What is the core technology behind Link-Up?

Based on slide 3, Link-Up utilizes AI video analytics, specifically skeletal tracking (labeled as OpenPose in the screenshot) to monitor personnel in manufacturing environments. The software processes live video at approximately 6.6 frames per second to analyze human motion and identify inefficiencies in real-time.

What specific problem does Link-Up solve for manufacturers?

Link-Up targets 'Non-Value Added time.' According to slide 2, this includes needless time caused by excessive motions in personnel and time spent waiting on materials or idle equipment. Slide 5 demonstrates their ability to quantify this, showing a task where 44% of the time was classified as 'misused' relative to a target cycle time.

Is there any information on the founding team?

No. The 7-slide deck completely omits a team slide. There is no mention of the founders' names, technical backgrounds, or previous experience in AI or manufacturing. The only identifying information is a general contact email and a physical address in Seoul, South Korea, provided on slide 7.

Does the deck include a market size or financial ask?

The deck contains no information regarding the Total Addressable Market (TAM), the business model (SaaS vs. Licensing), or the amount of capital being raised. It is a product-focused presentation likely intended for a demo day environment rather than a full venture capital due diligence process.

How does Link-Up define productivity?

Link-Up defines productivity through motion analysis. On slide 4, the AI analytics interface distinguishes between 'Idle' and 'Productive' states. Slide 6 further explains that the system 'catches' human error and delays by recording steps and creating a database, which is then used to 'improve' product quality and reduce costs.

Link-Up Pitch Deck (2018): 7-Slide Breakdown

See all 7 slides of the Link-Up pitch deck — a 2018 deck — with a slide-by-slide teardown of what the deck does well and where it falls short.

Link-Up’s pitch deck, presented at Draper University, is a brief, product-centric presentation that identifies a specific industrial pain point: wasted time in manufacturing. By citing the World Economics Forum's 2018 Future of Jobs Report, the company establishes a baseline for human-machine task distribution (71% human vs. 29% machine). The deck relies heavily on screenshots of its 'Live Streaming' and 'AI Analytics' interfaces to prove technical capability, specifically highlighting how it identifies 'Idle' versus 'Productive' time. While it successfully demonstrates a 44% misuse of task t…

Key takeaways

The deck identifies 'Motion' and 'Waiting' as the primary sources of manufacturing waste on slide 2.
Link-Up uses the World Economics Forum's 2018 Future of Jobs Report to frame the automation landscape on slide 2.
Technical validation is provided through a 'Live Streaming' slide showing OpenPose-style skeletal tracking at 6.6 fps on slide 3.
The software categorizes worker activity into binary states: 'Idle' (highlighted in red) and 'Productive' (highlighted in green) on slide 4.
A specific data organization example on slide 5 claims that 3.35 seconds, or 44% of a task, was misused.
The value proposition is split into two phases: 'Catch' (recording and locating delays) and 'Improve' (analysis and cost reduction) on slide 6.
The company is headquartered in Seoul, Republic of Korea, as stated on the final contact slide.
The deck omits critical investor information, including the business model, competition, and financial projections.

Link-Up Pitch Deck Teardown

The Link-Up pitch deck, originating from a Draper University program, is a minimalist 7-slide presentation. It focuses almost exclusively on the technical application of AI video analytics within a factory setting. While it lacks the traditional narrative arc of a seed-stage deck—omitting sections on market size, competition, and team—it provides a clear visual demonstration of the product's utility in identifying industrial waste.

Slide 1: Title Slide

The cover slide introduces the company name, Link-Up, and its core mission: "Awaken Productivity Through AI Video Analytics." The imagery is industrial, featuring a factory assembly line, a stock market candlestick chart, and cloud computing icons. This immediately signals that the company operates at the intersection of manufacturing (IoT/Edge) and data analytics.

Slide 2: The Problem and Market Context

Link-Up uses a quote from Carl Sandburg ("Time is the coin of your life") to frame the problem of inefficiency. More importantly, it cites the World Economics Forum's "Future of Jobs Report 2018," which notes that in 2018, the rate of automation was 71% human to 29% machine. This suggests a massive opportunity for human-centric optimization. The slide identifies two specific types of waste: Motion (wasted effort related to unnecessary movements) and Waiting (waste from time spent waiting for the next process step or idle equipment). These are classic Lean Manufacturing principles (Muda), which Link-Up intends to solve via software.

Slide 3: Live Streaming and Technical Proof

This slide serves as a technical proof of concept. It shows a screenshot of the software in action, utilizing skeletal tracking (OpenPose) on two factory workers. The interface displays a frame count (135) and a processing speed of 6.6 fps (frames per second). By showing the colorful skeletal overlays on the workers, the deck proves that the company has a functional computer vision model capable of identifying human posture and movement in a cluttered industrial environment.

Slide 4: AI Analytics Interface

Slide 4 demonstrates the output of the AI. It shows a split-screen interface where the software categorizes activities. On the left, a worker's state is labeled "Idle" in red; on the right, the state is labeled "Productive" in green. This binary classification is the foundation of their analytics engine, allowing managers to see exactly when and where time is being lost on the floor without manually reviewing hours of footage.

Slide 5: Data Organization and Case Study

This is the most data-dense slide in the deck. It shows a dashboard titled "Manufacturing / List / Hinge assembly / Measure / Graph." It breaks down a specific task (unloading a bracket from a press machine) and identifies that 3.35 seconds, or 44% of the task, was "misused." The bar chart compares "Current T/T" (Total Time) against a "Target T/T," highlighting the "Non-Value Added time" in red. This slide is critical because it moves the conversation from "we can track skeletons" to "we can provide actionable ROI by identifying specific seconds of waste."

Slide 6: The Value Proposition

Link-Up summarizes its workflow into two categories: Catch and Improve . Under "Catch," they list recording steps, creating a database, locating delays, and removing human error. Under "Improve," they promise comprehensive analysis, revamped productivity, reduced costs, and improved product quality. The visual uses arrows pointing up for efficiency and quality, and an arrow pointing down for cost, providing a standard business value summary.

Slide 7: Contact Information

The final slide provides a URL (link-up.co.kr), a phone number, a Gmail address, and a physical address in Seoul, Republic of Korea. The presence of a Korean address and a .co.kr domain suggests the company is targeting the robust manufacturing sector in East Asia.

What Link-Up Does Well

The deck excels at visual evidence . Instead of describing what AI video analytics could do, slides 3, 4, and 5 show the software actually doing it. The use of skeletal tracking screenshots and specific time-waste graphs (down to the hundredth of a second) builds immediate technical credibility. By focusing on "Non-Value Added time," they are speaking the language of factory managers and Lean Six Sigma practitioners, which makes the product's utility very clear.

What is Missing from the Deck

This deck is significantly incomplete by standard venture capital standards. The following elements are entirely missing:

Team Slide: There is no information on who is building this. In AI startups, the pedigree of the engineering team is often the most important factor for early-stage investors. · Market Size (TAM/SAM/SOM): The deck doesn't quantify how many factories could use this or what the total market opportunity is. · Business Model: It is unclear if this is a per-camera subscription, a per-factory license, or a consultancy-based model. · Competition: The deck does not mention other players in the computer vision or industrial IoT space. · The Ask: There is no mention of how much money the company is raising or what the milestones for the next 18 months are.

Founder Takeaways

Show, Don't Just Tell: Link-Up’s decision to include actual software screenshots with skeletal tracking and data dashboards is a strong move. For technical founders, showing a functional UI/UX is often more persuasive than a dozen slides of bullet points about "proprietary algorithms."

Identify a Specific Metric: By focusing on "3.35 seconds of misused time," the founders make the abstract concept of "productivity" concrete. When pitching, finding a single, relatable metric that your product impacts can help anchor the investor's understanding of your value.

Contextualize with Reputable Data: Using the World Economics Forum report on slide 2 helps validate that the problem Link-Up is solving is a recognized global trend, not just a niche observation. This adds a layer of professional gravity to the presentation.

Frequently asked questions

What is the core technology behind Link-Up?: Based on slide 3, Link-Up utilizes AI video analytics, specifically skeletal tracking (labeled as OpenPose in the screenshot) to monitor personnel in manufacturing environments. The software processes live video at approximately 6.6 frames per second to analyze human motion and identify inefficiencies in real-time.
What specific problem does Link-Up solve for manufacturers?: Link-Up targets 'Non-Value Added time.' According to slide 2, this includes needless time caused by excessive motions in personnel and time spent waiting on materials or idle equipment. Slide 5 demonstrates their ability to quantify this, showing a task where 44% of the time was classified as 'misused' relative to a target cycle time.
Is there any information on the founding team?: No. The 7-slide deck completely omits a team slide. There is no mention of the founders' names, technical backgrounds, or previous experience in AI or manufacturing. The only identifying information is a general contact email and a physical address in Seoul, South Korea, provided on slide 7.
Does the deck include a market size or financial ask?: The deck contains no information regarding the Total Addressable Market (TAM), the business model (SaaS vs. Licensing), or the amount of capital being raised. It is a product-focused presentation likely intended for a demo day environment rather than a full venture capital due diligence process.
How does Link-Up define productivity?: Link-Up defines productivity through motion analysis. On slide 4, the AI analytics interface distinguishes between 'Idle' and 'Productive' states. Slide 6 further explains that the system 'catches' human error and delays by recording steps and creating a database, which is then used to 'improve' product quality and reduce costs.

Cover slide of the Link-Up pitch deck — 2018 — Link-Up pitch deck, slide 1 (2018)

Link-Up pitch deck: the facts

Company: Link-Up
Year: 2018 (based…
Stage: Early Stage (Draper University)
Slides: 7
Sector: AI Video Analytics / Manufacturing
Deck type: Demo Day / Pitch Deck
Headquarters: Seoul, Republic of Korea

Link-Up pitch deck PDF

The full Link-Up 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 Link-Up pitch deck was used for

Link-Up is a Draper University early-stage startup that presented a 7-slide pitch deck focused on using **AI video analytics** to analyze recorded manufacturing tasks and improve production-line efficiency. The deck, hosted on SlideShare and dated April 2020, describes an AI program that performs skeletal and motion analysis on workers’ live-streamed manufacturing tasks to compute precise time allocations for each production step and identify wasted versus productive motion. The company claims to have “automatized a century old process,” producing comparative charts and line-of-balance graphs that highlight bottlenecks and delays in order to optimize workstation balance and overall process efficiency. This appears to be a program or demo-day style deck associated with Draper University rather than a publicly documented equity financing round.

What the Link-Up deck got right

Clear articulation of automating traditional time-and-motion studies. The deck clearly states that Link-Up’s AI analyzes live-streamed manufacturing tasks, captures skeletal motions, computes precise time allocations per production step, and classifies time as productive or wasted; this direct mapping to the traditional time-and-motion study process makes the value proposition concrete for manufacturing stakeholders.
Compelling technical narrative around AI-driven motion analysis. By emphasizing features like skeletal muscle analysis, meticulous motion capture, and detailed time computation for each production step, the deck showcases a technically ambitious and differentiated approach within AI video analytics for manufacturing processes.
Strong emphasis on visual, actionable analytics outputs. The deck highlights output in the form of comparative analysis charts and comprehensive graphs, including line-of-balance style visuals with average lines, which give managers intuitive tools to identify delays and rebalance workstations. This focus on actionable visual outputs increases perceived practical utility.
Effective framing as modernization of an established industrial process. The narrative that Link-Up has ‘automatized a century old process’ positions the product as a modern evolution of established industrial engineering practices, helping investors and manufacturing managers quickly understand the innovation relative to existing methods.
Product-focused storytelling appropriate for early-stage technical audiences. The SlideShare description notes that the deck concentrates on product evidence—such as how footage is analyzed and how data is graphed—rather than generic metrics, which aligns well with an early-stage, technical audience at Draper University and demonstrates the core technical insight.

What could have been stronger

Lack of externally validated traction or customer evidence. The SlideShare-hosted Draper University deck focuses heavily on narrative descriptions of how AI video analytics could optimize manufacturing processes, but it does not provide verifiable evidence of existing paying customers, pilot deployments, or quantified traction such as number of factories monitored or hours of video analyzed. Without externally corroborated case studies or adoption metrics, investors would struggle to assess real-world validation.
No independent validation of AI capabilities. Although the deck describes features like skeletal motion analysis, comparative charts, and line-of-balance graphs, it does not reference any published benchmarks, technical papers, or third-party evaluations that would independently substantiate the claimed AI capabilities or accuracy. This makes it difficult for a technical or industrial investor to evaluate feasibility and robustness.
Go-to-market and deployment strategy not articulated in the deck. The available deck does not disclose pricing models, implementation timelines, or integration approaches with existing manufacturing systems such as MES, ERP, or camera infrastructure. For an enterprise-focused manufacturing analytics tool, the absence of a clearly articulated go-to-market and deployment strategy limits investors’ ability to understand commercialization risk.
Target customer segment and initial niche are not clearly defined. The pitch emphasizes that Link-Up has ‘automatized a century old process’ and focuses on manufacturing efficiency as a broad concept, but it does not narrow down a specific initial segment (for example, automotive, electronics assembly, or particular types of repetitive tasks) where the solution is optimized and demonstrably superior. This broad positioning could raise questions about focus and initial beachhead market selection.
Limited competitive differentiation presented in the available materials. The deck, as captured on SlideShare, does not appear to include a detailed competitive landscape, and external research did not uncover a separate company site or independent comparison; in a space where many AI video analytics vendors operate, investors would want to see a differentiated positioning, which is not fully articulated in the available slides.

How an investor would read this deck

What draws attention

Evidence that the AI skeletal motion and time-allocation analysis described in the deck has been implemented in working prototypes or pilots, including demo videos or third-party testimonials from manufacturing partners.
Quantitative data demonstrating that applying Link-Up’s analytics reduces cycle time, improves line-of-balance, or increases throughput in specific manufacturing cells or lines.
Clarification of the initial target verticals and use cases, particularly where existing camera infrastructure and processes make deployment more straightforward and ROI easier to measure.
Details about the technical stack (e.g., on-premise vs. cloud processing, model training approach, edge hardware requirements) and how it integrates with existing factory systems.

Risks that stand out

The absence of any corroborating public information about Link-Up as a standalone company or about specific funding rounds suggests the possibility that this project did not progress beyond a Draper University or demo-day stage, increasing continuation risk.
Highly specialized AI video analytics for manufacturing is a competitive field, and more established vendors with existing deployments may already serve the target customers, potentially crowding out new entrants without strong differentiation.
The solution’s dependence on consistent, high-quality video capture and integration with factory systems could pose implementation challenges, especially in brownfield plants with heterogeneous infrastructure; without demonstrable integration wins, deployment risk remains high.

Questions this deck invites

Can you provide names and references (even under NDA) of factories or production lines where Link-Up’s AI video analytics has been deployed, and what measurable improvements were achieved?
What specific computer vision and pose-estimation techniques are used in your skeletal motion analysis, and how do your accuracy and inference speed compare with open-source baselines or industry benchmarks?
How does your solution integrate with existing camera systems and manufacturing IT/OT infrastructure, and what is the typical deployment timeline from contract to value realization?
What is your pricing and business model (for example, per-camera subscription, per-line license, or outcome-based pricing), and how have early customers responded to it?
Who are your main competitors in AI video analytics for manufacturing, and what capabilities or business advantages (such as lower implementation cost, better ergonomics insight, or specialized line-of-balance analytics) differentiate Link-

What founders can take from the Link-Up deck

A concise deck that uses concrete operational language—such as ‘skeletal muscle analysis,’ ‘time allocated in each of the production steps,’ and ‘line of balance’—can quickly signal domain expertise in manufacturing process engineering.
Demonstrating how AI transforms a well-known, ‘century old’ process (like traditional time-and-motion studies) into an automated, data-driven workflow can create a compelling modernization narrative in industrial contexts.
Visual artifacts like comparative charts and line-of-balance graphs derived from video analytics can help non-technical stakeholders intuitively see process bottlenecks and understand the value proposition without needing to understand model architectures.
Positioning video as a data source for operational analytics, rather than as mere surveillance footage, aligns with broader industry narratives around AI video analytics and can resonate with manufacturing managers and investors focused on efficiency.
Tying an AI analytics solution to roles and responsibilities within the factory (for example, giving manufacturing managers tools to ‘catch the setbacks’ and improve line balance) can clarify who the buyer and primary user will be, even if the deck does not fully elaborate the go-to-market strategy.

Link-Up pitch deck: common questions

What does Link-Up’s product do according to the Draper University pitch deck?

The Link-Up pitch deck describes an AI video analytics system that analyzes recorded or live-streamed footage of manufacturing workers’ tasks to compute precise times for each step, distinguish productive from wasted motions, and help managers identify bottlenecks and delays on the production line.

When and in what context was the Link-Up pitch deck presented?

The deck is labeled as a Draper University pitch deck and is hosted on SlideShare with a date of April 28, 2020; the user’s existing report associates it with an early-stage Draper University context around 2018, which is consistent with a training or demo-day environment rather than a later-stage fundraise.

Is there any public information about Link-Up’s investors or funding round?

The available SlideShare deck focuses on product concept and process optimization narrative and does not include any externally verifiable information on investors, funding amounts, or specific round terms; no separate funding announcements, investor profiles, or press releases were found that clearly match this Link-Up AI manufacturing analytics startup.

How does Link-Up claim to improve manufacturing efficiency?

The deck explains that Link-Up’s AI system captures skeletal motions from video, calculates how much time is spent on each motion, and then classifies that time into productive versus impractically wasted categories; this data is then visualized in graphs and line-of-balance style charts so managers can see where time is being lost and adjust processes.

Is Link-Up still active or operating as a company today?

There is no separate company website, press coverage, or corporate profile that can be definitively tied to this specific Link-Up AI manufacturing analytics project beyond the Draper University pitch deck itself, so any later-stage outcomes or current operational status cannot be confirmed from the available sources.

Sources

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

Link-Up pitch deck slides

Link-Up pitch deck slide 1 of 7 — Link-Up pitch deck — slide 1 of 7

Link-Up pitch deck slide 2 of 7 — Link-Up pitch deck — slide 2 of 7

Link-Up pitch deck slide 3 of 7 — Link-Up pitch deck — slide 3 of 7

Link-Up pitch deck slide 4 of 7 — Link-Up pitch deck — slide 4 of 7

Link-Up pitch deck slide 5 of 7 — Link-Up pitch deck — slide 5 of 7

Link-Up pitch deck slide 6 of 7 — Link-Up pitch deck — slide 6 of 7

What each slide of the Link-Up pitch deck says

Slide 1

Bh / i l mn - JL ) | Awaken Productivity Through Al Video Analytics

Slide 2

». "Timeis the coino your life” a -Carl Sandburg ned’ 4 Source: Future of Jobs Report 2018. World Economics Forum. No = time and / (] caused by @ coat bE 9 in personnel and f E L SSG “1 tomated machines must be aN designed. A+ py ’ Pac x] (9) CSM@&nufacturers need to minimize the > “Uy \ I" employees spend on WAIT haterials and idle equipment.

Slide 6

Link Up -— . Catch Improve * Record the steps. = Receive a comprehensive analysis. « Create a database. » Revamp productivity. « Locate the delays. « Reduce cost. + Remove human error. « Improve product quality. Your business with Link-up

Slide text above is read directly from the Link-Up deck PDF embedded on this page.