Proven — Immersive

About

Proven is a dynamic platform where companies post real-world AI challenges as live missions, attracting top AI-native talent to compete in solving them. By ranking participants based on actual performance, Proven provides businesses with verified, high-qu

Problems & Solutions

Problems

Difficulty in finding AI talent with verified skills and proven performance.
Lack of real-world problem-solving opportunities for AI professionals to showcase their abilities.
Companies facing challenges in obtaining high-quality AI solutions tailored to their specific needs.
Getting a hard AI problem solved usually means hiring a full-time builder or an agency, which is slow, expensive, and out of reach for many teams that just need the work done.
Resumes, credentials, and interviews poorly predict real performance in fast-moving AI roles, so companies waste weeks screening and still risk costly mishires.
Companies have no fast, low-commitment way to reach a broad pool of capable AI builders and compare them on their actual task before committing.

Solutions

Proven provides a platform where AI talent can be ranked based on their performance in solving real-world challenges, allowing companies to identify and hire individuals with verified skills.
By hosting live missions, Proven offers AI professionals a chance to work on actual industry problems, showcasing their abilities and gaining recognition in the AI community.
Proven curates a competitive environment where AI professionals provide tailored solutions to the specific challenges posted by companies, ensuring high-quality and relevant outcomes.
Proven lets a company crowdsource the problem as a paid challenge, receiving multiple working solutions for the price of a prize instead of a salary, so it gets solved fast and affordably by the crowd.
Proven replaces that guesswork with proof of work and an objective six-axis strength matrix, letting companies hire on a verifiable track record of what builders actually shipped.
Proven is a two-sided marketplace where one posted challenge reaches many ranked builders at once, and reverse-search surfaces a fit-explained shortlist on demand.

Pitch Deck

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Questions & Answers

What problem are you solving?

Two linked problems. First, hiring AI-native talent is broken: resumes, credentials, and interviews are weak predictors of who can actually ship in a field moving faster than any degree can certify, so companies spend weeks and real money screening and still hire on proxies for skill. Second, companies sit on hard AI problems they cannot get solved affordably, because a full-time hire or an agency is slow and expensive. Proven fixes both: a company posts a real challenge to the crowd, gets working solutions for the price of a prize, and can then hire the proven performer who built the best one.

What problem will be solved at the end of what you are doing?

When we're done, hiring an AI builder will look like checking a verifiable track record rather than guessing from a CV: a company posts a real challenge, sees who actually solved it best, and hires the proven performer. The outcome is evidence-based, meritocratic hiring where what you shipped, not who you know or how you interview, decides who gets the work.

Can you state the problem clearly in two sentences?

Companies hiring for AI-native roles can't reliably tell who is genuinely good, because credentials and interviews poorly predict real performance in a fast-moving field. Proven replaces that guesswork with proof of work, ranking builders by what they actually shipped on real challenges.

Have you experienced the problem yourself?

Yes. As technical founders who designed, built, and shipped this product ourselves, we repeatedly hit the problem from both sides, judging AI talent and being judged, and saw that interviews and resumes told us almost nothing about who could actually deliver, while a working solution to a real problem told us everything.

Can you define this problem narrowly?

Narrowly, we solve the signal problem for hiring AI builders: the difficulty of objectively ranking candidates for AI/ML, automation, backend, frontend, data, and product work when traditional signals are unreliable. We're not solving all of hiring, just the evidence gap for a specific, high-demand, fast-moving slice of technical roles.

Who can you help first?

We can help first the companies that urgently need AI-native builders but lack a reliable way to evaluate them, typically startups and small product teams without a deep technical bench to run rigorous screening. On the supply side, we help skilled AI builders who can deliver but struggle to prove it through conventional resumes.

What can we address immediately?

Immediately, a company can post a real AI problem as a challenge with a brief and a modest prize, crowdsource it to many builders at once, and receive working, ranked submissions, so a hard problem gets solved for the cost of a prize rather than a salary. They can also reverse-search the existing builder pool by required skill and get a fit-explained shortlist. Either way, a vague need becomes objective, comparable output within days.

How do we get the first indication this thing is working?

The first indication is a company posting a live challenge, receiving real submissions from our builder cohort, and selecting a winner they would actually consider hiring or paying again. We see early validation when companies return to post a second challenge or reverse-search the pool unprompted.

Who is your customer?

Our paying customer is the company that needs to hire or contract AI-native talent, especially startups, scale-ups, and product or innovation teams under pressure to ship AI features fast. Builders are the other side of the marketplace and join free; companies are the demand side that pays.

Who is the ideal first customer?

The ideal first customer is a fast-moving, technically literate company, often a startup or a product team inside a larger firm, that has a concrete AI problem to solve, struggles to assess AI talent through interviews, and is willing to put up a prize to see real work before committing. They feel the pain acutely and can decide quickly.

How will they know if your product has solved the problem?

They know it's solved when they hire or engage a builder whose ranked track record predicted real on-the-job performance, meaning the person actually delivered the way their proof-of-work suggested. The signal is a confident hire made faster and cheaper than their usual process, with less risk of a bad fit.

How often does your user have the problem?

Hiring and contracting for AI-native work is a recurring, often continuous need for our customers; growing product teams open new roles or commission new builds every quarter, and many need flexible, on-demand talent for specific AI tasks far more frequently than that. The problem isn't a one-time event but an ongoing constraint on shipping.

Who is getting the most value out of your product?

Companies get value on two fronts: they get hard AI problems actually solved cheaply, paying a prize to the crowd instead of a full-time salary or agency fee, and when they want to hire, they convert an unreliable interview gamble into an evidence-based decision. High-performing builders gain a public, ranked track record that lets their actual work, not their network, win them prizes and opportunities.

How intense is the problem?

The problem is intense and rising: AI talent is scarce and expensive, a bad hire can cost months and tens of thousands in salary and lost momentum, and the cost of misjudging who can really build is growing as AI becomes central to more products. For teams racing to ship AI features, getting the wrong builder is a serious, visible setback.

Are they willing to pay?

Yes, and the price point is attractive precisely because crowdsourcing a challenge costs a fraction of hiring full-time or engaging an agency: a company puts up a prize plus a platform fee, gets multiple working attempts, and mainly pays for the one that wins. Companies already spend heavily on hiring and on getting work done, so we capture an established budget while offering a cheaper, lower-risk path to both a solved problem and a confident hire.

How easy is it for your customer to find your product?

Builders find us through the pull of public challenges, prizes, and a track record worth building, which makes them natural advocates who attract more builders. Companies find us through targeted outreach, content around proof-of-work hiring, and the network effect of a growing leaderboard; as the talent pool deepens, the reverse-search becomes the reason to come to us.

Which customers should you run away from?

We run away from companies wanting cheap commodity labor or a body to fill a seat regardless of merit, since our edge is proof of quality, not lowest price. We also avoid those unwilling to define a real problem or fund a fair prize, and those wanting us to bypass our objective ranking and just rubber-stamp a predetermined pick.

Does your product actually solve the problem?

Yes, on both sides. By crowdsourcing a real challenge to many builders and ranking submissions objectively, the company gets working solutions to a hard problem for the price of a prize, and gets direct evidence of who can do the work, replacing the weak signal of resumes and interviews. The accumulating public track record, strength matrix, wins, and proof-of-work means each hiring decision rests on demonstrated outcomes, not claims.

Which customers should you go after first?

We go after fast-moving startups and product teams that already need AI builders, lack rigorous screening capacity, and can decide and pay quickly, the segment where the pain is sharpest and the sales cycle shortest. Winning these early customers also produces the most compelling proof-of-work stories to bring on the next wave.

How do you find people who are willing to use your "bad" first versions of your product?

We recruit early builders directly from AI, ML, and developer communities, hackathons, and online builder networks, where people are eager to compete, win prizes, and earn a visible track record. On the company side, we hand-pick a small set of friendly, problem-rich early adopters who value the chance to see real work and will tolerate rough edges in exchange for that.

Who are the most desperate customers and how do you talk to them first?

The most desperate customers are teams that have already mishired for an AI role or are stuck unable to evaluate candidates for a critical build. We reach them directly through founder networks, AI and startup communities, and targeted outreach, and lead with a concrete offer: post your real problem, see ranked working solutions, and only commit once you've seen proof.

Whose business is going to go out of business without using you?

Product companies whose competitive position depends on shipping AI features fast are most exposed; if they keep mishiring or can't find builders who can actually deliver, they fall behind rivals who execute. We're not claiming we single-handedly save them, but for teams where AI execution is existential, reliably sourcing proven builders is the difference between shipping and stalling.

Are you discounting or starting with a super low price?

We're deliberately pricing the early stage to maximize learning and proof, not margin: in this MVP phase the priority is getting real challenges run and real hires made so we can demonstrate the model works. We won't compete on being cheapest long term; our value is a better hiring outcome, but early adopters get favorable terms in exchange for being our reference cases.

What 5-10 metrics are you measuring to understand how your product functions?

We watch challenges posted, submissions per challenge, active builders, builder activation (signup to first submission), time-to-first-submission, time-to-winner-selected, winner-to-hire conversion, reverse-searches run and shortlists actioned, repeat-company rate, and marketplace liquidity (the share of challenges that attract enough quality submissions). Together these tell us whether both sides are engaged and matching.

When you build a new product or feature, what is the metric that will improve because of that feature/product?

Each feature targets one metric: the strength matrix and radar improve shortlist quality and reverse-search action rate; faster submission flows cut time-to-first-submission and lift submissions per challenge; richer proof-of-work pages raise winner-to-hire conversion. We tie every build to the specific number it's meant to move and check it afterward.

What number do you track to show how well your company is doing?

Our headline health metric is successful matches, the number of challenges that result in a company selecting a winner and moving toward a real hire or paid engagement, because it captures value delivered on both sides. Sustained, growing successful matches is the clearest evidence the marketplace is working.

What is your top level KPI (revenue, usage)?

Our top-level KPI is revenue from completed hiring outcomes, since that proves companies will pay for the value we create. In the near term, while we build liquidity, we track successful matches as the leading indicator that directly drives that revenue.

What are the underlying metrics that contribute to achieving your top level KPI?

Revenue is driven by the funnel beneath it: number of active companies, challenges posted per company, submissions and quality per challenge, winner-selection rate, and the conversion from selected winner to paid hire or placement. Underpinning all of it is builder supply and engagement, since a deep, active, high-quality talent pool is what makes every challenge and search produce a payable outcome.

How long is your product dev cycle?

As a small, hands-on technical founding team, we run a fast, continuous cycle, shipping meaningful improvements in days to a couple of weeks rather than long quarterly releases. We built and shipped the full MVP quickly, and we keep iterating in tight loops driven by what we learn from live challenges and real users.

Who is writing down notes at your product dev meeting?

We keep lightweight written records of every product session in a shared doc so decisions, open questions, and action items are captured and revisited; with three founders, whoever is driving the discussion logs it, and we keep it disciplined rather than relying on memory. The point is that nothing decided gets lost between our fast iteration cycles.

Which category does each of your brainstormed ideas fit?

We sort every idea into clear buckets, core marketplace mechanics (challenges, ranking, matching), trust and signal quality (the strength matrix, proof-of-work, anti-gaming), supply growth (builder acquisition and engagement), demand and monetization (company onboarding, pricing, conversion), and platform or infrastructure. Categorizing keeps us honest about whether an idea actually serves liquidity and our core insight.

How easy/medium/hard are they to do?

We tag each idea easy, medium, or hard by build effort and risk: easy items are small UI or flow improvements we ship same-week; medium items are new features touching matching or scoring logic; hard items involve deeper systems like robust anti-gaming, automated evaluation, or sophisticated fit-ranking algorithms. The tag sets expectations on cost and sequencing.

How can you restate the hard ideas?

We restate hard ideas by breaking them into the smallest version that delivers the core value and can be validated, then growing from there. For example, fully automated solution evaluation becomes, initially, a structured human-assisted ranking with clear criteria, which we later automate, so we learn fast without betting everything on the hardest piece up front.

What parts of hard ideas are useless or hard?

When we decompose a hard idea, we cut the parts that add cost without changing the customer outcome, gold-plating, premature generality, and edge cases that don't yet matter at our scale. What's genuinely hard and worth keeping is whatever protects signal quality and trust, the heart of the product; the rest we defer or drop.

Which hard idea will improve the KPI the most? Which medium? Which is easier?

Among hard ideas, robust trust and anti-gaming of the ranking moves the KPI most, because the integrity of the leaderboard is what makes companies pay; that's our priority hard bet. A medium-impact item is smarter fit-ranking for reverse-search, which lifts shortlist quality and conversion, while easier wins like streamlined company onboarding and richer proof-of-work pages compound quickly with low effort.

What is your exit strategy for the startup?

Our focus is building a category-defining, valuable company first; a strong exit follows from that, not the other way around. The most likely path is acquisition by a larger HR-tech, talent-marketplace, or developer-platform company for whom a verified, proof-of-work talent graph for AI-native skills is strategically valuable, with continued independent scaling as the alternative if the business reaches the size to justify it.

Are you considering an IPO, sale to another company, or a management buyout?

A trade sale to a strategic acquirer in HR tech, talent marketplaces, or developer and AI platforms is the most realistic primary path, given the value of our proprietary track-record data and two-sided network. An IPO is a long-horizon possibility only if we reach meaningful scale and durable revenue; a management buyout isn't part of our plan. We're building optionality rather than steering toward one fixed door.

How will your chosen exit strategy impact the company's valuation and the founders financial outcomes?

Valuation in our model is driven by marketplace liquidity, take-rate economics, and the defensibility of our track-record data, so the more proven hires flow through us, the more strategic and valuable the company becomes to an acquirer. A strategic acquisition typically rewards that network and data premium and gives founders and early investors a clean liquidity outcome, which is why we prioritize building real, defensible value over engineering a premature exit.

What/who is your target market?

Our target market is companies hiring or contracting for AI-native technical roles, AI/ML, automation, backend, frontend, data, and product, with an initial focus on startups, scale-ups, and product or innovation teams that need to ship AI features fast. The parallel market we aggregate is the global pool of AI builders who want their work to speak for them.

What needs does your target market have that your product or service can address?

Our customers need to identify genuinely capable AI builders quickly, reduce the risk and cost of mishiring, and move faster than slow, interview-heavy processes allow. Proven addresses this by giving them ranked proof of work, an objective fit-explained shortlist from reverse-search, and the ability to see a real solution before they commit.

How will you reach your target market?

We reach companies through founder and operator networks, targeted outreach to teams hiring for AI roles, and content that makes the case for proof-of-work hiring, amplified by the public visibility of live challenges and the leaderboard. We reach builders through AI, ML, and developer communities, hackathons, and the organic pull of prizes and a track record worth earning; each strong builder helps attract the next.

Who are your main competitors?

We compete with traditional hiring channels like LinkedIn, recruiting agencies, and resume-based job boards; with freelance and talent marketplaces like Upwork, Toptal, and Braintrust; with AI-talent matchers such as Mercor; and with competition and assessment platforms like Kaggle and hackathon sites. Each touches part of our space, but none combines real-challenge proof of work, an objective AI-skill ranking, and direct hiring the way we do.

What are their strengths and weaknesses?

LinkedIn and job boards have huge reach but rely on self-reported credentials, weak signals for AI ability. Upwork and Toptal offer vetted contractors but lean on portfolios, past gigs, and reviews rather than head-to-head proof on your actual problem. Mercor brings AI-driven matching but still leans on interviews and profiles. Kaggle and hackathons generate real proof of work but aren't built to convert that into hiring. Our weakness today is that we're early and must build liquidity; their strength is scale we have yet to reach.

What are their key marketing strategies?

Job boards and LinkedIn market through ubiquity, employer branding, and recruiter tooling; Upwork and Toptal lean on SEO, performance marketing, and a vetted-quality message; Braintrust emphasizes a network-owned, no-middleman-fee narrative; Mercor leans into AI-matching and speed; Kaggle grows through community, competitions, and prestige. Across the board, community-driven and competition-led growth is the playbook closest to ours.

What are their pricing strategies?

Job boards and LinkedIn sell subscriptions and per-post or recruiter-seat fees; Upwork and Toptal take marketplace fees or markups on contractor work; Braintrust famously charges employers a fee while passing full pay to talent; Kaggle hosts prize-funded competitions. Pricing models in our space already span subscriptions, take-rates, placement fees, and prize pools, validating the components of our own monetization.

How are they positioning themselves in the market?

LinkedIn positions as the professional identity and recruiting graph; Upwork as the broad freelance marketplace; Toptal as the elite vetted-talent network; Braintrust as the fairer, talent-owned alternative; Mercor as AI-powered hiring and matching; Kaggle as the home of data-science competition and prestige. We position distinctly as proof-of-work hiring for AI-native talent: hire what's already proven, on your real problem, ranked objectively.

What are their growth plans?

These players are broadly expanding into AI: job boards and marketplaces are adding AI matching and assessment, vetted networks are widening their talent categories and going upmarket, AI-matching startups are scaling outreach and enterprise sales, and competition platforms are deepening community and corporate partnerships. The common direction, toward AI-driven, evidence-based matching, validates our thesis while leaving the specific proof-of-work-to-hire wedge open for us to own.

What is your competitive advantage?

Our advantage is a dual model on top of a defensible, accumulating track-record graph. Companies can crowdsource a hard AI problem and get it solved cheaply by the crowd, and the very same challenges generate verifiable proof of work and an objective six-axis strength matrix that no resume- or interview-based competitor can replicate. As more builders and challenges flow through us, the solving, the ranking, the data, and the two-sided network compound into a moat that strengthens with scale.

What is your value proposition?

Proven turns hard AI problems into solved work and proven hires. A company posts a real challenge, crowdsources it to many AI builders, and gets working, ranked solutions for the price of a prize instead of a salary, then can hire the proven performer behind the best one, based on a verifiable track record rather than resumes and interviews. For builders, it is a meritocratic stage where the work they ship wins them prizes and opportunities. In short: get it solved, then hire what's already proven.

How is your offering better or different from others in the market?

Unlike credential- and interview-based platforms, we evaluate people on real work on real problems and rank them objectively, so the signal is demonstrated performance, not self-reported claims. Unlike freelance marketplaces that pair you with one contractor chosen on portfolios and reviews, we crowdsource your actual problem to many builders competing head-to-head, so you get multiple working solutions cheaply and pick the best. And unlike pure competition or hackathon platforms, we turn that proof directly into a hiring decision.

What benefits will your target market experience?

Companies get hard AI problems solved fast and cheaply, paying a prize to the crowd rather than a full-time salary or agency markup, plus faster, more confident, lower-risk hiring when they want to bring someone on. They also gain a ranked pool of builders mapped across six concrete skill dimensions. Builders get a public, portable track record that lets their work, not their pedigree, earn them prizes and paid opportunities.

Is your value proposition realistic and achievable?

Yes. We're not promising to predict everything about a hire; we're providing a far stronger signal than the status quo by basing decisions on demonstrated work on a relevant problem, which is a realistic, well-understood improvement. The mechanics, challenge, ranked submissions, winner selection, and a track record, are already live in our MVP, so this is achievable, not aspirational.

Have you tested it out on potential customers for feedback?

We're validating it now with our first cohort of builders and an initial company challenge live on the MVP, and we're actively gathering feedback from both sides to sharpen the proposition. It's early, the evidence is directional rather than statistically conclusive, but the early signal is that companies value seeing real work before committing and builders value a track record they own.

What problem are you solving with your business model?

Our model attacks two inefficiencies at once: companies overpay for slow, low-signal hiring and still mishire, and they cannot get hard AI problems solved affordably without committing to a costly full-time hire or agency. By letting them crowdsource challenges for the price of a prize and then monetizing successful matches and hires rather than noise, we align what we earn with the real outcomes customers care about, work done and a good hire made with confidence.

Who is your target market within this model?

Within the model, the paying side is companies hiring or contracting for AI-native roles, with first focus on startups, scale-ups, and product or innovation teams that move fast and feel hiring risk acutely. Builders are the non-paying supply side whose free participation and accumulating track record create the talent pool companies pay to access and hire from.

How will you make money?

Companies pay to post challenges and fund the prize pool that goes to winning builders, so they get a hard problem solved for far less than a hire, while we take a platform fee on each challenge and a success or placement fee when a challenge leads to a hire or paid engagement. We layer on access and recruiter subscriptions for ongoing reverse-search and shortlisting of the ranked talent pool, and a marketplace take-rate lets revenue scale with the value of the work and matches we enable. Builders always join and compete for free.

What is your competitive landscape in this model?

Within our monetization model, we sit between subscription job boards and recruiter tools (LinkedIn), marketplace take-rate platforms (Upwork, Toptal, Braintrust), AI matchers (Mercor), and prize-funded competition sites (Kaggle), borrowing the proven mechanics, subscriptions, take-rates, placement fees, prize pools, while combining them around an outcome none of them sells: a hire made on objective proof of work for AI-native skills.

How will you scale your business?

We scale by deepening marketplace liquidity, more builders make every challenge and search more valuable, which attracts more companies, which funds more prizes and draws more builders, a compounding two-sided network. The product is software with low marginal cost per match, our take-rate and subscription revenue grow with volume, and as our track-record data deepens, matching quality improves, strengthening the loop as we expand across AI-skill categories and geographies.

Who is your target market for sales and marketing?

Our sales and marketing target is companies hiring for AI-native roles, led by founders, engineering and product leaders, and talent or recruiting teams at startups, scale-ups, and product groups inside larger firms. In parallel, we run community-led marketing to AI builders, since growing and engaging that supply is what makes the demand side worth selling to.

What needs does your target market have that your product or service can address in sales?

These buyers need to hire capable AI builders quickly, de-risk expensive hiring decisions, and avoid wasting weeks on interviews that don't predict performance. Our message in sales is that they can see real, ranked proof of work on their actual problem before committing, turning a high-stakes gamble into an evidence-based decision, which is exactly the reassurance a hiring manager wants to hear.

How will you reach your target market for sales?

We reach companies through direct founder-led outreach, warm introductions from operator networks, and targeted content and thought leadership on proof-of-work hiring, with the visibility of live challenges and the public leaderboard acting as ongoing inbound pull. We reach builders through AI, ML, and developer communities, hackathon partnerships, and the organic draw of prizes and a track record worth earning.

What are your unique selling points in marketing?

Our unique selling points are: crowdsource a hard AI problem and get working solutions for the price of a prize, not a salary; objective, verifiable proof of work over resumes and interviews; a six-axis strength matrix that maps AI talent concretely; and a platform that turns demonstrated performance directly into a hire. The line captures it: get it solved, then hire what's already proven.

How will you convert leads into customers?

We convert by getting a company to post one real challenge or run one reverse-search, then letting the experience sell itself, seeing ranked, working submissions on their actual problem is far more persuasive than any pitch. From there we move them to a paid hire or engagement with a placement fee, and to ongoing subscription access once they've felt the value, with founder-led, high-touch onboarding for these first customers.

What are your plans for scaling your business in terms of sales and marketing?

Early on, sales is founder-led and high-touch to learn the buyer and prove the model; as we accumulate reference hires and case studies, we shift weight toward repeatable inbound, community-driven supply growth, and a lighter-touch self-serve motion for posting challenges and searching the pool. The network effect does increasing work as the talent pool and leaderboard grow, lowering our cost to acquire each new company over time.

What are your budgets for sales and marketing?

As a pre-seed team, we keep sales and marketing spend lean and efficiency-focused, prioritizing founder-led outreach, community engagement, and content over paid acquisition, since our strongest early channel is the organic pull of challenges, prizes, and a visible leaderboard. A meaningful share of any raise will fund disciplined go-to-market experiments, but only once we've validated which channels convert; we'd rather earn growth through proof than buy it prematurely.

What does your team do better than any other team in the world?

We are an unusually fast, full-stack, applied-AI founding team that designed, built, and shipped the entire product, marketplace, ranking, strength matrix, proof-of-work flow, and reverse-search, ourselves and quickly. That combination of product sense, technical depth across AI and full-stack, and shipping velocity is what lets us out-iterate larger teams in a space that's moving faster than incumbents can adapt.

What are the unique skills and experiences that your team brings to the table?

The three of us are hands-on technical co-founders who span the full stack and applied AI, so we can design the product, build the matching and ranking systems, and ship live features without waiting on anyone. Critically, we are exactly the kind of people our product judges, AI builders who've shipped real work, which gives us deep, first-hand insight into what makes the signal trustworthy and the marketplace fair.

How do those strengths help you execute on your vision better than anyone?

Because we can build and ship ourselves, we iterate on real user feedback in days rather than quarters, which matters enormously in a fast-moving field where the right design isn't obvious up front. And because we understand both the technical heart of the product and the lived reality of being evaluated as an AI builder, we're well placed to get the hardest part, trustworthy, gameable-resistant proof of work, right.

What are your financial projections for the startup?

Our projections are directional at this stage: in the first 12 months we aim to prove the model by running a growing number of paid challenges and converting a first set of successful matches into placement-fee and subscription revenue, establishing unit economics rather than headline scale. From there, our plan assumes revenue compounds with marketplace liquidity as take-rate, placement, and subscription streams stack on a growing base of companies and builders.

How much money will you need to raise based on these projections?

We're raising a pre-seed round sized to reach clear marketplace-liquidity and early-revenue milestones, roughly the runway to deepen the builder pool, land and prove paying companies, and harden the trust and ranking systems before a seed round. The amount is calibrated to those milestones rather than a fixed headcount, and we'll right-size it to the validation an investor wants to see; we're being deliberately conservative given pre-seed reality.

How quickly does your startup need to grow financially?

We need to grow fast enough to establish two-sided liquidity before momentum stalls, since a marketplace that doesn't reach critical mass on both sides loses value, so our near-term urgency is about matches and engagement, not premature revenue maximization. Once liquidity and repeatable monetization are proven, the goal shifts to efficient, compounding revenue growth that justifies a seed round and beyond.

What are your costs and revenue projections?

Our main early costs are the small founding team, product and infrastructure, and lean go-to-market, with prize pools funded by the companies posting challenges rather than by us. Revenue is illustrative at this stage and comes from placement and success fees, paid challenge postings, pool-access and recruiter subscriptions, and a marketplace take-rate; we expect it to start modest while we build liquidity and scale as the volume and quality of matches grow.

How will you generate revenue and achieve profitability?

We generate revenue by monetizing successful, evidence-based matches, placement and success fees when hires happen, paid challenge postings, subscription access to the ranked talent pool, and a marketplace take-rate, while builders participate free. Because the product is software with low marginal cost per match, the path to profitability is reaching enough match volume that recurring and take-rate revenue covers our lean cost base, after which strong marketplace economics let margins expand with scale.