Every VC associate knows the feeling: it's Tuesday morning, your inbox has 47 new pitch decks, your CRM shows 63 companies flagged for review, and your partner wants the "top 5" by Thursday's IC meeting. You have roughly 10 hours this week for deal screening. The math doesn't work — unless you have a system.
At a typical Series A/B fund, an associate or principal reviews 100-150 inbound deals per week. These come from multiple channels: cold inbound, warm introductions, accelerator demo days, portfolio referrals, and outbound sourcing. Of those 100+ companies, statistically:
The challenge isn't finding good deals — it's efficiently eliminating the 95% that aren't right so you can spend quality time on the 5% that matter. Speed without accuracy wastes everyone's time. Accuracy without speed means you miss the best deals because you were buried in mediocre ones.
For the 40 deals that pass your initial filter, you need a systematic 5-minute assessment. Not every company deserves an hour. Here's the framework top associates use:
LinkedIn the founders. What you're looking for: domain expertise (did they work in the industry they're disrupting?), builder credibility (have they shipped products before?), complementary skills (technical + commercial), and network quality (who invested, who advises). Quick kill: solo non-technical founder in a technical space, no domain experience, or a team that's never worked together.
Is this a market you believe in? Does the TAM support a venture-scale outcome ($1B+)? Is the timing right — are there structural tailwinds (regulatory changes, technology shifts, behavioral changes) or is this a "nice to have"? Quick kill: TAM under $5B, market already consolidated, or no clear tailwind.
What's the evidence of product-market fit? Revenue ($ARR, growth rate, NRR), usage metrics (DAU/MAU ratio, engagement depth), or pre-revenue signals (waitlist size, LOIs, design partners). Stage-appropriate expectations: Seed = early signals, Series A = repeatable revenue, Series B = scalable unit economics. Quick kill: Series A with no revenue, or Series B with declining growth.
Burn multiple (net burn / net new ARR) — below 2x is excellent, above 4x is concerning. Gross margins — should be 70%+ for software. LTV/CAC — at least 3x, ideally 5x+. Payback period — under 18 months for SaaS. Quick kill: Burn multiple above 5x with no clear path to efficiency, or gross margins below 50%.
| Red Flag | Why It Matters | % of Inbound |
|---|---|---|
| Wrong stage for your fund | Pre-seed deck to a Series B fund. Instant pass. | ~20% |
| Outside sector focus | Hardware pitch to a SaaS fund. No expertise to evaluate. | ~15% |
| No differentiation visible | "Uber for X" with no unique insight. Competitive moat unclear. | ~10% |
| Team red flags | 3rd-time founder who's never reached PMF. Part-time founders. Outsourced core product. | ~8% |
| Unrealistic ask / valuation | Pre-revenue company seeking $50M at $200M valuation. Math doesn't work. | ~5% |
| Stale round / zombie raise | Been raising for 9+ months. Something is wrong. | ~2% |
After your 5-minute assessment, every deal goes into one of three tiers:
Criteria: Strong team with domain expertise + clear traction signal + large market + reasonable valuation. You'd invest your own money.
Action: Write a 1-page investment memo. Schedule partner intro within 48 hours. Begin reference checks immediately.
Criteria: Interesting but missing one element. Great team but early traction. Strong traction but crowded market. Worth tracking but not acting on now.
Action: Add to CRM with a 3-month follow-up. Send a polite "not right now, but keep us posted" email. Set alerts for future funding rounds.
Criteria: Doesn't meet your fund's criteria on multiple dimensions. No shame in this — it's 80% of what you see.
Action: Polite decline within 48 hours. Be specific about why (stage, sector, timing) — founders remember associates who give real feedback. Log in CRM for pattern analysis.
After studying the workflows of associates and principals at firms like Sequoia, a16z, Index Ventures, and Benchmark, clear patterns emerge:
| Tool | Best For | Limitations | Price | Our Rating |
|---|---|---|---|---|
| Affinity | Relationship intelligence, automatic contact/email capture, deal pipeline | Expensive, steep learning curve, overkill for small funds | $$$ | ⭐⭐⭐⭐⭐ |
| Attio | Modern UI, flexible data model, good API, growing VC-specific features | Newer product, smaller ecosystem, fewer integrations | $$ | ⭐⭐⭐⭐ |
| Notion | Flexible, cheap, good for small teams, customizable databases | Not built for CRM, no email integration, manual data entry | $ | ⭐⭐⭐ |
| Spreadsheets | Free, universal, no learning curve, everyone knows Excel/Sheets | No automation, breaks at scale, no relationship tracking, version hell | Free | ⭐⭐ |
Here's the insight that changes the math: the quantitative portion of deal screening is automatable. Market size data, funding history, team background checks, traction metrics, competitive landscape mapping — all of this can be systematically gathered and scored before a human ever looks at the deal.
PV1 automates the first two stages of the funnel:
The result: associates spend zero time on Tier C deals and minimal time on Tier B. Their 10 hours per week go entirely into deep evaluation of the 15 deals that have already passed quantitative screening. This is how you find the needle without touching every piece of hay.
Total: ~10 hours. The same amount of time most associates spend — but now focused entirely on the deals that matter, with the mechanical screening automated away.
PV1 automates the quantitative screen so your team spends time on what humans do best: evaluating founders, assessing market timing, and making judgment calls. Join the funds that screen smarter, not harder.
.svg.svg)