vision techniques does the platform use, and how does its performance compare to incumbent video analytics solutions on core benchmarks such as detection accuracy and latency?
How many paying customers or pilots with integrators and end‑users were live at the time of the deck, and what were the key usage metrics (camera count, sites, alerts processed, accuracy)?
How is revenue split between product/software licensing, hardware, and consulting services, and which segment is expected to drive long‑term growth?
What is the company’s go‑to‑market motion with integrators—are there signed distribution agreements, and what are typical deal sizes, sales cycles, and margins?
What capital is being raised, how will it be allocated between product development, sales/partnerships, and deployment support, and what milestones are promised for the next 18–24 months?

What founders can take from the AI-Driven Large Space Security deck

An AI‑driven security deck benefits from clearly articulating a channel strategy—this deck’s business model slide shows how integrators, focused providers, and consulting services can be combined, but investors still need quantitative detail to fully assess scalability.
Strong third‑party market validation alone does not replace the need for financial metrics and a clear funding ask; investors expect to see concrete numbers alongside market proof points.
Early‑stage AI video analytics companies should emphasize both technical differentiation and operational outcomes (e.g., reduced incidents, faster response) to stand out in a crowded security market; this deck leaves those specifics underdeveloped.
Reliance on consulting services as part of the business model can help customers adopt complex AI solutions but must be framed carefully so investors see a path to product‑led scalability rather than labor‑intensive engagements.
Including explicit team credentials and prior accomplishments is critical in security and AI; when these are missing or vague, it becomes significantly harder for investors to underwrite execution risk.
A clear and quantified funding ask, paired with specific milestones and use of funds, is necessary for investors to understand how capital will translate into product maturity, integrator traction, and revenue growth; its absence is a notable weakness in this deck.

AI-Driven Large Space Security pitch deck: common questions

What does AI-Driven Large Space Security do according to the pitch deck?

The deck describes AI‑Driven Large Space Security as an AI‑driven video analytics platform focused on securing large physical spaces, using software and hardware to analyze surveillance video and provide actionable security insights through integrators and consulting services.

When was this pitch deck created and what stage was the company at?

The pitch deck is dated around 2019 based on the market data shown and consists of 14 slides hosted on Slideshare under the title "AI-Driven Large Space Security." The stage is not explicitly stated, but the content and level of detail suggest an early Seed or Pre‑Seed fundraising context.

What fundraise is this deck associated with and what are the terms?

The Slideshare deck presents an AI‑driven video analytics platform for large spaces, with business model details around working through system integrators, focused single‑step providers, and offering consulting services; however, it does not clearly state how much the company is raising, at what valuation, or with which investors.

How does AI-Driven Large Space Security plan to get its product to market?

The deck emphasizes partnerships with security and surveillance integrators, collaboration with focused single‑step providers to embed video analytics into their offerings, and consulting services that help customers adopt and leverage video analytics technology as part of broader security and operational strategies.

What are the main strengths and weaknesses of this pitch deck?

The teardown notes that while the deck offers strong third‑party market validation for AI‑driven video analytics in security, it lacks specific financial metrics, clear team credentials, and a well‑defined funding ask, so the company’s traction and fundraising progress cannot be inferred from the deck alone.

Sources

Funding and outcome facts on this page were researched on 2026-08-22 from the pages below.

AI-Driven Large Space Security pitch deck slides

AI-Driven Large Space Security pitch deck slide 1 of 14 — AI-Driven Large Space Security pitch deck — slide 1 of 14

AI-Driven Large Space Security pitch deck slide 2 of 14 — AI-Driven Large Space Security pitch deck — slide 2 of 14

AI-Driven Large Space Security pitch deck slide 3 of 14 — AI-Driven Large Space Security pitch deck — slide 3 of 14

AI-Driven Large Space Security pitch deck slide 4 of 14 — AI-Driven Large Space Security pitch deck — slide 4 of 14

AI-Driven Large Space Security pitch deck slide 5 of 14 — AI-Driven Large Space Security pitch deck — slide 5 of 14

AI-Driven Large Space Security pitch deck slide 6 of 14 — AI-Driven Large Space Security pitch deck — slide 6 of 14

What each slide of the AI-Driven Large Space Security pitch deck says

Slide 1

PITCH DECK Al-Driven Large Space Security Ensure crowd health and safety in large event and place

Slide 2

1 CROWD SAFETY PROBLEM pe 222 pobken Lots of Security Guards are required to ensure n - Js oy pe crowd safety in large places such as stadium, 2s earl campus, park, square, convention center, etc. fh PORE pone Human attention drops off very quickly to make him . Lasgs miss critical incidents. Sei, Kid, 3 Iz ix Competion Not easy to monitor crowds in a large place, - “fn 7 24 Tew especially in large event. BP cle ALT A oc FN 3 Ag 1s aa = x » hb an i

Slide 4

3 MARKET VALIDATION 2015 Population 2018 Population per camera per camera China 6.8 4.1 USA 6.9 4.6 Taiwan 79 5.5 UK and Eire 8.2 6.5 Singapore 12.8 7A Data issued: November 2019 Source: 2019 Informa Tech LLC ©2019 Informa Tech LLC.

Slide 5

4 MARKET VALIDATION The installed base for security cameras by region: World Problem Installed base units (thousands; 2018 2021 Solution EMEA 88,254.2 131,946.7 — Americas 121,227.1 168,198.8 f Product : China 348,617.9 567,239.8 Business Model tik Asia (excluding China) 97,716.7 150,056.1 Growth Total pr Installed base units (thou 655,815.9 1,017,441.4 2018-2021 Data issued: November 2019 i CAGR 16% Source: IHS Markit © 2019 IHS Markit

Slide 6

5 MARKET SIZE pom Global Video Analytics Market Size, 2015-2026 (USS Million) ge 215 2016 2017 2018 2019 2020 2021 22 223 22 2025 202 www. fortunebusinessinsights.com (Source: http://www. fortunebusinessinsights.com/industry-reports/video-analytics-market-101114)

Slide 10

9 BUSINESS MODEL Integrators Giving customers a single source for all their surveillancetechnology needs. Focused single-step providers Partner with integrators to provide video analytics technology, marketing and customer support so as to reduce a large sales team. Consulting services In addition to providing SW/HW solutions, consultants also make business recommendations to leverage video analytics technology.

Slide text above is read directly from the AI-Driven Large Space Security deck PDF embedded on this page.