What is the primary problem Academig is trying to solve?

Academig addresses the fragmented nature of academic data. Currently, researchers must navigate poorly updated lab websites, ResearchGate, Google Scholar, and various job portals to find opportunities. As shown on Slide 5, this often results in researchers manually creating complex Excel files to track lab information, leading to inefficiency and the fear of missing out on high-quality opportunities.

How does Academig plan to make money?

The deck outlines a two-sided revenue model on Slide 14. For the 'demand' side, students and researchers pay a $7 monthly subscription. For the 'supply' side, research labs pay between $99 and $550 for job postings and $200 for 'lab building services.' Slide 15 also suggests future revenue from departmental plans and data licensing through an API.

What is the current traction of the platform?

Slide 16 displays a growth chart starting in May 2019. By April 2020, the platform reached 4,000 users and 400 research labs. The deck projects a significant 'hockey stick' growth curve aiming for 50,000 users and 10,000 research labs by December 2020, though it does not specify the current month of the pitch.

Who are the competitors mentioned in the deck?

The deck identifies ResearchGate, Academia.edu, and Google Scholar as existing platforms in the space on Slide 12. However, it differentiates Academig by stating these competitors focus on individual researchers, whereas Academig is the first platform dedicated solely to finding and listing research labs.

What is missing from this pitch deck?

The most notable omission is a specific financial 'Ask.' Slide 17 mentions looking for 'Business development and valuations' and 'Connections,' but does not state a fundraising target (e.g., $500k for 18 months of runway). Additionally, there is no detailed breakdown of the 'Human factor' in their data scraping process or a competitive matrix beyond a high-level focus comparison.

Academig Pitch Deck: 19-Slide Breakdown

See all 19 slides of the Academig pitch deck — an EdTech deck — with a slide-by-slide teardown of what the deck does well and where it falls short.

Academig’s pitch deck is a textbook example of the 'founder-problem fit' narrative. By spending the first eight slides on the personal struggle of finding a PhD position, the founders establish deep empathy for their target user. The solution positions itself as the 'Crunchbase for academia,' aiming to index 8,000,000 research labs. While the deck excels at defining the pain point and the two-sided revenue model—charging students $7/month and labs up to $550 for job postings—it lacks a specific financial 'Ask' and detailed unit economics. The traction slide shows promising growth toward a 50,…

Key takeaways

The deck uses a heavy narrative opening, dedicating slides 2 through 8 to a personal story about the difficulty of finding PhD positions in Germany.
Academig identifies a specific market gap: while ResearchGate and Google Scholar focus on individual researchers, Academig focuses on research labs (Slide 12).
The company estimates a total market of 8,000,000 research labs globally (Slide 12).
The business model is a two-sided network, charging researchers a $7 monthly subscription and labs $99-$550 for job postings (Slide 14).
Future revenue streams include institutional plans for departments and data licensing via an API (Slide 15).
Traction data shows a growth trajectory from May 2019, targeting 50,000 users and 10,000 research labs by December 2020 (Slide 16).
The 'Ask' is non-financial, listing 'Team,' 'Network,' and 'Business' development rather than a specific dollar amount (Slide 17).
The founding team possesses high academic credentials, including two PhDs in Quantum Physics and Stem Cell Biology (Slide 18).

The Narrative Arc: From Personal Pain to Platform Solution

Academig’s pitch deck is structured as a journey. It avoids the traditional 'Problem/Solution' bullet points in favor of a chronological story. This approach is highly effective for early-stage startups where founder-market fit is a primary concern for investors. By the time the viewer reaches the product slides, the founders have already proven they understand the user's frustration because they lived it.

Slides 1-8: The Problem as a Story

Slide 1 is a minimalist title slide featuring the Academig logo. Slide 2 introduces the protagonist: a Master's graduate in neuroscience from Tel Aviv University determined to find a PhD in Germany. This sets a specific, relatable context. Slide 3 illustrates the initial step—a Google search for 'Neuroscience research labs in Germany'—and identifies the three pillars of the search: Open Positions, Academic Partners, and Research Labs.

Slide 4 visualizes the chaos of the current process, showing a user juggling icons for ResearchGate, Google Scholar, emails, and poorly updated lab websites. Slide 5 provides 'proof of pain' by showing an actual complex Excel spreadsheet used to track lab data, including columns for 'Research Interests,' 'Lab Website,' and 'ORCID.' This is a powerful visual because it represents a manual process that is ripe for automation.

Slide 6 and 7 focus on the emotional toll, using phrases like 'I got upset and tired' and a missing puzzle piece graphic to represent the fear of missing 'amazing labs and great opportunities.' Slide 8 broadens the scope, providing testimonials from other researchers in Germany and Greece to prove this isn't just a personal problem, but a systemic one.

Slides 9-13: Defining the Solution and Market

Slide 9 acts as a transition, showing a field of flowers with the text 'In search for a better, more organized system.' Slide 10 defines the goal: helping researchers find all the academic data they need through a combination of 'Scraping,' 'Data-mining,' and the 'Human factor' to create structured data. This is the first hint at the technical backend of the platform.

Slide 11 provides a clear differentiation. It lists ResearchGate, Academia.edu, and Google Scholar as platforms that 'Focus on individual researchers,' while Academig 'Focuses on research labs.' It claims to be the 'world’s 1st platform dedicated solely to finding and listing research labs,' citing a total market of 8,000,000 labs. Slide 12 uses a 'Crunchbase for X' analogy, comparing Academig’s role in academia to Crunchbase’s role in the business world. This is a shorthand that investors immediately understand.

Slides 14-15: The Business Model

Slide 14 breaks down the 'Pricing Model' for a two-sided network. Students and researchers are charged a $7 monthly subscription. Labs are charged for 'Jobs posting' ($99-$550) and 'Lab building services' ($200). This shows a diversified revenue stream, though the deck does not explain what 'Lab building services' entails. Slide 15 looks forward, identifying 'Departments' (institutional plans) and 'Data Licensing' via an API as future revenue sources. The mention of 'Academic trends and analytics' suggests the founders see the long-term value in the data they are aggregating, not just the marketplace fees.

Slides 16-19: Traction, Team, and The Ask

Slide 16 presents a traction graph. The solid line shows growth from May 2019 to April 2020, reaching 4,000 users and 400 research labs. A dotted line projects a sharp increase to 50,000 users and 10,000 labs by December 2020. While the growth is visually impressive, the lack of a 'current' marker makes it difficult to tell if they are currently on track for that projection.

Slide 17 , titled 'What we are looking for,' is surprisingly vague. It lists 'Expand our team,' 'Connections with mentors and investors,' and 'Business development and valuations.' The absence of a specific dollar amount or equity offer is a missed opportunity to drive a concrete investment conversation. Slide 18 introduces the team: Rony Pozner (PhD in Quantum Physics) and Tatyana Pozner (PhD in Stem Cell Biology). Their academic pedigree is impeccable, providing high credibility for a product targeting the research community. Slide 19 lists three mentors/consultants with backgrounds in cancer genetics, healthcare coaching, and growth marketing, followed by a final contact slide.

What Works in the Academig Deck

Founder-Market Fit: The team’s academic credentials (two PhDs) and the personal story of the search for a PhD position make them the perfect people to build this product. · The Excel Slide: Showing the 'before' state (the manual spreadsheet) is the most effective way to demonstrate the necessity of the tool. · Clear Positioning: The comparison to Crunchbase and the distinction between 'individual-focused' and 'lab-focused' platforms clearly defines their niche. · Two-Sided Revenue: Identifying how to monetize both the researchers and the institutions shows a sophisticated understanding of marketplace dynamics.

What is Missing or Needs Improvement

The Financial Ask: Investors need to know how much money is being raised and what milestones that money will buy. 'Looking for valuations' is not a standard fundraising ask. · Product Depth: We see the 'what' and the 'why,' but very little of the 'how.' A few screenshots of the actual platform would help investors visualize the user experience. · Data Acquisition Strategy: The deck mentions 'Scraping' and 'Human factor,' but for a platform aiming to index 8 million labs, the scalability of the 'human factor' is a major question mark that isn't addressed. · Unit Economics: While pricing is listed, there is no information on Customer Acquisition Cost (CAC) or Lifetime Value (LTV), which are critical for a subscription/marketplace model.

Founder's Playbook: Lessons to Copy

Use the 'Before' Visual: If your product replaces a manual process, show the spreadsheet or the paper trail your users are currently suffering through. It is more persuasive than any chart. · The Power of Analogy: Comparing yourself to a well-known success (Crunchbase) helps investors categorize your business model instantly. · Credentialing: If you have a PhD or specialized experience in your field, lead with it. In highly technical or academic markets, the 'Dr.' title acts as a significant trust signal.

Frequently asked questions

What is the primary problem Academig is trying to solve?: Academig addresses the fragmented nature of academic data. Currently, researchers must navigate poorly updated lab websites, ResearchGate, Google Scholar, and various job portals to find opportunities. As shown on Slide 5, this often results in researchers manually creating complex Excel files to track lab information, leading to inefficiency and the fear of missing out on high-quality opportunities.
How does Academig plan to make money?: The deck outlines a two-sided revenue model on Slide 14. For the 'demand' side, students and researchers pay a $7 monthly subscription. For the 'supply' side, research labs pay between $99 and $550 for job postings and $200 for 'lab building services.' Slide 15 also suggests future revenue from departmental plans and data licensing through an API.
What is the current traction of the platform?: Slide 16 displays a growth chart starting in May 2019. By April 2020, the platform reached 4,000 users and 400 research labs. The deck projects a significant 'hockey stick' growth curve aiming for 50,000 users and 10,000 research labs by December 2020, though it does not specify the current month of the pitch.
Who are the competitors mentioned in the deck?: The deck identifies ResearchGate, Academia.edu, and Google Scholar as existing platforms in the space on Slide 12. However, it differentiates Academig by stating these competitors focus on individual researchers, whereas Academig is the first platform dedicated solely to finding and listing research labs.
What is missing from this pitch deck?: The most notable omission is a specific financial 'Ask.' Slide 17 mentions looking for 'Business development and valuations' and 'Connections,' but does not state a fundraising target (e.g., $500k for 18 months of runway). Additionally, there is no detailed breakdown of the 'Human factor' in their data scraping process or a competitive matrix beyond a high-level focus comparison.

Cover slide of the Academig pitch deck — Academig pitch deck, slide 1

Academig pitch deck: the facts

Company: Academig
Slides: 19
Sector: EdTech

Academig pitch deck PDF

The full Academig 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 Academig pitch deck was used for

Academig is presented as an EdTech marketplace listing academic research labs to help researchers discover positions and opportunities more efficiently, based on a founder’s own difficulty finding a PhD position in Germany after a neuroscience master’s at Tel Aviv University. The available pitch deck on Slideshare appears to be from around 2020, when the company claimed to list over 4,000 labs and have 4,000 users, with a goal to reach 10,000 labs and 50,000 users by the end of 2020. The deck describes a two-sided platform serving both researchers and institutional labs, monetizing primarily through job posting fees, with potential future revenue from data licensing and analytics. No externally verified information about funding rounds or investors tied to this specific deck could be found beyond the deck itself.

What the Academig deck got right

Strong, personal founder problem narrative. The deck uses a detailed founder narrative about struggling to find a PhD position in Germany after a neuroscience master’s at Tel Aviv University to concretely illustrate the friction in academic lab discovery, making the problem highly relatable and credible.
Clear two-sided marketplace positioning. Academig is framed clearly as a two-sided marketplace connecting individual researchers with institutional labs, with a focus on structured lab profiles and search tools that address a well-defined pain point in academic career navigation.
Concrete technical approach to structuring academic lab data. Slides describe a specific technical approach—scraping, data-mining, and a human factor—to consolidate scattered academic data into structured information about research labs, giving the product concept a tangible implementation path.
Early traction claims and explicit growth targets. The deck provides headline traction figures (over 4,000 labs and 4,000 users at the time of the deck) and sets explicit growth goals to reach 10,000 labs and 50,000 users by the end of 2020, signaling ambition and a focus on measurable scale.
Multiple envisioned revenue streams tied to the data asset. Revenue generation via lab job posting fees is articulated, alongside possible future revenue from data licensing and analytics, which indicates that the team is thinking beyond a single monetization channel and about the value of the underlying data asset.

What could have been stronger

Limited evidence of monetization and financial detail in the deck. The available material from the deck does not show clear, slide-backed evidence of paying customers, recurring revenue, or detailed financial projections; monetization is described conceptually (job posting fees, data licensing) without concrete numbers, pricing, or adoption metrics, which weakens the ability to assess business viability at the time of the pitch.
Traction metrics are high-level and lack depth. Although the deck states user and lab counts and sets ambitious targets (4,000 labs and users, aiming for 10,000 labs and 50,000 users by end of 2020), it does not provide supporting data such as cohort retention, engagement metrics, or breakdown by geography or discipline, making it hard for an investor to gauge real traction and product-market fit.
Competitive positioning appears underdeveloped. The narrative places strong emphasis on the founder’s personal journey and the generic friction in finding academic labs but appears to provide limited detail on competitive landscape, differentiation versus existing academic job boards and databases, or barriers to entry beyond access to scraped data. This could leave investors with unanswered questions about why Academig would win this market.
Data sourcing and institutional relationships are not fully specified. The technical approach mentions scraping, data-mining, and human factor for academic data on structured research labs but does not, in the accessible slides, detail compliance, data quality controls, or partnerships with universities and institutions, which may be important for a platform that relies heavily on academic data accuracy and institutional trust.

How an investor would read this deck

What draws attention

Evidence of strong search friction in academic lab discovery experienced directly by the founder, making the problem personally credible and emotionally resonant in the pitch.
Clear two-sided marketplace framing between researchers and academic labs, with a focus on structured lab profiles and opportunities that could, if executed, create network effects.
Early traction claims of thousands of labs and users and a roadmap to expand to tens of thousands of labs and tens of thousands of users by a specific date.
Stated monetization via job posting fees plus future potential for data licensing and analytics, which hints at multiple revenue streams from the same data asset.

Risks that stand out

If academic labs and universities choose to rely on existing institutional portals or competing platforms instead of Academig, the marketplace could struggle to reach critical mass on the lab side, limiting job inventory and platform attractiveness.
Heavy reliance on scraping and data-mining publicly available academic information might face issues with data quality, maintenance, or institutional pushback, potentially affecting platform reliability and scalability.
The business model centered on job posting fees may encounter budget constraints or limited willingness to pay among academic labs, which often operate under tight funding, making revenue growth slower than marketplace user growth.
Achieving ambitious growth targets in labs and users by a specific date, as stated in the deck, poses execution risk; missing these targets without strong alternative metrics or pivots would undermine the investment thesis.

Questions this deck invites

Can the team provide independently verified data on current labs listed, active users, and engagement metrics (e.g., applications per posting, conversion to hires)?
What are the current paying customer numbers, pricing model for job postings, and realized revenue to date, compared with what was projected in the deck?
How does Academig differentiate from existing academic job boards, university career portals, and research directories, and what is the defensible moat (data, relationships, technology)?
What is the status of institutional partnerships with universities or research organizations, and how are data rights, accuracy, and compliance managed for scraped or aggregated lab information?
Did Academig achieve the 2020 goals of 10,000 labs and 50,000 users presented in the deck, and if not, what changed in strategy or focus since then?

What founders can take from the Academig deck

A highly narrative-driven deck that grounds the problem in a founder’s personal struggle to navigate academic career paths can make an abstract marketplace concept tangible and compelling to readers.
Clearly articulating a two-sided marketplace (researchers and labs) with a specific data asset (structured lab information) helps investors understand platform dynamics and potential network effects, even before deep metrics are shown.
Including concrete, time-bound growth goals for labs and users (e.g., targets by the end of 2020) gives a sense of ambition and direction, but these numbers must later be backed by externally verifiable outcomes for credibility.
Describing multiple envisioned revenue streams (job posting fees, data licensing, analytics) from the same underlying data can signal monetization creativity, though it should be supported by detailed go-to-market and pricing plans.
Highlighting the technical approach (scraping, data-mining, human curation) and the existence of scattered academic data emphasizes why a structured platform is needed, but investors will expect additional detail on data governance and institutional relationships beyond what appears in the deck.
Reliance on internal traction numbers and projections within a deck, without corroborating external sources, limits what third-party researchers can verify later, underscoring the importance of transparent public updates for fundraising narratives.

Academig pitch deck: common questions

What is Academig and what problem does it aim to solve?

Academig is described in its pitch deck as a platform dedicated to listing academic research labs so that researchers can more easily find positions and opportunities in those labs. It functions as a two-sided marketplace connecting individual researchers with institutional research labs, aiming to make academic lab discovery and job search more efficient.

What founder story does the Academig pitch deck use to motivate the product?

According to the pitch deck, Academig was created after the founder, who had completed a master’s degree in neuroscience at Tel Aviv University, struggled to find a PhD position in Germany using traditional methods such as emailing professors and searching scattered websites. This personal experience with friction in academic lab discovery motivated the creation of a centralized platform listing structured research labs and their opportunities.

How does Academig say it will make money in the pitch deck?

The deck states that Academig generates revenue through fees that labs pay to post job opportunities on the platform. It also mentions plans to explore additional revenue streams such as data licensing and analytics built on top of the aggregated academic lab information and activity on the platform.

What traction and growth goals does Academig present in its pitch deck?

In the Slideshare pitch deck, Academig claims that at that time it already listed over 4,000 labs and had around 4,000 users. The team’s stated goal was to expand these numbers to 10,000 labs and 50,000 users by the end of 2020. These figures are slide-era metrics and goals, not externally verified outcomes.

How does Academig’s platform work, according to the deck?

The pitch deck shows Academig as an online platform where labs can create listings and post jobs, and researchers can search by academic field, location, and other filters to find suitable labs and opportunities. The technical approach emphasizes data scraping, data-mining, and organizing scattered academic data about structured research labs into a searchable, structured database, supplemented by a human factor for curation.

Sources

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

Academig pitch deck slides

Academig pitch deck slide 1 of 19 — Academig pitch deck — slide 1 of 19

Academig pitch deck slide 2 of 19 — Academig pitch deck — slide 2 of 19

Academig pitch deck slide 3 of 19 — Academig pitch deck — slide 3 of 19

Academig pitch deck slide 4 of 19 — Academig pitch deck — slide 4 of 19

Academig pitch deck slide 5 of 19 — Academig pitch deck — slide 5 of 19

Academig pitch deck slide 6 of 19 — Academig pitch deck — slide 6 of 19

What each slide of the Academig pitch deck says

Slide 2

After graduating with a Master degree in neuroscience from Tel Aviv University, Israel was determined to find a PhD position in Germany

Slide 3

Neuroscience research labs in Germany Y Google Search I'm Feeling Lucky EmB, Ng Open Academic Research Positions Partners Labs

Slide 4

Multiple platforms, websites, and emails ® ResearchGate ¢ ( Google Scholar 54 Emails ¢ -

Slide 6

| got upset and tired wf - Be. « But (even) more importantly...

Slide 10

Our goal is to help researchers find all the academic data they need. Scraping Data-mining Human factor —> l Information about Structured research labs is out there academic data

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

Related fundraising guides (24)

Browse companies alphabetically (1)

Pitch decks from companies starting with A

More pitch deck teardowns (16)

Recently published pitch deck teardowns (12)

Fundraising library · Pitch deck examples · Investor directory · Founder database