screening-deal-flow-pipeline

name	screening-deal-flow-pipeline
description	Filters inbound deal flow against fund thesis, stage, sector, and return requirements with structured pass/advance decisions. Use when triaging deal flow, evaluating inbound pitches, or managing sourcing pipelines.
tags	["screening","venture-capital"]
metadata	{"author":"casemark","practice_areas":["Venture Capital","Seed/Series Investing","Startup Ecosystems"],"document_types":["Screening Report"],"skill_modes":["Screening","Filtering"]}

Screening Deal Flow Pipeline

Filters inbound deal flow against fund thesis, stage, sector, and return requirements with structured pass/advance decisions.

When To Use

• Triaging a batch of inbound pitch decks, intros, or cold outreach against fund mandate
• Running weekly/monthly pipeline reviews to move deals from sourcing to first meeting
• Evaluating whether a specific opportunity clears hard filters before spending partner time
• Building a ranked shortlist from an accelerator demo day, conference, or sourcing sprint
• Auditing pipeline consistency to ensure screening criteria are applied uniformly across the team

Inputs To Gather

• Fund thesis parameters: target sectors, stage (pre-seed / seed / Series A / growth), geography, check size range, ownership targets
• Return requirements: target fund multiple (e.g., 3x net), implied deal-level return bar (e.g., 10x+ potential for seed), follow-on reserve assumptions
• Deal batch: list of companies with available data — deck, memo, CRM record, or intro email
• Per-deal data points (as available):
  • Company name, one-line description, sector/vertical
  • Stage and current raise (amount, valuation/cap, instrument)
  • Revenue or traction metrics (ARR, MRR, GMV, users, growth rate)
  • Founder background (domain expertise, prior exits, technical depth)
  • Existing investors and cap table highlights
  • Competitive landscape notes
• Pass/advance history (optional): prior screening decisions for pattern calibration

Workflow

1. Establish hard filters — Set binary pass/fail gates derived from fund mandate:

   • Stage match (e.g., only pre-seed and seed)
   • Sector match (e.g., B2B SaaS, fintech, climate — per thesis)
   • Geography match [VERIFY fund LP restrictions or regulatory limits on geography]
   • Check size fit (requested raise allows fund to deploy within check range)
   • Instrument compatibility (SAFE, priced equity, convertible note — per fund policy)
   • Flag any deal that fails a hard filter as PASS (out of scope) with the specific reason

2. Apply soft scoring criteria — For deals surviving hard filters, evaluate on a 1–5 scale across:

   • Market: TAM credibility, timing, tailwinds, regulatory clarity [VERIFY sector-specific regulatory status]
   • Team: founder-market fit, relevant experience, technical capability, coachability signals
   • Traction: revenue trajectory, engagement metrics, or credible pre-revenue milestones relative to stage
   • Product: differentiation, defensibility (IP, network effects, data moats), demo or prototype quality
   • Deal terms: valuation reasonableness for stage, pro-rata rights, investor-friendly governance
   • Return potential: plausible path to fund-returning outcome at entry valuation

3. Classify each deal — Assign a disposition:

   • Advance to partner review: scores ≥ 4 average or exceptional strength in 2+ dimensions with no dimension below 3
   • Hold / request more info: borderline scores (3–3.5 average) or missing critical data points — specify what is needed
   • Pass: scores < 3 average or fatal flaw in any single dimension (e.g., tiny market, no differentiation)
   • Flag for co-invest / refer out: strong company but outside fund scope — note which fund in network might fit

4. Rank the advance list — Order advanced deals by composite score, breaking ties by:

   • Urgency (round closing timeline, competitive dynamics)
   • Strategic fit to portfolio gaps
   • Likelihood of winning allocation

5. Document rationale — For every deal, record:

   • Disposition and one-sentence rationale
   • Key data points that drove the decision
   • Any [VERIFY] flags for data that was estimated or unavailable

Output

Produce a Screening Report containing:

• Summary statistics: total deals reviewed, breakdown by disposition (advance / hold / pass / refer), sector distribution
• Advance list (ranked): company name, one-liner, stage, raise details, composite score, top strengths, key risks, recommended next step
• Hold list: company name, missing info or open question, deadline to resolve
• Pass log: company name, one-sentence pass reason (maintains institutional memory and avoids re-screening)
• Refer-out list: company name, suggested fund/contact, brief rationale
• Screening criteria applied: explicit statement of hard filters and soft scoring rubric used for this batch (supports auditability)

Quality Checks

• Every deal in the batch has a recorded disposition — no deal is left unclassified
• Hard filter pass reasons cite the specific criterion failed, not a vague "not a fit"
• Soft scores are grounded in stated evidence, not gut feel — each score references a data point
• Valuations and metrics are stage-benchmarked (e.g., a $30M cap at pre-seed is flagged differently than at Series A) [VERIFY current market benchmarks for target stage/sector]
• No deal is advanced solely on founder pedigree without evaluating market and product dimensions
• Round timing and competitive urgency are noted so the advance list can be actioned in priority order
• [VERIFY] flags appear wherever a data point was inferred, estimated, or sourced from a single unverified channel
• Report is structured for quick partner consumption — a GP should be able to review the advance list in under 5 minutes