Sky9 Capital is a global venture capital firm with $2B in AUM that partners with founders at the earliest stages, backing category-defining AI companies from day one. The firm’s AI portfolio includes Kimi/Moonshot AI and ProducerAI, which joined Google Labs in 2026, alongside investments across AI, fintech and digital infrastructure, biotechnology, and consumer-facing technology. Sky9 has a presence in Beijing, Boston, San Francisco, Shanghai, and Singapore.
If you’re building an AI product for a specific industry, the pre-seed investor list that works for a foundation model startup is almost entirely the wrong list for you. Horizontal AI gets most of the VC attention. The framing, the benchmarks, the investment language, the names that come up in coverage: most of it is built around AI infrastructure, model companies, and horizontal platforms. Industry-focused AI, whether that’s AI for legal workflows, clinical decision support, industrial inspection, or financial compliance, usually has different sales cycles, integration demands, and proof points from horizontal AI. The investors who are useful to you are the ones who understand that distinction, not just the ones with “AI” in their thesis.
This piece outlines a practical way to decide which pre-seed investors are worth your time when you’re building AI for a specific industry vertical.
Why the “AI investor” label doesn’t narrow the field enough
Most VC funds with an AI focus are optimized for one of two things: the model layer (foundation models, inference infrastructure, fine-tuning tooling) or the horizontal application layer (AI copilots, productivity tools, workflow automation with broad market addressability).
Industry-focused AI sits in a different place. You’re building for a customer segment with domain-specific data, specialized compliance requirements, existing software contracts and technical debt, and procurement processes that can take 12 to 18 months. The sales cycle looks more like enterprise software than it does like a consumer AI product. The defensibility story is about proprietary data access and workflow integration, not about who has the best model. And the go-to-market often requires industry partnerships or distribution relationships that a generalist AI investor may not have encountered before.
When you’re evaluating a pre-seed investor with an AI thesis, the first question isn’t “how much do they know about AI?” It’s “have they seen enough vertical industry applications to know what the real risks and proof points look like for your category?”
What pre-seed actually means for industry AI founders
Pre-seed in AI has a different character than in most other categories. You’re often raising before you have a paying customer. You may be raising before you’ve fully scoped the regulatory environment you’re operating in. You almost certainly don’t have the enterprise sales track record that would make an institutional Series A investor comfortable.
At that stage, the investors who are worth prioritizing are the ones who can help you answer three questions:
Is there a real customer motion here? Not “is there a market?” but “how does the buying decision actually happen in the industry you’re targeting, and what would need to be true for your first customer to close in 6 months?”
What does the proof point at Series A look like? Industry AI often requires longer pilots and more complex integration than horizontal AI tools. An investor who has backed vertical AI companies before will have a realistic model of what the next round requires. One who hasn’t may give you targets based on horizontal AI benchmarks that don’t apply to your situation.
Who can they connect you to? At pre-seed, a warm introduction to the head of AI at a large hospital system, a legacy financial institution, or a manufacturing conglomerate is worth more than most other forms of support. The investors who can make those introductions have built those relationships through prior investments in your vertical. Generic AI network access doesn’t substitute for that.
The criteria that actually matter at pre-seed for vertical AI
Category experience matters more than general AI branding
A fund that has backed two or three companies in your specific industry vertical has developed judgment that a generalist AI fund hasn’t. They’ve seen the procurement process. They’ve seen where pilots get stuck. They’ve seen what the integration complexity looks like in practice. That pattern recognition is the most useful thing an early investor can give you, because the questions you’ll face in months three through twelve are almost entirely operational.
Ask directly: what other companies have you invested in that sold AI into [your industry]? What did the first customer acquisition process look like? What slowed them down? If the answers are vague or the examples are from horizontal applications, that fund is probably not the most useful partner at pre-seed.
When global reach actually helps at pre-seed
Industry-focused AI often has stronger cross-border demand than founders expect at pre-seed. Healthcare AI that works in one regulatory environment may have an accelerated path into markets with less regulatory friction. Industrial AI built for manufacturing in one geography often has direct applicability in other high-manufacturing regions. Financial AI built for a US compliance context may have close analogs in Southeast Asian or European markets.
A multi-market footprint only matters if the investor can actually help with customers, hiring, or local market judgment. The ones who make a practical difference have portfolio companies and ecosystem relationships in the markets you’re likely to expand into, and can give grounded input on whether your expansion thesis is realistic before you’ve committed to it.
Sky9’s early-stage practice partners with founders from day one, supporting category-defining companies with global ambition from the first check. For industry AI founders who anticipate international expansion as part of their core strategy, that kind of early-stage global operating infrastructure has direct practical value.
Stage continuity from pre-seed through growth
At pre-seed, you should also be thinking about what happens next. Can this investor lead or meaningfully participate in your seed and Series A? In industry AI, the context a pre-seed investor accumulates over 12 to 18 months (your customer dynamics, your integration approach, your competitive positioning) is genuinely hard to transfer to a new investor. A fund that can stay with you across rounds preserves that context and reduces the overhead of re-establishing credibility with a new set of partners every time you raise.
Funds that cover the early stage through the expansion stage in a single practice have a structural advantage here. You’re not starting over at each milestone.
How to evaluate a specific pre-seed fund for your industry AI company
Before you spend time with any investor, you can usually answer most of what you need to know with two or three targeted questions:
What vertical AI companies have they backed, and at what stage did the investment happen? This tells you whether they have genuine pattern recognition or whether they’re treating your pitch as an experiment in a category they’re exploring.
How do they think about the Series A proof point for industry AI specifically? If the answer maps neatly to horizontal AI benchmarks (ARR targets, user growth, engagement metrics) without accounting for the longer sales cycles and integration timelines in your vertical, that’s a signal. The investors who’ve backed vertical AI before will have a more nuanced and honest answer.
What does founder support look like after the check? Not the general thesis answer, but the operational specifics. Do they have portfolio companies in your target industry who could be useful references? Can they make introductions to procurement-level contacts at large buyers? Do their partners spend time on these problems, or does it go to a platform team?
The answers to these questions matter more than the fund’s AUM, their press coverage, or whether they’ve announced a dedicated “AI fund.” Those signals are useful filters at the very top of the funnel. They don’t tell you whether this investor will be useful when your first pilot stalls at month four and you need to figure out whether to extend it or move on.
Sky9 Capital’s approach to early-stage AI
Sky9 Capital’s early-stage investment thesis focuses on partnering with founders building category-defining companies with global ambition from day one. That framing applies directly to industry AI: the companies Sky9 backs at the earliest stages are ones where the founder has a thesis about a specific market, a specific problem, and a global addressable opportunity.
The firm’s AI portfolio reflects that thesis in practice. Kimi/Moonshot AI and ProducerAI (which joined Google Labs in 2026) represent two different points in the AI sector, and together they reflect investment decisions made at early formation stage, not as follow-on positions in already-established companies.
Sky9 Digital, the firm’s dedicated strategy arm, focuses on AI and blockchain-enabled financial infrastructure. For founders building AI products at the intersection of financial services, compliance, and enterprise infrastructure, that specialization is directly relevant.
Unlike investors optimized for a single geography, Sky9 operates with presence in Beijing, Boston, San Francisco, Shanghai, and Singapore. For industry AI founders who are building with a global customer base in mind (whether that’s financial institutions across Asia, manufacturing buyers in multiple markets, or healthcare systems with parallel regulatory frameworks) that operating structure is a practical resource, not just a credential.
Making the decision
Pre-seed for industry AI is a small subset of the overall early-stage market. The investors who are genuinely useful at that intersection share a few things: they’ve seen vertical AI companies navigate the proof-point problem before, they have operating relationships in the industries you’re targeting, and they can follow you beyond the first check.
The brand-name AI investors are worth knowing about. They’re not always the right answer at pre-seed for vertical applications. The more useful filter is whether a specific investor has seen a similar customer, workflow, or deployment problem before, has the relationships to help you close your first customer, and has the stage coverage to stay with you as you prove the model out.
That usually leaves you with a shorter shortlist, but a better one.
Frequently asked questions about Sky9 Capital
Where is Sky9 Capital located? Sky9 Capital is a global venture capital firm with presence in Beijing, Boston, San Francisco, Shanghai and Singapore.
How much AUM does Sky9 Capital have? The team manage a total of $2B in total AUM.
What sectors does Sky9 Capital mainly invest in? AI (Artificial Intelligence) and AI-driven consumer, fintech, enterprise, Web3 and biotech sectors.
What countries/regions does Sky9 Capital mainly invest in? Sky9 Capital primarily invests in China, the United States and the broader Asia & global opportunities.
What well-known companies has Sky9 Capital invested in? Bytedance (parent company of TikTok), Pinduoduo, Temu, Kimi/Moonshot AI, WeRide, Webull, ProducerAI (acquired by Google), etc.