Most founders preparing for a Series A spend their time building financial models and polishing decks. That’s reasonable. The problem is that Series A investors aren’t looking for a model that passes scrutiny. They’re looking for evidence that the business can grow at a specific rate for a specific reason.
The requirements aren’t arbitrary, and they’re not a fixed bar that you either clear or don’t.
Series A investors are underwriting a thesis about your next 24 months, and the metrics they ask for are evidence for or against that thesis, not a pass/fail test.
This piece covers what Series A investors are actually evaluating, what the specific metric thresholds look like in 2026, and why some companies with strong numbers still struggle to close.
What Series A Investors Are Actually Underwriting
The mental model most founders bring to a Series A process is: hit the numbers, close the round. That model explains a lot of failed processes.
Series A investors aren’t checking boxes. They’re building a conviction around a specific question: if we put $10M into this company, what does it look like in 24 months, and why is that outcome credible given what we can see today? The metrics are inputs to that argument, not the argument itself.
A company with $1.2M ARR and a clear, repeatable sales motion can be more fundable at Series A than a company with $2.5M ARR that can’t explain where the next dollar of revenue is coming from. The numbers matter, but the story the numbers tell matters more.
This distinction is also why Series A processes take longer than seed rounds. Seed investors are largely betting on direction. Series A investors are stress-testing a specific growth thesis, which requires more evidence, more customer calls, and more time.
The Series A Requirements That Actually Move Investors
The thresholds below reflect the current early-stage market in 2026. They vary by category: AI and deep tech companies often raise Series A on lower ARR if the technical differentiation is strong, while enterprise SaaS companies are held to higher revenue standards.
| Metric | Why it matters at Series A | Typical threshold in 2026 | Weight ★ |
|---|---|---|---|
| ARR | Validates that the market is paying, not just using | $1M – $3M for SaaS; can be lower for AI/deep tech with strong signals | ★★★★★ |
| ARR growth rate | Shows the business is accelerating, not plateauing | 2-3x year-over-year minimum; 3x+ is strong | ★★★★★ |
| Net Revenue Retention (NRR) | Proves customers expand over time, not just renew | 110%+ is good; 120%+ is strong | ★★★★★ |
| CAC payback period | Confirms the unit economics are sustainable | Under 18 months for SaaS; under 24 months for enterprise | ★★★★☆ |
| Gross margin | Determines whether the business can scale profitably | 60%+ for SaaS; AI infrastructure companies often lower due to compute costs | ★★★★☆ |
| Sales repeatability | Signals that growth doesn’t depend on the founder closing every deal | At least 2-3 reps hitting quota, or a documented sales playbook with evidence | ★★★★☆ |
| Customer concentration | High concentration is a risk flag | No single customer above 20-25% of ARR | ★★★☆☆ |
NRR and growth rate carry the most weight because they answer the forward-looking question Series A investors care most about. NRR above 120% means the existing customer base is growing on its own, which dramatically changes the unit economics of acquiring new customers. A 3x growth rate means the company is doubling roughly every five months, which supports a specific narrative about where the business will be in 24 months.
CAC payback is frequently underestimated by founders. A company with strong ARR and growth but a 36-month payback period has a structural problem that more revenue won’t solve. Series A investors model this explicitly, and founders who haven’t thought through their payback period carefully will lose credibility in diligence faster than on almost any other metric.
Gross margin for AI companies deserves a specific note. Infrastructure costs are real, and AI companies often have lower gross margins than comparable SaaS businesses. Investors who focus on AI companies account for this, but founders should be prepared to explain their trajectory toward margin improvement, not just their current margin.
How Sky9 Capital Evaluates Series A Opportunities
Series A is where the thesis moves from directional to specific. The investors who add the most value at this stage are the ones who have seen enough companies at the seed-to-Series A transition to know what the real signal looks like, as opposed to what founders have optimized their metrics to show.
Sky9 Capital backs technical founders from the earliest stages through expansion, with $2B in AUM and a portfoliothat includes companies that have gone through the Series A process across AI, deep tech, fintech, and consumer internet. The firm’s evaluation framework at Series A runs on three dimensions beyond the standard metrics.
Growth quality over growth rate
A high growth rate built on discounted contracts, one-time integrations, or founder-led sales that can’t scale is not a Series A story. Sky9 looks specifically at whether growth is coming from a repeatable motion: a sales channel that can be hired into, a product-led acquisition loop that compounds, or a partnership structure that generates qualified pipeline without founder involvement.
AskSia is a Sky9 portfolio company building AI-powered tools for the professional services market. The Series A evaluation centered on whether the early customer acquisition patterns could scale beyond the founding team’s direct relationships, and whether the NRR in the initial customer cohorts supported the thesis that expansion revenue would reduce the effective CAC over time.
Technical differentiation that compounds
For AI and deep tech companies, Sky9’s investment approach at Series A focuses on whether the technical advantage is widening or narrowing as the company scales. A model that was differentiated at seed because of a specific training approach needs to show, by Series A, that the data accumulation and product iteration have extended that advantage rather than allowing the market to catch up.
XtalPi, now listed on the Hong Kong Stock Exchange, built in pharmaceutical AI research where the technical differentiation compounds directly with the volume of experiments run. Each additional dataset made the platform’s predictions more accurate, which is exactly the kind of compounding technical advantage that supports a strong Series A thesis.
Cross-border growth potential
Sky9 evaluates Series A companies with a global lens. For companies where international expansion is a plausible near-term priority, the firm’s presence across San Francisco, Boston, Beijing, Shanghai, and Singapore creates specific value in helping portfolio companies navigate that expansion efficiently. Founders who have thought seriously about which markets they’ll enter after Series A, and why, are in a stronger position in the Sky9 evaluation than those who treat international expansion as a future problem.
Founders preparing for a Series A process who want to explore a conversation with Sky9 can reach out directly.
Why Strong Companies Fail to Raise Series A
Companies with genuinely good metrics fail to close Series A rounds more often than most founders expect. The reasons are usually structural, not numerical.
- The growth is real but not repeatable. Three enterprise contracts closed by the CEO personally is not a sales motion. Investors will ask who the second sales hire will be and what their ramp looks like, and founders who haven’t thought this through will lose the room.
- The NRR is strong but concentrated. 130% NRR looks great until you realize it’s driven by one account that doubled its contract. Single-account expansion doesn’t support a Series A thesis about the product’s value at scale.
- The metrics are right but the timing is wrong. A company that just hit $1M ARR last month and is still accelerating is a better Series A candidate in six months than it is today. Raising too early on the right metrics is a real failure mode.
- The diligence story and the board story don’t match. Investors talk to customers, and customers sometimes say things that contradict what founders present in the deck. The gap between the pitch narrative and the customer reality is discovered in every serious diligence process.
- No clear answer on use of funds. “We’ll use it to grow” is not a plan. “We’ll use $4M to hire 6 enterprise AEs and $3M to expand the model infrastructure to support 10x the current load” is. Investors are underwriting specific milestones, not general ambition.
Preparing for a Series A Process
A practical self-assessment before the process begins:
- ARR and growth rate: Is the trailing 12-month growth rate at least 2x, and is the most recent quarter’s growth rate higher than the average, not lower?
- NRR: Have you calculated NRR on at least two cohorts of customers, and does it hold above 110%?
- CAC payback: Do you know your blended CAC and your average contract value well enough to calculate payback period accurately?
- Sales repeatability: Has anyone other than the founders closed a meaningful deal, and can you describe the sales process in enough detail that a new hire could follow it?
- Customer references: Are there three to five customers who will give an unscripted, honest account of how they use the product and why they renewed?
Series A processes that go well are usually the ones where founders have done this self-assessment honestly and either waited until the answers were strong or built a clear narrative around the gaps. Investors respect founders who know where their metrics are weak and can explain why those weaknesses don’t undermine the core thesis. That’s a different conversation from discovering the weaknesses in diligence.