Shiva Pitch Deck: All 10 Slides + Teardown

Shiva’s 10-slide deck presents a specialized investment vehicle designed for the AI era, where small teams of 1-3 people can build global products. The fund, led by former Méliuz COO Lucas Marques, aims to back 100 programmer-founders using a milestone-based stipend model rather than traditional large upfront rounds. The deck leans heavily on the thesis that AI has collapsed the cost of software development, allowing for a 'fast death of ideas' and a portfolio of 'antifragile' micro-startups. By targeting three distinct founder personas—Underdogs, Blacksmiths, and Camels—Shiva positions itsel…

Key takeaways

• The fund targets 100 programmer-founders with a focus on 'solo builders' to reduce the cost and complexity of software development (Slide 3).
• Shiva takes up to 15% equity using NVCA-standard terms and retains a secondary right to sell up to 1/3 of its stake in each priced round (Slide 3).
• The 'Fast Death of Ideas' strategy limits runway to 12 months with weekly reviews to cut non-performing projects quickly (Slide 4).
• The business model differentiates from traditional VC by targeting $20M-$50M exits or profitable dividends as 'wins' (Slide 5).
• Portfolio company FoxApply is cited as a success indicator, reaching 20+ countries and 102 international customers in 20 days (Slide 6).
• Founder personas are segmented into 'Underdogs' (45), 'Blacksmiths' (45), and 'Camels' (10), each receiving different stipend levels (Slide 7).
• The $10M use of proceeds is broken down by persona, with $5.5M allocated to 'Blacksmiths' and $1.8M to 'Underdogs' (Slide 8).
• Founder Lucas Marques highlights his experience taking Méliuz from zero to IPO and his work placing 150 low-income students per year in tech roles (Slide 9).

Frequently asked questions

How does Shiva's investment model differ from a traditional VC?

As detailed on Slide 5, Shiva focuses on milestone-based stipends rather than large upfront rounds. While traditional VCs seek 'unicorn' exits of hundreds of millions, Shiva views exits between $20M and $50M, or even profitable dividends, as successful outcomes. They also employ a 'fast cut' strategy, reviewing traction weekly and ending support for projects that don't show immediate promise.

What kind of equity does Shiva take in its startups?

According to Slide 3, Shiva receives up to 15% equity in the companies it backs. The terms are described as NVCA-standard and founder-friendly. Additionally, Shiva secures a 'Secondary right,' allowing the fund to sell up to one-third of its stake during each subsequent priced round, providing earlier liquidity than traditional venture models.

Who are the 'Blacksmiths' and 'Underdogs' mentioned in the deck?

These are two of the three founder personas Shiva targets (Slide 7). 'Underdogs' are young programmers with high grit from diverse socioeconomic backgrounds who receive a 'lower stipend.' 'Blacksmiths' are highly experienced programmers from top startups who have never founded a company due to fear of losing income; they receive a 'mid stipend.' Together, they represent 90% of the planned cohort.

What is the 'Fast Death of Ideas' mentioned on Slide 4?

This is a core operational pillar of the Shiva Program. It limits the runway for any single idea to a maximum of 12 months. Through weekly reviews, founders who lack commitment or traction are cut early. This approach is designed to preserve capital, shifting resources only to the 'winners' who demonstrate the ability to create value quickly.

What is the primary geographic focus of the fund?

While the publisher reports the company is based in Brazil (S. America), Slide 6 emphasizes a 'Global From Day One' philosophy. The deck argues that Brazil represents only 2% of global GDP, so startups must target the 50x larger global market immediately. This is supported by the mention of portfolio company FoxApply, which launched in 20+ countries.

Shiva pitch deck: the facts

Company

Shiva

Year

2024

Stage

Pre-seed

Slides

10

Sector

AI

Outcome

$10M raised

Shiva pitch deck PDF

The full Shiva 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 Shiva pitch deck was used for

This deck appears to be Shiva’s 2024 pre‑seed fundraising pitch for a US$10 million fund dedicated to backing Brazilian and global AI “micro‑startups” of one to three founders.[2][3][10][13] It positions Shiva as a pre‑seed investment vehicle that replaces traditional lump‑sum checks with milestone‑tied monthly stipends, capped at roughly US$300,000 per startup for up to a year, in exchange for up to 15% equity.[2][3][9][10][13] The fund is led by former Méliuz COO Lucas Marques, headquartered in Brazil, and marketed as the largest reported pre‑seed raise in Latin America at the time, with Monashees leading and Endeavor Catalyst participating.[3][7][10] The deck likely underpins press coverage describing Shiva’s ambition to support around 100 AI and niche software startups, starting from an initial test cohort of about 30 companies already in a trial phase when the fund was announced.[2][3][9][11][13]

Business model: Investment vehicle / pre-seed fund that finances very small AI and niche software startup teams (typically 1–3 founders) via monthly stipends in exchange for single‑digit to low‑teens equity stakes.[2][3][7][10][13]

Round

Pre-seed fundraise for a new pre‑seed investment vehicle

Year

2024

Raised

US$10 million

Lead investor

Monashees

Investors

Monashees, Endeavor Catalyst

Founders

Lucas Marques

Headquarters

São Paulo, Brazil[7][10]

Industry: Venture capital / startup investment focusing on AI and niche software at pre‑seed stage.[2][3][7][10][13]

Total funding: US$10 million pre-seed fund capital raised to invest in portfolio startups.[2][3][7][10][13]

Use of funds as presented: To finance up to ~100 AI and niche software startups of one to three founders through monthly stipends (including living stipends and coverage of AI, cloud, and legal costs) and to build out Shiva’s community and support infrastructure.[2][3][7][9][10][13]

What happened after the Shiva deck

Following its 2024 pre‑seed fundraising effort, Shiva has launched operations as a Brazilian pre‑seed fund with US$10 million in capital, an initial cohort of about 30 startups in a testing phase, and an ambition to fund roughly 100 AI‑driven, micro‑team ventures using its monthly‑stipend investment model.[2][3][7][9][11][13]

• Raised a US$10 million pre‑seed fund led by Monashees with participation from Endeavor Catalyst, described as the larges
• Launched an initial test cohort of roughly 30 entrepreneurs or startups undergoing a three‑month trial phase with commun
• Set a target to ultimately support around 100 AI and niche software startups through its stipend‑based program.[2][3][9]

What the Shiva deck got right

• Highly differentiated AI micro‑team investment thesis. A sharply defined thesis focused on backing one‑ to three‑founder AI and niche software startups at pre‑seed via monthly stipends differentiates Shiva from traditional VC funds and accelerators and aligns with the “micro‑startup” and automation trends emphasized in coverage of the model.[2][3][9][11][12][13]
• Clear and founder‑friendly deal mechanics. The fund structure—US$10 million with capped per‑company funding (~US$300,000) and equity (≤15%)—is clearly communicated and presents as disciplined, founder‑friendly economics that can support a portfolio of around 100 companies.[2][3][9][10][13]
• Strong signaling from reputable backers and ecosystem positioning. Backing by Monashees and Endeavor Catalyst, along with public endorsements describing Shiva as analogous to a community‑driven accelerator for solopreneurs, lends strong external validation to a novel investment model.[3][7][10][11][14]
• Evidence of early execution through a pilot cohort. An initial test cohort of about 30 startups and a structured multi‑month testing phase with community and mentoring demonstrate that Shiva is already operational, not just a concept, which helps investors see early pipeline and learning loops.[2][3][9][11][13]
• Experienced founder with public‑company scale background. Founder Lucas Marques brings experience as former COO of Méliuz (a company that later went public), which supports the narrative that Shiva is led by an operator familiar with scaling technology businesses.[3][7][11][14]
• Compelling inclusion and access narrative for founders. The narrative explicitly connects the model to expanding access for diverse or disadvantaged founders, providing a mission‑driven angle that can appeal to LPs interested in inclusion and broader talent activation for AI entrepreneurship.[2][3][6][11][12]

What could have been stronger

• Limited transparency on fund‑level return math and portfolio construction. Shiva’s model expects many small bets with relatively modest exit targets (e.g., US$20–50 million range according to coverage of its thesis), but the deck’s public narrative does not clearly articulate how these exits aggregate into venture‑scale returns or how the fund’s portfolio construction and ownership targets support a compelling fund‑level IRR.[9][11][12][13]
• Sparse validation of the stipend‑first model versus traditional accelerators. While press and interviews mention an initial cohort of roughly 30 startups and a target of about 100 ventures, there is little externally visible detail on the selection funnel, performance metrics of the test cohort, or early evidence that the stipend model yields better company quality or outcomes than traditional accelerators.[2][3][9][11][13]
• Execution risks of prolonged micro‑team structure are not fully addressed. The model depends on founders keeping teams at one to three people for an extended period while heavily relying on AI tools, but public materials do not address potential downsides such as burnout, skill gaps, or difficulty scaling past the micro‑team phase once companies reach product‑market fit.[2][3][9][11][13]
• Limited detail on how diversity and access goals are operationalized. Although coverage highlights a focus on diverse and potentially disadvantaged founders who may lack access to capital, there is limited detail on how Shiva mitigates selection bias, supports non‑technical founders, or ensures inclusion across regions and demographics in Brazil and beyond.[2][3][6][11][12]

How an investor would read this deck

What draws attention

• Fund size and positioning as potentially the largest reported pre‑seed raise in Latin America at the time (US$10 million) and focus on AI‑native, ultra‑lean software startups.[3][7][10]
• Unique deal mechanics that use monthly stipends up to a US$300,000 cap per company for up to a year, with equity capped at roughly 15%, explicitly framed as an alternative to traditional pre‑seed rounds.[2][3][9][10][13]
• Clear thematic focus on one‑ to three‑founder teams using AI to build niche B2B software, with an ambition to back around 100 ventures and a first cohort of about 30 startups already in testing.[2][3][9][11][13]
• Founder pedigree of Lucas Marques as former COO of public company Méliuz and the backing of established investors Monashees and Endeavor Catalyst.[3][7][10][14]

Risks that stand out

• Model risk that a high‑volume portfolio of very lean AI micro‑startups may not generate enough sizable exits to return a US$10 million fund plus venture‑scale upside, especially if exit markets for US$20–50 million outcomes are limited.[9][11][13]
• Execution risk that solo or very small founding teams may struggle with company‑building beyond initial product, including sales, compliance, hiring, and internationalization, despite access to AI tools and Shiva’s mentorship.[2][3][9][11][13]
• Concentration of thematic risk around AI and niche software in Brazil and similar markets, which could be adversely affected by regulatory shifts, infrastructure constraints, or slower‑than‑expected enterprise AI adoption.[2][3][7][9][11][13]
• Operational risk that managing stipends, milestones, and support for up to ~100 ventures could strain Shiva’s internal resources and dilute the quality of mentorship and community support per startup if not carefully systematized.[2][3][7][9][11][13]

Questions this deck invites

• What is the precise portfolio construction plan (number of companies, average and maximum check sizes, ownership targets, and follow‑on strategy) needed to produce top‑quartile fund returns given the modest per‑company exit targets implied
• What quantitative signals from the initial ~30‑startup test cohort (e.g., survival rate, revenue, product launches) support the assertion that the stipend‑based, micro‑team model leads to better outcomes than traditional accelerators or pre
• How does Shiva manage adverse selection and founder quality when offering grant‑like stipends, and what structured mechanisms exist to cut underperforming teams quickly while preserving relationships with promising founders for future ventu
• What concrete frameworks, playbooks, or platform services does Shiva provide to help solo and very small founding teams cover gaps in sales, distribution, and enterprise go‑to‑market, particularly for B2B software targeting global markets?[
• How does Shiva plan to manage subsequent fundraising for its portfolio companies, given that early capital comes as stipends with relatively low dilution, and how do downstream VCs perceive and price this structure in later rounds?[2][3][9]

What founders can take from the Shiva deck

• A tightly defined thesis—funding one‑ to three‑founder AI teams via stipends at pre‑seed—can differentiate a fund and generate press interest when clearly articulated and consistently echoed across communications.[2][3][7][9][11][13]
• Structuring investments as monthly stipends with caps on both capital per company and equity can be framed as founder‑friendly while still giving investors disciplined downside protection and portfolio‑level scalability.[2][3][9][10][13]
• Anchoring a fund narrative in macro trends—such as AI enabling micro‑teams to build global products with low capital—helps investors connect the strategy to broader market shifts rather than viewing it as a niche experiment.[2][3][9][11][12][13]
• Highlighting an initial test cohort (around 30 startups) and a clear target portfolio size (around 100 ventures) gives concreteness to an otherwise abstract program thesis and allows investors to reason about funnel metrics and scalability.[2][3][9][11][13]
• Pairing a differentiated model with credible backers (Monashees, Endeavor Catalyst) and a founder with prior scale‑up experience (ex‑COO of Méliuz) can meaningfully de‑risk a novel fund structure in the eyes of LPs.[3][7][10][14]

Shiva pitch deck: common questions

What is Shiva?

Shiva is a Brazilian pre‑seed investment vehicle that runs a US$10 million fund to finance very small AI and niche software startup teams, usually one to three founders, using monthly stipends rather than large upfront checks.[2][3][7][10][13]

How much funding has Shiva raised and from whom?

Public reporting indicates that Shiva raised a US$10 million pre‑seed fund, described as the largest pre‑seed round reported in Latin America at the time, led by Monashees with participation from Endeavor Catalyst.[3][7][10] The capital is dedicated to funding AI‑driven and niche software startups via a stipend model.[2][3][10][13]

How does Shiva’s investment model work for AI startups?

Shiva’s model is to invest at the pre‑seed stage in AI and niche software startups of one to three people—called “Stars”—by providing monthly stipends for up to a year, with total capital per company capped at about US$300,000 and equity stakes capped at around 15%.[2][3][9][10][13] It expects founders to keep teams very small during this period while using AI tools to build and validate products quickly.[3][9][11][13]

What types of startups does Shiva back?

The fund focuses on pre‑seed‑stage AI and niche software companies, especially very lean teams of one to three founders that aim to build global products using AI to minimize operational overhead.[2][3][9][11][12][13] Shiva explicitly targets founders from diverse or non‑traditional backgrounds and seeks businesses that can reach revenue quickly with modest capital.[2][3][6][11][12]

What stage is Shiva’s program at and how many startups will it fund?

According to press coverage, Shiva began with a roughly 30‑startup test cohort, providing a three‑month trial phase with community, mentoring, and access to AI tools, and ultimately aims to build a portfolio of about 100 ventures funded through its stipend model.[2][3][9][11][13] The deck in question is associated with Shiva’s initial US$10 million pre‑seed fundraise in 2024 to launch and scale this program.[2][3][7][10]

Sources

Funding and outcome facts on this page were researched on 2026-08-30 from the pages below.

• Lucas Marques' Shiva Fund Invests $10M in Brazilian AI ...
• Shiva Fund Bets $10M on Small AI Teams for Big Impact
• O pre-seed da Shiva, que quer bancar startups criadas só com AI
• Brazil - Business Insider
• Shiva raised $10M | Venture Capital - MapCo
• Shiva Fund Invests $10M in Early-Stage AI Startups
• Shiva levanta US$ 10 mi com Monashees - Baguete
• Brazilian AI startup fund Shiva raised $10 million, and it's betting on ...

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