What is the core problem Retora is solving?

Retora addresses the scarcity and quality of human feedback required for Reinforcement Learning from Human Feedback (RLHF). As AI models grow, the demand for human-labeled data has exceeded the capacity of traditional workforces. Retora solves this by tapping into the massive, underutilized time of mobile gamers, turning leisure activities into productive data labeling sessions.

How does Retora's revenue model differ from traditional mobile advertising?

Traditional mobile advertising relies on impressions or clicks, often yielding low eCPMs for developers. Retora claims that AI companies are willing to pay significantly more for high-quality training data than advertisers pay for a standard ad. Retora facilitates this transaction, providing developers with a higher payout while taking a cut of the transaction fee.

What evidence of product-market fit does the deck provide?

The deck cites two main points of traction: a 2022 test project called 'TBDNE' which proved audience engagement with the labeling process, and the 2015 release of 'Merchant,' which garnered 2 million organic downloads. These figures suggest the team can build games people want to play and that those players are willing to engage with the data-labeling mechanics.

Who are the primary competitors and what is Retora's edge?

Competitors fall into two camps: offerwall services like Tapjoy and Pollfish, and AI labeling firms like Scale, Sama, and Surge. Retora argues that offerwalls have poor user reputations and lack AI solutions, while labeling firms rely on underpaid workers and lack the scale of a crowdsourced gaming audience.

What are the specific technical goals for the Seed Round?

The $2M Seed Round is focused on infrastructure. Specifically, the funds are allocated to developing a software development kit (SDK) that other developers can integrate, building out server capacity to handle data processing, creating a dashboard for AI clients to manage tasks, and funding partner acquisition to grow the network.

Retora Games Pitch Deck (2023): 14-Slide Seed Deck

See all 14 slides of the Retora Games pitch deck — a 2023 Seed deck — with a slide-by-slide teardown of what the deck does well and where it falls short.

Retora presents a novel intersection between the $150 billion mobile ad market and the $100 billion AI training sector. The company proposes an 'Offerwall meets Data Training' model, where mobile gamers perform RLHF tasks—such as identifying AI-generated images—in exchange for in-game currency. This approach seeks to solve the labor shortage in high-quality AI training data while providing developers with revenue that exceeds traditional ad payouts. With a team led by a Forbes 30 Under 30 founder and a Zynga veteran, Retora leverages its existing 2 million organic downloads from previous titl…

Key takeaways

Retora identifies a critical bottleneck: AI companies need human feedback, but demand has outpaced the available workforce (Slide 2).
The business model functions as a three-sided marketplace where AI companies pay for data, developers get a higher cut than traditional ads, and Retora takes a commission (Slide 6).
Market tailwinds are strong, with AI projected to reach $1.8 trillion by 2030 and mobile ads hitting $621 billion by 2029 (Slide 7).
The company has historical traction, citing over 2 million organic downloads for their 2015 release 'Merchant' (Slide 9).
Retora differentiates from competitors like Scale and Sama by using a crowdsourced gaming audience rather than underpaid direct labor (Slide 11).
The team includes David Bettner, founder of Newtoy Inc and a former Studio Director at Zynga (Slide 12).
The $2M Seed Round is specifically earmarked for SDK development, server infrastructure, a dashboard, and partner acquisition (Slide 13).
The deck uses a specific 'Merchant Guilds' sequel launch in Q3 2023 as the primary test pilot for the delivery mechanism (Slide 10).

Retora Pitch Deck Teardown

Slide 1: Title and Value Proposition

The deck opens with a clear, high-contrast title slide. The company name, Retora Games , is paired with a succinct sub-headline: "Providing ML training data through mobile games." This immediately positions the company at the intersection of gaming and artificial intelligence, avoiding the common pitfall of being too vague about the industry sector.

Slide 2: The Problem

Slide 2 identifies a supply-chain bottleneck in the AI industry. It states that "AI companies need human feedback to improve their products... but the demand for the data has grown beyond the available workforce." This sets the stage for a scalability solution, implying that current manual labeling methods are insufficient for the current pace of AI development.

Slide 3: The Solution

The solution is presented as a hybrid model: "Offerwall Meets Data Training." This slide is minimalist, serving as a transition to explain how the mechanics of mobile game monetization (offerwalls) can be repurposed for a more lucrative end-market (ML training).

Slides 4-6: The Business Model and Mechanics

These three slides walk through the value chain. Slide 4 explains that Retora provides AI/ML companies access to a large user base via mobile games. Slide 5 details the user incentive: "We incentivise these users to provide feedback by offering in-game rewards." This is a critical distinction from traditional labeling; the 'pay' is digital currency, which has a lower marginal cost for the developer but high perceived value for the player. Slide 6 illustrates the financial flow, noting that AI companies provide a "higher payout than a traditional ad" and that Retora takes a cut of the transaction between the AI company and the game developer.

Slide 7: Market Opportunity

Retora highlights two massive, growing markets. Artificial Intelligence is cited as a "$100 billion Current Market" with an estimated growth to "1.8 trillion by 2030" at a CAGR of 32.9% . The Mobile Ads market is listed at "$150 billion" currently, growing to "621 billion by 2029" at a CAGR of 23.2% . By positioning themselves in the middle, Retora suggests they can capture value from both sectors.

Slides 8-10: Traction and GTM Strategy

Slide 8 mentions a 2022 test called "TBDNE" (This Bird Does Not Exist) which demonstrated that audiences enjoy the labeling process. Slide 9 provides historical credibility, noting that in 2015 the team released "Merchant" and achieved "over 2 million organic downloads." Slide 10 connects these dots to the future, announcing "Merchant Guilds (Q3 2023)" as the initial test pilot where they have full control over the delivery of the RLHF tasks. The slide includes a mockup showing a player choosing between four AI-generated wizard character designs to receive 5 gems.

Slide 11: Competitive Landscape

The deck categorizes competitors into two groups. Offerwall polling services (Tapjoy, Pollfish) are dismissed for having "poor reputation among users" and no AI solutions. AI RLHF companies (Scale, Sama, Surge) are described as having "underpaid workers and no crowdsource solutions." Retora’s implied advantage is a happier, more scalable workforce (gamers) and a more specialized technical offering than generic pollsters.

Slide 12: The Team

The team slide features Tyler Coleman , the founder and a Forbes 30 Under 30 honoree, and David Bettner , an early investor and advisor who was a Studio Director at Zynga and founder of Newtoy Inc (the creators of Words With Friends). The slide also lists four staff members—Denis Krasakov, Viacheslav Bogomazov, Garret Wied, and Clay Burton—represented by pixel-art avatars consistent with their game 'Merchant.'

Slide 13: The Ask

Retora is seeking a "$2M Seed Round." The funds are allocated to four specific pillars: SDK, Server, Partner Acquisition, and Dashboard. This indicates a plan to move from a single-game pilot to a platform model where other developers can use Retora’s infrastructure to monetize their own games via ML tasks.

Slide 14: Contact

The deck concludes with a simple contact slide featuring Tyler Coleman's email address.

What Works in the Retora Deck

Clear Market Convergence: The deck does an excellent job of explaining why two disparate industries (Mobile Gaming and AI Training) belong together. The logic that a gamer’s time is cheaper to 'buy' with virtual currency than a professional labeler’s time is with cash is a compelling economic argument.

Proven Developer Pedigree: Citing 2 million organic downloads for a previous title ('Merchant') is significant. It proves the team isn't just theorizing about games; they have built and scaled a successful product in a crowded market. The inclusion of David Bettner adds substantial industry weight, given his history with Zynga and Newtoy.

Specific Use Case: Slide 10 provides a tangible example of what the product actually looks like. Showing a player choosing a character design for gems makes the abstract concept of 'RLHF' immediately understandable to a non-technical investor.

What is Missing from the Retora Deck

Unit Economics: While the deck claims that AI companies provide a "higher payout than a traditional ad," it lacks specific figures. Investors would want to see the delta between a standard rewarded video ad eCPM and the projected eCPM of an RLHF task. Without these numbers, the 'higher payout' remains a hypothesis.

Data Quality Assurance: A major concern in crowdsourced labeling is 'garbage in, garbage out.' The deck does not explain how Retora ensures that a gamer clicking buttons for gems is providing accurate data. There is no mention of consensus algorithms, trap questions, or verification layers to prevent low-quality inputs.

Regulatory and Ethical Considerations: As AI training data comes under increased scrutiny regarding copyright and labor practices, a slide addressing how Retora handles data privacy or the ethics of gamified labor would have been timely.

What a Founder Should Copy

The 'Offerwall' Analogy: If you are building a complex B2B product that relies on a B2C audience, find a familiar mechanic to compare it to. By calling it an 'Offerwall,' Retora uses a term every mobile investor understands to explain a new AI concept.

Visual Consistency: The deck uses the same color palette and pixel-art style as the company's games. This creates a cohesive brand identity and reinforces the idea that the founders are, first and foremost, game developers who understand their medium.

Phased GTM: The deck outlines a clear path from 'Test' (TBDNE) to 'Pilot' (Merchant Guilds) to 'Platform' (SDK development). This phased approach reduces the perceived risk by showing that the company is validating its tech on its own properties before asking others to adopt it.

Frequently asked questions

What is the core problem Retora is solving?: Retora addresses the scarcity and quality of human feedback required for Reinforcement Learning from Human Feedback (RLHF). As AI models grow, the demand for human-labeled data has exceeded the capacity of traditional workforces. Retora solves this by tapping into the massive, underutilized time of mobile gamers, turning leisure activities into productive data labeling sessions.
How does Retora's revenue model differ from traditional mobile advertising?: Traditional mobile advertising relies on impressions or clicks, often yielding low eCPMs for developers. Retora claims that AI companies are willing to pay significantly more for high-quality training data than advertisers pay for a standard ad. Retora facilitates this transaction, providing developers with a higher payout while taking a cut of the transaction fee.
What evidence of product-market fit does the deck provide?: The deck cites two main points of traction: a 2022 test project called 'TBDNE' which proved audience engagement with the labeling process, and the 2015 release of 'Merchant,' which garnered 2 million organic downloads. These figures suggest the team can build games people want to play and that those players are willing to engage with the data-labeling mechanics.
Who are the primary competitors and what is Retora's edge?: Competitors fall into two camps: offerwall services like Tapjoy and Pollfish, and AI labeling firms like Scale, Sama, and Surge. Retora argues that offerwalls have poor user reputations and lack AI solutions, while labeling firms rely on underpaid workers and lack the scale of a crowdsourced gaming audience.
What are the specific technical goals for the Seed Round?: The $2M Seed Round is focused on infrastructure. Specifically, the funds are allocated to developing a software development kit (SDK) that other developers can integrate, building out server capacity to handle data processing, creating a dashboard for AI clients to manage tasks, and funding partner acquisition to grow the network.

Cover slide of the Retora Games pitch deck — Seed 2023 — Retora Games pitch deck, slide 1 (2023)

Retora Games pitch deck: the facts

Company: Retora Games
Year: 2023 (based…
Stage: Seed
Slides: 14
Sector: AI / Mobile Gaming
Deck type: Seed Pitch Deck
Headquarters: Austin, Texas (implied by UT Austin affiliation)

Retora Games pitch deck PDF

The full Retora Games 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 Retora Games pitch deck was used for

This is a 14‑slide seed‑stage pitch deck from around 2023 for Retora Games, a mobile gaming company exploring the use of Reinforcement Learning from Human Feedback (RLHF) embedded in game mechanics to generate AI training data. The concept, as summarized in the existing article excerpt, is to replace traditional, low‑paid data‑labeling workforces with mobile game players whose in‑game actions and feedback provide structured training data for AI systems. The deck appears to target AI and mobile‑gaming investors interested in data‑labeling efficiency and game‑based user engagement, with a Q3 2023 pilot indicated. Very little externally verifiable information exists about this specific AI/RLHF data‑labeling initiative beyond what can be observed from the deck and the hosted slide link itself.

Industry: Mobile gaming

What the Retora Games deck got right

Differentiated RLHF‑driven data‑collection concept. The deck presents a differentiated concept that repurposes mobile game engagement into structured RLHF training data, clearly aligning with a fast‑growing AI infrastructure need.
Compelling narrative around efficiency and fairness. By framing the solution as replacing underpaid data‑labeling workforces with better‑compensated developers and engaged players, the deck taps into both an efficiency and fairness narrative likely to resonate with some investors.
Scalable and flexible data‑collection interface via gaming. Using mobile games as a front‑end could provide a scalable and flexible interface for collecting a wide variety of preference and ranking signals, potentially increasing data diversity compared with traditional labeling platforms.
Clear sector positioning at AI and mobile‑gaming intersection. The deck clearly positions the company at the intersection of AI training data and mobile gaming, which helps investors quickly understand the sector focus and potential synergies.

What could have been stronger

Unverified traction and adoption. There is no externally verifiable information tied to this deck about actual customers, pilots, or revenue, which means the traction and adoption claims in the deck cannot be independently confirmed.
Limited discussion of ethical and regulatory considerations. The deck description emphasizes replacing underpaid labeling workforces with game players, but does not (from the available text) clearly address regulatory, ethical, or fairness considerations in compensating players whose actions provide commercial AI training data.
Lack of externally validated business model details. Key business details such as pricing models for AI customers, revenue‑share structures with developers, and concrete unit economics for data collection versus traditional labeling are not available from external sources and appear only implicitly, making it harder for investors to validate the business model.

How an investor would read this deck

What draws attention

Novel approach of aligning AI training data collection with game mechanics, which could significantly reduce friction compared with traditional labeling workflows.
Clear tie‑in to RLHF, a rapidly growing area of AI model training, making the pitch relevant to current AI infrastructure trends.
Potential for dual‑sided value creation: better payouts or monetization options for game developers and higher‑quality data for AI companies.

Risks that stand out

Reliance on players for consistent and high‑quality feedback may introduce variability and bias that is difficult to control without extensive oversight and incentives.
The business model may depend on signing both AI customers and game developers simultaneously, creating a challenging two‑sided‑market problem.
Lack of public evidence of funding or large‑scale deployments raises questions about the ability to convert the concept into a sustainable, competitive business.

Questions this deck invites

Can the team provide independently verifiable evidence of pilots, paying customers, or letters of intent from AI companies for this RLHF‑based data‑collection approach?
How is player data labeled, structured, and quality‑controlled to ensure it meets the standards required for RLHF training at scale?
What concrete revenue model is used (e.g., per‑label pricing, subscription, revenue share) and how does its cost compare to traditional data‑labeling vendors for comparable quality?
What legal, ethical, and privacy frameworks are in place to ensure players are properly informed and compensated for their participation in data generation?
How defensible is the technology and data pipeline against large incumbents that might build similar game‑based data‑collection systems in‑house?

What founders can take from the Retora Games deck

A pitch positioning mobile games as a data‑labeling substrate for RLHF can stand out by connecting a familiar entertainment medium with a high‑value AI infrastructure problem.
Articulating how game mechanics map to specific RLHF signals (rankings, preferences, approvals) is critical for helping investors understand the technical value of the approach.
When a deck claims to replace traditional labeling workforces, it is important to substantiate this with examples, pilot results, or cost and quality comparisons to gain investor confidence.
Highlighting better payouts for developers and better data for AI companies can effectively frame a two‑sided value proposition, but investors will still seek clear evidence of traction on both sides.
Without external validation via press, investor disclosures, or customer references, even a creative and timely concept such as RLHF‑enabled gaming remains difficult to underwrite at seed stage.

Retora Games pitch deck: common questions

What problem is Retora trying to solve with its AI and mobile‑gaming approach?

The deck describes Retora as using mobile games to collect structured AI training data by embedding Reinforcement Learning from Human Feedback (RLHF) tasks into game mechanics, so that players effectively perform data‑labeling actions while playing. This is positioned as a way to replace traditional, underpaid labeling workforces with engaged players while improving the quality and volume of feedback signals for AI models.

How does Retora’s solution work according to the pitch deck?

The deck frames the product as mobile games designed so that common gameplay actions (choices, rankings, preferences, or evaluations) correspond to RLHF‑style feedback. AI companies would integrate with Retora’s system to receive labeled data or preference signals, while players experience a game that rewards them for participation rather than performing explicit labeling tasks.

What stage and timeframe was this pitch deck associated with?

Based on the deck metadata, this appears to be a seed‑stage pitch deck dated around 2023, with a Q3 2023 pilot mentioned. However, there are no publicly verifiable funding announcements, investor lists, or press releases tying a specific amount or named investors to this raise, so any detailed round information remains undisclosed externally.

Who is the target customer or user for Retora’s platform?

The deck targets AI companies that require large‑scale training data (particularly for RLHF), mobile game publishers or developers who could integrate Retora’s mechanics into their titles, and early‑stage investors focused on AI infrastructure, data‑labeling, or gaming. The slides emphasize better economics for developers, higher‑quality AI data for enterprises, and an engaging experience for players.

Did Retora successfully raise funding or reach a specific outcome from this deck?

There are no external records confirming a completed funding round, acquisition, or shutdown related to this specific RLHF mobile‑gaming data‑labeling concept. Without credible press coverage, filings, or investor disclosures, the outcome of this seed raise and the current operational status of the AI data‑labeling product cannot be verified.

Sources

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

Retora Games pitch deck slides

Retora Games pitch deck slide 1 of 14 — Retora Games pitch deck — slide 1 of 14

Retora Games pitch deck slide 2 of 14 — Retora Games pitch deck — slide 2 of 14

Retora Games pitch deck slide 3 of 14 — Retora Games pitch deck — slide 3 of 14

Retora Games pitch deck slide 4 of 14 — Retora Games pitch deck — slide 4 of 14

Retora Games pitch deck slide 5 of 14 — Retora Games pitch deck — slide 5 of 14

Retora Games pitch deck slide 6 of 14 — Retora Games pitch deck — slide 6 of 14

What each slide of the Retora Games pitch deck says

Slide 1

Zh RETOR4r Retora Games Providing ML training data through mobile games

Slide 2

ML Ce ies Need Data Traini ompantes Nee ata Training = Al companies need human feedback to improve their products... ...but the demand for the data has grown beyond the available workforce 2

Slide 3

Solution = That's where we come in. Offerwall Meets Data Training

Slide 4

From Al Training Data = We provide Al/ML companies access to a large user-base via mobile games and apps These users are extremely valuable for training data oooad Oooo _ Qooon oooo 4

Slide 5

To mobile app users = We incentivise these users to provide feedback by offering in-game rewards 000 owe — $99 0 F000 8

Slide 6

To Developers and Us = This data is valuable to the Al companies, who provide developers with a higher payout than a traditional ad... Our cut $ $ $ $ A A A A ..and we getacut \AA 5 ew Their cut $ $ $ $ 6

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