Artifex/docs/venture_cohort_qwen_concurrency2_run.md

Venture Cohort Qwen Concurrency-2 Run

Date: 2026-08-16

Purpose

Run a 10-company Venture Discovery cohort on Spark using Qwen-only model routing, capped at concurrency 2, after adding Qwen retry/probe support and a reusable Django management command.

Commits

• 44c5ba4 Add Qwen retry and probe command
• 526e280 Add Venture cohort run command

New Commands

Probe Qwen/local inference capacity:

DATABASE_URL=sqlite:///db.sqlite3 PATH=/home/daniel/Artifex/.venv/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin .venv/bin/python manage.py qwen_service_probe --requests 2 --concurrency 2 --token-budget 256

Run a Venture Discovery cohort:

DATABASE_URL=sqlite:///db.sqlite3 PATH=/home/daniel/Artifex/.venv/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin .venv/bin/python manage.py run_venture_cohort --size 10 --concurrency 2 --qwen-only --persist-requests

Useful flags:

• --size: number of companies to generate.
• --concurrency: bounded graph/service concurrency.
• --qwen-only: routes Venture ideation, research, and portfolio IC roles to Qwen.
• --persist-requests: persists sanitized model request telemetry.
• --no-web-research: disables SearXNG/page-fetch research.
• --indent 0: compact JSON output.

Verification

Local targeted tests:

python -m pytest -q tests/test_model_router_providers.py tests/test_venture_discovery_cohort_v02.py

Result: 12 passed.

Spark targeted provider tests:

PATH=/home/daniel/Artifex/.venv/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin PYTEST_ADDOPTS= .venv/bin/python -m pytest -p no:django -q tests/test_model_router_providers.py

Result: 6 passed.

Spark Qwen Probe

Before the cohort run:

{
  "before_health": "AVAILABLE",
  "after_health": "AVAILABLE",
  "requests": 2,
  "concurrency": 2,
  "completed": 2,
  "failed": 0,
  "runtime_seconds": 1.19
}

Cohort Run Result

• Graph run: 15
• Status: COMPLETE
• Cohort: VDV02-20260816085736-69d4879b
• Cohort primary key: e2f736e1-a095-4a5c-931a-d4857c46d48c
• Members: 10
• Concurrency: 2
• Peak concurrency: 2
• Fallback count: 0
• Generation sources: qwen
• Total sources: 22
• Report artifact: 84ada259-8c89-4f5e-a680-b7c24a7c4220

Metrics:

{
  "proposal_generation_runtime_seconds": 270.0,
  "proposal_generation_peak_concurrency": 2,
  "research_runtime_seconds": 133.62,
  "total_sources": 22,
  "public_research_queries": 10,
  "research_peak_concurrency": 2,
  "peak_concurrency": 2,
  "individual_diligence_runtime_seconds": 3.8,
  "individual_diligence_peak_concurrency": 2
}

All 10 Company Details

Rank 1: Contract Clause Risk Scanner for Freelancers

Portfolio score: 62.3. IC decision: REVISE_AND_RESUBMIT. Composite score: 60.6. P($500/30d): 55.0%. Raw P($500/30d): 61.0%. Evidence ceiling: 55.0%. Evidence tier: TIER_1_PUBLIC_EVIDENCE. Generation source: qwen. Fallback: false.

Description: A lightweight web application where freelancers upload or paste contract text. Local LLM inference identifies high-risk clauses such as unlimited revisions, IP ownership ambiguity, and late payment penalties, then provides plain-English explanations and suggested counter-clauses. It is positioned as a pre-legal review tool, not a law firm.

Problem: Freelancers often sign contracts without understanding legal jargon, leading to unpaid work, IP disputes, and scope creep. Hiring a lawyer for every contract is too expensive, while ignoring contract risk is dangerous.

Target customer: Independent writers, designers, and developers billing $50k-$150k annually and signing 5-10 contracts per month.

Proposed solution: A SaaS scanner that ingests contract text, runs it through a local LLM workflow trained around common freelance contract pitfalls, and outputs a risk score, highlighted clauses, suggested edits, and a PDF report for negotiation.

Business model: Freemium SaaS with one free scan per month and a Pro tier for unlimited scans and priority support.

Pricing hypothesis: $19/month for Pro.

Acquisition strategy: Content-led SEO and community presence through articles such as Top 10 Dangerous Freelance Clauses, plus useful posts in freelance communities and forums. No cold outreach.

Validation plan: Build a static landing page with waitlist and sample risk report, publish educational content in three freelance forums or LinkedIn, measure waitlist conversion and email engagement, and proceed only if waitlist conversion exceeds 5%.

Capital requested: $0.00. Time to first dollar: 14 days post-MVP launch. Expected margin: 90%. Build complexity: Low. Confidence: 0.73.

Differentiation: Fast, cheap, single-purpose contract risk review focused on freelance contracts rather than general legal documents or full-suite business management.

Major risks: Legal liability from bad advice, freelancer price sensitivity, and competition from full-suite tools adding similar features.

Research: Coverage 80%; sources 12; page fetches 7; provider qwen; search provider searxng; unverified category regulatory_platform_risks.

IC component scores: Demand Evidence 77; Time-to-First-Dollar Attractiveness 50; Capital Efficiency 82; Validation Affordability 42; Gross Margin Potential 85; Distribution Feasibility 42; Build Simplicity 42; Defensibility 50; Market Opportunity 62; Competitive Position 68; Risk Manageability 34; AI Leverage 78; Platformization Potential 82; Probability of Reaching $500 55.

Validation condition: Obtain 5 credible target-customer responses or 1 explicit willingness-to-pay signal before any build or further spend.

Kill criteria: No credible responses after 10 targeted compliant conversations/posts once outreach is approved; no willingness-to-pay signal at $49-$99; customers only want free advice rather than a paid report.

Rank 2: AI-Driven RFP Response Drafting Engine for Niche B2B SaaS

Portfolio score: 58.8. IC decision: CONDITIONAL_FUND. Composite score: 65.1. P($500/30d): 55.0%. Raw P($500/30d): 69.0%. Evidence ceiling: 55.0%. Initial tranche: $10.00. Evidence tier: TIER_1_PUBLIC_EVIDENCE. Generation source: qwen. Fallback: false.

Description: A high-margin AI-enabled service that parses complex RFP documents, extracts requirements, maps them to client product documentation and prior winning bids, and generates structured compliant draft responses. The human-in-the-loop model handles final quality while AI performs parsing, mapping, and initial drafting.

Problem: Mid-market B2B SaaS companies with 50-500 employees face sales-cycle bottlenecks from lengthy RFP processes. Manual RFP responses take 2-4 weeks, consume senior sales-engineering time, and create missed opportunities or reduced margins.

Target customer: VPs of Sales, Bid Managers, and Sales Engineers at B2B SaaS companies regularly responding to RFPs in categories such as cybersecurity, HR tech, and logistics software.

Proposed solution: An AI-agent workflow that ingests RFP PDF/Word files and a client knowledge base, extracts requirements, matches them to relevant content, and generates a structured draft with citations for client review.

Business model: Productized service with subscription path. Start with per-RFP fees for the first clients, then transition to monthly retainers for unlimited or priority drafts.

Pricing hypothesis: $500 per RFP draft for first 10 clients; long-term target of $2,000/month for 5-10 RFPs/month.

Acquisition strategy: Content-led case studies in LinkedIn and niche B2B SaaS communities, plus a free RFP Complexity Score tool. No cold outreach in V0.

Validation plan: Build a minimal workflow with existing Artifex compute, create a landing page and demo, share in 3-5 communities, target 5-10 qualified conversations, and close 1-2 paid pilots within 30 days.

Capital requested: $0.00. Time to first dollar: 14-21 days. Expected margin: 85-90%. Build complexity: Medium. Confidence: 0.73.

Differentiation: Privacy-first local inference, mid-market focus, and human-in-the-loop service quality rather than a generic chatbot or enterprise-only platform.

Major risks: AI hallucinations in compliance sections, resistance to AI-generated content, long B2B sales cycles, and competition from established RFP tools adding AI.

Research: Coverage 80%; sources 7; page fetches 3; provider qwen; search provider searxng; unverified category market_alternatives.

IC component scores: Demand Evidence 77; Time-to-First-Dollar Attractiveness 76; Capital Efficiency 82; Validation Affordability 42; Gross Margin Potential 85; Distribution Feasibility 60; Build Simplicity 42; Defensibility 60; Market Opportunity 62; Competitive Position 38; Risk Manageability 72; AI Leverage 78; Platformization Potential 82; Probability of Reaching $500 55.

Validation condition: Obtain 5 credible target-customer responses or 1 explicit willingness-to-pay signal before any build or further spend.

Kill criteria: No credible responses after 10 targeted compliant conversations/posts once outreach is approved; no willingness-to-pay signal at $49-$99; customers only want free advice rather than a paid report.

Rank 3: AI-Powered RFP Response Drafting Engine for Niche B2B SaaS

Portfolio score: 52.3. IC decision: REVISE_AND_RESUBMIT. Composite score: 60.2. P($500/30d): 47.0%. Raw P($500/30d): 47.0%. Evidence ceiling: 55.0%. Evidence tier: TIER_1_PUBLIC_EVIDENCE. Generation source: qwen. Fallback: false.

Description: A privacy-first RFP Copilot that connects to a company's product docs, case studies, and previous proposals, then parses uploaded RFP/RFQ requirements and generates a structured compliant draft. It focuses on repetitive security, compliance, and technical sections while highlighting strategic areas for human input.

Problem: Mid-market B2B SaaS companies spend 20-40 hours per RFP on manual drafting by senior sales engineers or product managers. Generic AI chatbots lack context and compliance rigor.

Target customer: VPs of Sales and Heads of Sales Operations at B2B SaaS companies with $5M-$50M ARR pursuing enterprise RFP deals.

Proposed solution: A RAG-based first-pass drafter over proprietary client data that outputs a Word/Docx file with tracked changes for human review.

Business model: Productized service transitioning to SaaS. Initially a flat monthly retainer for unlimited RFP drafts with human-in-the-loop review, later self-serve SaaS/API.

Pricing hypothesis: $1,500/month per sales-ops team seat.

Acquisition strategy: LinkedIn and RevOps community content around RFP efficiency. No cold outreach; inbound from pain-point content.

Validation plan: Build a landing page, publish LinkedIn posts targeting RevOps leaders, offer five free RFP audits using existing agents, and convert 1-2 leads to $500 pilot fees.

Capital requested: $0.00. Time to first dollar: 14-21 days. Expected margin: 90%+. Build complexity: Medium. Confidence: 0.52.

Differentiation: Privacy-first local inference, fast draft creation, lower cost than enterprise tools, and focus on drafting rather than full lifecycle RFP management.

Major risks: Data privacy concerns, hallucinations in compliance sections, and long sales cycles.

Research: Coverage 20%; sources 3; page fetches 3; provider qwen; search provider searxng; unverified categories pricing, customer_pain, market_alternatives, regulatory_platform_risks.

IC component scores: Demand Evidence 45; Time-to-First-Dollar Attractiveness 76; Capital Efficiency 82; Validation Affordability 78; Gross Margin Potential 85; Distribution Feasibility 60; Build Simplicity 42; Defensibility 52; Market Opportunity 44; Competitive Position 38; Risk Manageability 34; AI Leverage 78; Platformization Potential 82; Probability of Reaching $500 47.

Validation condition: Obtain 5 credible target-customer responses or 1 explicit willingness-to-pay signal before any build or further spend.

Kill criteria: No credible responses after 10 targeted compliant conversations/posts once outreach is approved; no willingness-to-pay signal at $49-$99; customers only want free advice rather than a paid report.

Rank 4: AI-Driven RFP Response Drafting Service

Portfolio score: 45.2. IC decision: REVISE_AND_RESUBMIT. Composite score: 54.5. P($500/30d): 35.0%. Raw P($500/30d): 43.0%. Evidence ceiling: 35.0%. Evidence tier: TIER_0_THESIS. Generation source: qwen. Fallback: false.

Description: A RFP Copilot service for B2B SaaS, IT services, and professional services companies. Clients upload an RFP and past winning proposals, and Artifex local inference extracts requirements, maps them to product features, and drafts a compliant response.

Problem: Mid-market B2B companies spend 20-40 hours per RFP, often with junior staff, causing missed deadlines, generic answers, and lost deals.

Target customer: VPs of Sales, Proposal Managers, and Sales Engineers at B2B SaaS and IT services companies with $5M-$50M ARR.

Proposed solution: Client uploads RFP and prior proposals, AI parses requirements and drafts in client voice, then client receives a 90% complete draft within 24 hours with optional human review.

Business model: Productized per-RFP service transitioning to monthly retainer for teams with high RFP volume.

Pricing hypothesis: $500 per RFP draft or $2,000/month for 4 RFPs.

Acquisition strategy: Content-led case studies on reducing RFP time and referral partnerships with sales enablement consultants. No cold outreach in V0.

Validation plan: Create landing page, publish three LinkedIn posts, offer free RFP teardown, and target 5 qualified leads plus 1 paid pilot in 30 days.

Capital requested: $0.00. Time to first dollar: 14-21 days. Expected margin: 85%. Build complexity: Low-Medium. Confidence: 0.45.

Differentiation: Faster and cheaper than agencies or enterprise tools, specialized for RFP structure and compliance rather than generic AI chat.

Major risks: Hallucinations in compliance sections, low inbound traffic, and client resistance to AI-generated content.

Research: Coverage 0%; sources 0; page fetches 0; provider none; search provider none; all five research categories unverified.

IC component scores: Demand Evidence 25; Time-to-First-Dollar Attractiveness 76; Capital Efficiency 82; Validation Affordability 42; Gross Margin Potential 85; Distribution Feasibility 60; Build Simplicity 42; Defensibility 52; Market Opportunity 44; Competitive Position 38; Risk Manageability 34; AI Leverage 66; Platformization Potential 82; Probability of Reaching $500 35.

Validation condition: Obtain 5 credible target-customer responses or 1 explicit willingness-to-pay signal before any build or further spend.

Kill criteria: No credible responses after 10 targeted compliant conversations/posts once outreach is approved; no willingness-to-pay signal at $49-$99; customers only want free advice rather than a paid report.

Rank 5: Churn-Proof: AI-Driven Retention Playbook Generator for SaaS

Portfolio score: 45.1. IC decision: REVISE_AND_RESUBMIT. Composite score: 55.4. P($500/30d): 35.0%. Raw P($500/30d): 43.0%. Evidence ceiling: 35.0%. Evidence tier: TIER_0_THESIS. Generation source: qwen. Fallback: false.

Description: An AI-enabled service that helps SaaS companies reduce churn by analyzing behavior patterns such as login frequency, feature adoption, and support tickets, then generating data-backed retention email copy and segmentation strategies. V0 is a service delivered with Artifex agents rather than a full SaaS platform.

Problem: Early-stage SaaS companies struggle with churn but lack data science resources to build retention models. They often rely on generic email blasts that fail to address usage gaps and pain points.

Target customer: Founders and Heads of Growth at B2B SaaS companies with 10-100 employees and $50k-$500k MRR.

Proposed solution: A Retention Sprint where the client provides anonymized usage data and churn metrics. Artifex agents identify at-risk segments and generate a three-part email sequence, subject lines, body copy, CTA recommendations, and segmented CSV output.

Business model: Service-as-a-product flat-fee Retention Sprint with future subscription Churn Monitor.

Pricing hypothesis: $500 per Retention Sprint.

Acquisition strategy: Publish a synthetic-data case study on LinkedIn/Product Hunt and offer free Churn Health Checks to attract SaaS founders organically. No cold outreach.

Validation plan: Create a synthetic SaaS dataset, run agents to generate a retention playbook, publish the methodology as a clearly labeled V0 simulation, monitor engagement, and offer a first Retention Sprint to a willing participant.

Capital requested: $50.00. Time to first dollar: 14-21 days. Expected margin: 95%. Build complexity: Low. Confidence: 0.0.

Differentiation: Focuses on the why behind churn, not generic marketing copy. AI-native workflow reduces delivery cost and time-to-value.

Major risks: Data privacy concerns, AI hallucination in generated copy, and market saturation from generic AI marketing tools.

Research: Coverage 0%; sources 0; page fetches 0; provider none; search provider none; all five research categories unverified.

IC component scores: Demand Evidence 25; Time-to-First-Dollar Attractiveness 76; Capital Efficiency 82; Validation Affordability 42; Gross Margin Potential 85; Distribution Feasibility 60; Build Simplicity 42; Defensibility 52; Market Opportunity 44; Competitive Position 38; Risk Manageability 34; AI Leverage 78; Platformization Potential 82; Probability of Reaching $500 35.

Validation condition: Obtain 5 credible target-customer responses or 1 explicit willingness-to-pay signal before any build or further spend.

Kill criteria: No credible responses after 10 targeted compliant conversations/posts once outreach is approved; no willingness-to-pay signal at $49-$99; customers only want free advice rather than a paid report.

Rank 6: Churn-Proof: AI-Driven Customer Retention Playbook Generator

Portfolio score: 43.2. IC decision: REVISE_AND_RESUBMIT. Composite score: 54.5. P($500/30d): 35.0%. Raw P($500/30d): 43.0%. Evidence ceiling: 35.0%. Evidence tier: TIER_0_THESIS. Generation source: qwen. Fallback: false.

Description: An AI-enabled service that analyzes SaaS customer health data including NPS, usage logs, and support tickets to identify churn risks and generate a customer success playbook. It turns weeks of manual analysis into a 24-hour deliverable.

Problem: SMB SaaS companies with 10-50 employees have churn problems but lack resources to identify at-risk segments. Enterprise tools are too expensive and consulting decks are too generic.

Target customer: Customer Success Managers and founders at B2B SaaS companies with 10-50 employees and $1M-$5M ARR.

Proposed solution: Clients upload anonymized CRM/support CSVs. Artifex agents cluster customers, identify churn predictors, and generate a PDF Retention Playbook with outreach scripts, feature recommendations, and pricing adjustments, plus a 15-minute review call.

Business model: Productized service with potential monthly monitoring or implementation support upsells.

Pricing hypothesis: $500 per playbook.

Acquisition strategy: Publish anonymized case studies on LinkedIn/Product Hunt and engage SaaS communities by offering free mini-audits for testimonials. No cold outbound.

Validation plan: Build a demo agent using public data, create a landing page with Request a Sample CTA, share the sample in three SaaS communities, measure call/pre-order conversion, and target one pre-order or three strong intent signals.

Capital requested: $0.00. Time to first dollar: 14-21 days. Expected margin: 90%+. Build complexity: Low. Confidence: 0.45.

Differentiation: Specific, data-driven, fast playbooks that are more actionable than generic dashboards or consulting reports.

Major risks: Data privacy concerns, weak sample-playbook conversion, and competition from generic AI chatbots.

Research: Coverage 0%; sources 0; page fetches 0; provider none; search provider none; all five research categories unverified.

IC component scores: Demand Evidence 25; Time-to-First-Dollar Attractiveness 76; Capital Efficiency 82; Validation Affordability 42; Gross Margin Potential 85; Distribution Feasibility 60; Build Simplicity 42; Defensibility 52; Market Opportunity 44; Competitive Position 38; Risk Manageability 34; AI Leverage 66; Platformization Potential 82; Probability of Reaching $500 35.

Validation condition: Obtain 5 credible target-customer responses or 1 explicit willingness-to-pay signal before any build or further spend.

Kill criteria: No credible responses after 10 targeted compliant conversations/posts once outreach is approved; no willingness-to-pay signal at $49-$99; customers only want free advice rather than a paid report.

Rank 7: AI-Driven RFP Response Drafting Engine for Niche B2B SaaS

Portfolio score: 40.5. IC decision: REVISE_AND_RESUBMIT. Composite score: 50.6. P($500/30d): 35.0%. Raw P($500/30d): 40.0%. Evidence ceiling: 35.0%. Evidence tier: TIER_0_THESIS. Generation source: qwen. Fallback: false.

Description: A RFP Copilot for B2B SaaS companies selling to enterprise or mid-market clients. The system ingests product docs, security whitepapers, and previous proposals, then maps new RFP requirements to existing content and drafts responses.

Problem: Mid-market B2B SaaS companies lose deals or waste engineering hours on RFPs. Sales engineers spend 10-20 hours copying, pasting, and editing responses, while generic AI lacks product and compliance context.

Target customer: Sales Engineers and Heads of Sales at B2B SaaS companies with $5M-$50M ARR pursuing deals requiring formal RFPs.

Proposed solution: A web interface for uploading a knowledge base and new RFPs. A RAG pipeline with local inference generates structured response documents with citations and confidence scores per section.

Business model: SaaS subscription with freemium tier and one-time setup fee.

Pricing hypothesis: Free for 1 RFP/month; Pro at $299/month; setup at $500.

Acquisition strategy: Content-led case studies, niche SaaS sales communities, a free RFP Audit lead magnet, and partnerships with sales enablement consultants.

Validation plan: Build MVP in 5 days, launch waitlist/demo page, use $50 for highly targeted LinkedIn ads, book 10 qualified demos, run manual agent-assisted demos, and close 1-2 discounted pilots.

Capital requested: $50.00. Time to first dollar: 14 days. Expected margin: 85%. Build complexity: Medium. Confidence: 0.45.

Differentiation: Privacy via local inference, specialized workflow rather than chatbot, SaaS-specific tuning, and RAG constraints for lower hallucination.

Major risks: Hallucinations in compliance sections, competition from larger RFP platforms adding AI, and adoption friction if setup is complex.

Research: Coverage 0%; sources 0; page fetches 0; provider none; search provider none; all five research categories unverified.

IC component scores: Demand Evidence 25; Time-to-First-Dollar Attractiveness 50; Capital Efficiency 82; Validation Affordability 42; Gross Margin Potential 85; Distribution Feasibility 42; Build Simplicity 42; Defensibility 42; Market Opportunity 44; Competitive Position 38; Risk Manageability 34; AI Leverage 78; Platformization Potential 70; Probability of Reaching $500 35.

Validation condition: Obtain 5 credible target-customer responses or 1 explicit willingness-to-pay signal before any build or further spend.

Kill criteria: No credible responses after 10 targeted compliant conversations/posts once outreach is approved; no willingness-to-pay signal at $49-$99; customers only want free advice rather than a paid report.

Rank 8: Churn-Proof: AI-Powered Churn Risk Scoring for SaaS

Portfolio score: 38.6. IC decision: REVISE_AND_RESUBMIT. Composite score: 53.6. P($500/30d): 35.0%. Raw P($500/30d): 43.0%. Evidence ceiling: 35.0%. Evidence tier: TIER_0_THESIS. Generation source: qwen. Fallback: false.

Description: An AI-enabled service that analyzes product usage logs, support tickets, and billing history to score churn probability and generate retention actions for at-risk accounts.

Problem: SaaS companies lose revenue to churn but lack data infrastructure or data science talent. Existing tools are too expensive or too dashboard-centric to produce actionable account-level insights.

Target customer: B2B SaaS companies with 10-100 employees and $100k-$1M ARR that have data but no dedicated data science team.

Proposed solution: A one-time Churn Risk Diagnostic where clients provide anonymized data, Artifex agents process it locally, train a lightweight churn model, and generate a PDF report with the top 20 at-risk accounts and specific retention actions within 48 hours.

Business model: Productized service with future subscription monitoring dashboard.

Pricing hypothesis: $500 per diagnostic report.

Acquisition strategy: Content-led case studies using synthetic or public data, LinkedIn/Product Hunt posting, SaaS communities, and free mini-audits of available public data. No cold outreach.

Validation plan: Build a synthetic-data demo dashboard, create a landing page with Book a Demo CTA, share in SaaS communities, and use $50 for a small LinkedIn ad boost targeting SaaS founders.

Capital requested: $50.00. Time to first dollar: 14-21 days. Expected margin: 90%. Build complexity: Low. Confidence: 0.45.

Differentiation: Actionable AI-generated retention playbooks, local inference, and analysis of unstructured sources such as support tickets.

Major risks: Data privacy concerns, competition from BI tools, and first-customer acquisition difficulty.

Research: Coverage 0%; sources 0; page fetches 0; provider none; search provider none; all five research categories unverified.

IC component scores: Demand Evidence 25; Time-to-First-Dollar Attractiveness 76; Capital Efficiency 82; Validation Affordability 42; Gross Margin Potential 85; Distribution Feasibility 60; Build Simplicity 42; Defensibility 52; Market Opportunity 44; Competitive Position 38; Risk Manageability 34; AI Leverage 66; Platformization Potential 70; Probability of Reaching $500 35.

Validation condition: Obtain 5 credible target-customer responses or 1 explicit willingness-to-pay signal before any build or further spend.

Kill criteria: No credible responses after 10 targeted compliant conversations/posts once outreach is approved; no willingness-to-pay signal at $49-$99; customers only want free advice rather than a paid report.

Rank 9: Churn-Proof: AI-Powered Churn Prediction & Save-Play Generator for SaaS

Portfolio score: 37.7. IC decision: REVISE_AND_RESUBMIT. Composite score: 53.2. P($500/30d): 35.0%. Raw P($500/30d): 43.0%. Evidence ceiling: 35.0%. Evidence tier: TIER_0_THESIS. Generation source: qwen. Fallback: false.

Description: An AI-enabled service that connects to product analytics or CRM exports to identify at-risk accounts from usage decay, support sentiment, and payment behavior, then generates personalized save plays.

Problem: SaaS companies lose 5-10% of MRR monthly to churn, and Customer Success teams often react after cancellation intent rather than proactively saving accounts.

Target customer: B2B SaaS companies with 10-100 employees, especially Customer Success Managers and Heads of CS responsible for retention.

Proposed solution: A weekly subscription service where clients provide anonymized usage/CRM data and Artifex agents flag top at-risk accounts with email drafts, in-app messages, or call scripts tailored to user behavior.

Business model: Productized monthly retainer for Churn Risk Reports and save-play generation. No software license initially.

Pricing hypothesis: $500/month per client.

Acquisition strategy: Targeted high-signal outreach to CS managers who recently posted about churn challenges, offering a free Churn Risk Snapshot in exchange for a 15-minute call. Positioned as personalized value-first outreach, not spam.

Validation plan: Build a data ingestion script and prompt chain, identify 20 SaaS CS managers, send 5 personalized messages, run 1-2 free snapshots, present the $500/month offer, close 1 client, and invoice after first report.

Capital requested: $0.00. Time to first dollar: 14-21 days. Expected margin: 95%. Build complexity: Low. Confidence: 0.45.

Differentiation: Lightweight, high-touch service with immediate actionable value and no 6-month implementation; AI handles data analysis and copywriting.

Major risks: Data privacy concerns, low conversion from free snapshot to paid, and client churn.

Research: Coverage 0%; sources 0; page fetches 0; provider none; search provider none; all five research categories unverified.

IC component scores: Demand Evidence 25; Time-to-First-Dollar Attractiveness 76; Capital Efficiency 82; Validation Affordability 42; Gross Margin Potential 85; Distribution Feasibility 60; Build Simplicity 42; Defensibility 52; Market Opportunity 44; Competitive Position 38; Risk Manageability 34; AI Leverage 60; Platformization Potential 70; Probability of Reaching $500 35.

Validation condition: Obtain 5 credible target-customer responses or 1 explicit willingness-to-pay signal before any build or further spend.

Kill criteria: No credible responses after 10 targeted compliant conversations/posts once outreach is approved; no willingness-to-pay signal at $49-$99; customers only want free advice rather than a paid report.

Rank 10: Churn-Proof: AI-Driven Retention Playbook Generator for SaaS

Portfolio score: 35.5. IC decision: REVISE_AND_RESUBMIT. Composite score: 52.0. P($500/30d): 35.0%. Raw P($500/30d): 43.0%. Evidence ceiling: 35.0%. Evidence tier: TIER_0_THESIS. Generation source: qwen. Fallback: false.

Description: An AI-enabled service that analyzes customer usage logs, support tickets, and billing history to identify at-risk accounts and generate a Retention Playbook for CSMs. V0 is a productized service with AI doing most analysis and drafting, reviewed by Artifex.

Problem: SaaS companies have churn data but not enough time to produce personalized retention strategies. CSMs rely on gut feeling and generic templates.

Target customer: B2B SaaS companies with 10-100 employees and a dedicated Customer Success team, especially users of Intercom, Zendesk, Mixpanel, or Amplitude.

Proposed solution: Clients provide anonymized usage/support data. Artifex agents identify at-risk segments and produce a concise document with specific scripts and feature recommendations for the top 10 at-risk accounts.

Business model: One-time Retention Audit & Playbook package with low marginal cost from AI-assisted analysis.

Pricing hypothesis: $500 per playbook.

Acquisition strategy: Content-led case study on LinkedIn/Product Hunt showing AI-identified churn risk, plus SaaS community engagement and free mini-audits for testimonials. No cold outreach.

Validation plan: Build a demo playbook using public or synthetic SaaS data, launch a landing page with Book a Demo CTA, post in LinkedIn and SaaS subreddits, target 5-10 inbound leads, and close 1-2 $500 sales.

Capital requested: $50.00. Time to first dollar: 14 days. Expected margin: 95%. Build complexity: Low. Confidence: 0.45.

Differentiation: Most churn tools show who is churning; this tells CSMs how to save them with specific scripts and strategies.

Major risks: Data privacy concerns and difficulty building initial trust.

Research: Coverage 0%; sources 0; page fetches 0; provider none; search provider none; all five research categories unverified.

IC component scores: Demand Evidence 25; Time-to-First-Dollar Attractiveness 76; Capital Efficiency 82; Validation Affordability 42; Gross Margin Potential 85; Distribution Feasibility 60; Build Simplicity 42; Defensibility 52; Market Opportunity 44; Competitive Position 38; Risk Manageability 34; AI Leverage 63; Platformization Potential 50; Probability of Reaching $500 35.

Validation condition: Obtain 5 credible target-customer responses or 1 explicit willingness-to-pay signal before any build or further spend.

Kill criteria: No credible responses after 10 targeted compliant conversations/posts once outreach is approved; no willingness-to-pay signal at $49-$99; customers only want free advice rather than a paid report.

Post-Run Health

• Qwen: AVAILABLE
• GPT-5.6 Sol: AVAILABLE
• GPT-5.6 Terra: AVAILABLE
• GPT-5.6 Luna: AVAILABLE
• SearXNG Search: AVAILABLE

Notes

• Qwen-only generation at concurrency 2 completed without deterministic fallback.
• vLLM logs showed no crash during the run.
• The top 3 still includes a near-duplicate RFP idea, so the next quality fix should be portfolio de-duplication or reranking before treating top 3 as final.