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Welcome back, Alex. Here's your command center.
Daily Signal Highlights
Sarah Chen (ex-Stripe) launched Dataflow - matches your AI infrastructure thesis.
Surge in Climate Fintech startups (+27% MoM) - 12 new companies this week.
Competitor fund led $8M in Quantum Computing startup you passed on last quarter.
Tasks Due
Prepare technical assessment for partner meeting at 3PM.
Complete Q2 performance summary for Sequoia Capital.
Quarterly review with founding team - prepare GTM feedback.
Live Portfolio Updates
Burn rate increased 32% MoM. Runway reduced to 8 months.
Closed .2M enterprise deal with Fortune 500 client.
Hired new CTO from Google AI team. Starting next month.
Recent Activity
The Rise of AI Infrastructure Startups
Our analysis of the emerging AI stack and investment opportunities in 2023.
Market Insights
Funding Activity
Last 30 days vs previous period
$4.2B total
Portfolio Performance
Fund Performance
Current TVPI
+0.3x QoQ
Sourcing
Discover and track promising startups with AI-powered insights.
AI Suggested Startups
NeuralStack
AI Infrastructure
Building a serverless platform for AI model deployment with 10x faster inference.
Dataflow
Data Infrastructure
Real-time data processing platform with built-in ML capabilities for enterprise.
ClimateFinance
Climate Tech
Carbon credit marketplace with blockchain verification and AI-powered impact tracking.
Trending Sectors
AI Infrastructure
27 new startups this month
Climate Fintech
18 new startups this month
Healthcare AI
15 new startups this month
FounderGraph
Serial Founder Network
Discover connections between successful founders
Interactive founder network visualization
Recent Activity
Diligence
Comprehensive analysis tools for evaluating potential investments.
Active Diligence
NeuralStack
AI Infrastructure
Dataflow
Data Infrastructure
ClimateFinance
Climate Tech
NeuralStack Dossier
NeuralStack
AI Infrastructure • .2M raised • San Francisco, CA
Team Assessment
The NeuralStack founding team consists of three technical co-founders with strong backgrounds in AI infrastructure and distributed systems.
CEO David Chen (ex-Google AI) led infrastructure teams for large-scale ML training. CTO Sarah Kim (ex-NVIDIA) specialized in GPU optimization for deep learning. COO Michael Rodriguez (ex-AWS) brings enterprise go-to-market experience.
The team demonstrates strong technical expertise but may benefit from additional enterprise sales experience as they scale. Their previous collaborations at Google show proven ability to work together effectively.
Key Strengths
Areas for Development
Founding Team
David Chen
CEO, Co-founder
Ex-Google AI, 8+ years in ML infrastructure. MS in Computer Science from Stanford.
Sarah Kim
CTO, Co-founder
Ex-NVIDIA, 6+ years in GPU optimization. PhD in Computer Engineering from MIT.
Michael Rodriguez
COO, Co-founder
Ex-AWS, 5+ years in product management. MBA from Berkeley Haas.
Reference Checks
Recent Activity
Activity Timeline
Diligence Resources
NeuralStack Pitch Deck
PDF • 28 slides • Uploaded 5 days ago
Financial Model
XLSX • 5 sheets • Uploaded 4 days ago
Technical Architecture
PDF • 12 pages • Uploaded 3 days ago
Customer Interview - EnterpriseAI
MP3 • 42 min • Recorded yesterday
Dossier Generator
Upload Documents
Drag and drop files here
Supports PDF, PPTX, DOCX, XLSX (max 50MB)
company_pitch.pdf
financial_model.xlsx
Additional Information
Dossier Settings
Processing Time Estimate
Repository Scan
GitHub Repository URL
Scan Options
Recent Scans
NeuralStack/inference-engine
Scanned 2 days ago • 87/100 score
Dataflow/stream-processor
Scanned 5 days ago • 92/100 score
Repository scans help evaluate code quality, architecture, and development practices.
Portfolio
Monitor and support your portfolio companies with real-time insights.
Total Companies
Total Invested
Current TVPI
Avg. Runway
Portfolio Alerts
CloudSecure
Cybersecurity • Series A
Burn rate increased 32% MoM. Runway reduced to 8 months.
Dataflow
Data Infrastructure • Seed
Customer churn increased to 4.5% this month. Above target of 3%.
Portfolio Companies
Quantum AI
AI/ML • Series B
Runway
24 months
Burn Rate
$320K/mo
ARR
$4.2M
Team Size
42 people
Datalytics
Data Analytics • Series A
Runway
18 months
Burn Rate
80K/mo
ARR
$2.1M
Team Size
28 people
CloudSecure
Cybersecurity • Series A
Runway
8 months
Burn Rate
$420K/mo
ARR
.8M
Team Size
35 people
Portfolio Performance
Performance by Stage
TVPI by Investment Stage
Current multiple on invested capital
Overall Portfolio TVPI
Weighted average across all stages
Performance by Sector
TVPI by Industry
Current multiple on invested capital
Top Performing Sector
Highest TVPI in the portfolio
3.8x TVPI
Portfolio Benchmarking
Net Revenue Retention
Industry average: 113%
Burn Multiple
Industry average: 1.2x
CAC Payback
Industry average: 18 months
Recent Updates
Latest Investor Updates
Quantum AI - Q2 Update
2 days agoClosed .2M enterprise deal with Fortune 500 client. Hiring new CTO from Google AI team.
Datalytics - Q2 Update
5 days agoLaunched new product feature. ARR grew 15% QoQ. Expanding sales team in Europe.
CloudSecure - Q2 Update
1 week agoBurn rate increased due to expanded R&D. Planning strategic pivot to focus on enterprise.
Portfolio Milestones
Quantum AI Copilot
Copilot Assistant
Create Investor Update
Generated Update
Quantum AI - Monthly Investor Update
July 2023
Dear Investors,
I'm pleased to share our July update, which has been a pivotal month for Quantum AI with significant commercial and team developments.
Key Highlights:
- Closed a .2M enterprise deal with a Fortune 500 client (12-month contract with renewal options)
- Hired our new CTO, Dr. Sarah Chen, previously AI Research Lead at Google
- Achieved 15% MoM growth in ARR, now at $4.2M
- Maintained healthy burn rate at $320K/month with 24 months of runway
Financial Overview:
Our ARR now stands at $4.2M, representing 15% MoM growth and 112% YoY growth. We've maintained our burn rate at $320K/month, giving us a comfortable 24-month runway. Our net revenue retention is strong at 118%, indicating high customer satisfaction and expansion.
Team Updates:
We're thrilled to welcome Dr. Sarah Chen as our new CTO. Sarah brings invaluable experience from her role as AI Research Lead at Google, where she led a team of 15 engineers working on large language models. We've grown to 42 team members, with 3 new hires in engineering and 2 in sales this month.
Product Development:
We've released v2.3 of our platform with significant improvements to inference speed (40% faster) and model accuracy (12% improvement). Our product roadmap remains on track, with our next major release scheduled for September.
Challenges & Focus Areas:
While we're seeing strong enterprise traction, our self-serve product adoption is below target. We're implementing a revised onboarding flow and more intuitive UI based on user feedback, expected to launch next month.
Looking Ahead:
Our Q3 focus remains on enterprise sales expansion and improving self-serve conversion. We have a strong pipeline of potential enterprise deals and are confident in our ability to meet or exceed our annual targets.
As always, we appreciate your continued support and welcome any questions or feedback.
Best regards,
David Chen
CEO, Quantum AI
Feedback & Refinement
FundOps
Manage fund operations, capital calls, and LP reporting with AI assistance.
Fund Size
Deployed Capital
Current IRR
Current MOIC
Fund Performance
IRR & MOIC Trends
Quarterly Performance
IRR and MOIC over time
Interactive performance chart
Current IRR
22.4%
+2.1% QoQ
Current MOIC
2.4x
+0.3x QoQ
Current DPI
0.4x
+0.1x QoQ
Fund Allocation
Capital Allocation
By industry and stage
Interactive allocation chart
Capital Calls & Distributions
Recent Activity
| Type | Date | Amount | Status | Actions |
|---|---|---|---|---|
|
|
Jun 15, 2023 | $5,250,000 | Completed | |
|
|
May 22, 2023 | ,800,000 | Completed | |
|
|
Mar 10, 2023 | $7,500,000 | Completed | |
|
|
Jan 05, 2023 | $6,750,000 | Completed |
Capital Summary
Key Metrics
DPI
0.4x
RVPI
2.0x
TVPI
2.4x
PIC
64.7%
LP Reporting
Recent LP Updates
Q2 2023 LP Update
Jun 30, 2023Quarterly performance update with portfolio highlights and financial metrics.
Q1 2023 LP Update
Mar 31, 2023Quarterly performance update with portfolio highlights and financial metrics.
Q4 2022 LP Update
Dec 31, 2022Quarterly performance update with portfolio highlights and financial metrics.
LP Update Preview
Q2 2023 Highlights
- IRR increased to 22.4% (+2.1% QoQ)
- MOIC improved to 2.4x (+0.3x QoQ)
- Quantum AI closed .2M enterprise deal
- Distributed .8M to LPs from partial exit
Portfolio Snapshot
Companies
12 active
Deployed
$48.5M (64.7%)
Reserves
$26.5M (35.3%)
Distributions
9.4M (DPI: 0.4x)
Top Performers
Market Outlook
AI/ML sector continues to outperform with 42% YoY growth. We're seeing strong enterprise adoption across our portfolio. Valuations have stabilized after 2022 correction, creating attractive entry points for new investments.
Portfolio Risk Heatmap
Risk Assessment
Interactive risk heatmap visualization
High Risk (1)
8 months runway, burn rate increased 32% MoM
Medium Risk (2)
Customer churn increased to 4.5% this month
Regulatory challenges in EU market
Low Risk (9)
Strong growth, 24 months runway
Stable growth, 18 months runway
New Capital Call
Generate LP Update
Preview
VentureAmp Fund I - Q3 2023 Update
July 15, 2023
Dear Limited Partners,
We are pleased to share our Q3 2023 update for VentureAmp Fund I. The fund continues to perform well, with strong growth in our AI and data infrastructure portfolio companies.
Fund Performance Highlights
- Current IRR: 22.4% (+2.1% QoQ)
- Current MOIC: 2.4x (+0.3x QoQ)
- DPI: 0.4x (+0.1x QoQ)
- RVPI: 2.0x
- Total Value: 16.4M on $48.5M deployed
Portfolio Company Updates
Our portfolio continues to show strong performance, with several companies achieving significant milestones this quarter:
Quantum AI (4.2x MOIC) closed a .2M enterprise deal with a Fortune 500 client and hired a new CTO from Google's AI team. The company is on track to reach $5M ARR by year-end, representing 120% YoY growth.
Datalytics (2.8x MOIC) launched their new data processing platform and expanded their sales team in Europe. ARR grew 15% QoQ to $2.1M.
ClimateFinance (1.9x MOIC) secured partnerships with three major European banks for their carbon credit verification platform.
New Investments
We made no new investments this quarter as we focused on supporting our existing portfolio. We continue to evaluate opportunities in AI infrastructure and climate tech sectors.
Exits & Distributions
We distributed .8M to LPs from a partial exit of our position in TechSecure, representing a 3.2x return on our initial investment.
Market Outlook
The AI/ML sector continues to outperform with 42% YoY growth. We're seeing strong enterprise adoption across our portfolio. Valuations have stabilized after the 2022 correction, creating attractive entry points for new investments.
Team Updates
We welcomed Jane Smith as our new Platform Lead, focusing on AI and GTM support for our portfolio companies. Jane brings 10+ years of experience from Google and Stripe.
We appreciate your continued support and welcome any questions or feedback.
Best regards,
The VentureAmp Team
Copilots
AI-powered assistants to help your portfolio companies grow faster.
Total Copilots
Total Prompts
Usage This Month
Avg. Satisfaction
Portfolio Copilots
Quantum AI Copilot
AI Infrastructure • Series B
GTM
42%
Hiring
28%
Updates
30%
Datalytics Copilot
Data Analytics • Series A
GTM
35%
Hiring
45%
Updates
20%
CloudSecure Copilot
Cybersecurity • Series A
GTM
25%
Hiring
15%
Updates
60%
Copilot Assistants
GTM Assistant
Marketing, sales, and customer acquisition
Hiring Assistant
Recruitment, job descriptions, and interviews
Update Assistant
Investor updates and progress summaries
Prompt Library
| Prompt Name | Category | Created By | Usage | Rating | Actions |
|---|---|---|---|---|---|
|
ICP Email Sequence Generator Generate 5-email sequence for ideal customer profile |
GTM | Alex Morgan | 87 uses |
5.0
|
|
|
Senior Engineer Job Description Create compelling JD for senior engineering roles |
Hiring | Sarah Johnson | 64 uses |
4.8
|
|
|
Monthly Investor Update Template Structured update with metrics and milestones |
Updates | Michael Chen | 92 uses |
4.9
|
|
Usage Analytics
Usage by Assistant Type
Interactive usage analytics chart
Usage by Company
Total Usage
Last 30 days
+18% vs previous period
GTM Assistant
Create Marketing Content
Generated Email Sequence
Email 1: Introduction & Problem Statement
Subject: 80% Faster AI Deployment: A New Approach for [Company Name]
Dear [Name],
As [Company Name] scales its AI initiatives, is your team facing challenges with model deployment speed and inference costs?
Fortune 500 CTOs report that traditional ML infrastructure creates three critical bottlenecks:
- Weeks-long deployment cycles that delay time-to-market
- Excessive inference costs that scale unpredictably
- Integration complexity with existing enterprise systems
American Express recently faced similar challenges before reducing their ML deployment time from 3 weeks to just 4 days.
I'd like to share how they achieved this. Would a brief 15-minute call next week be helpful?
Best regards,
[Your Name]
Email 2: Value Proposition & Case Study
Subject: How [Similar Company] Cut AI Costs by 60% in 90 Days
Dear [Name],
Following up on my previous email about accelerating AI deployment.
A Fortune 100 financial services company (similar to [Company Name]) recently implemented our AI infrastructure platform with remarkable results:
- Reduced model deployment time from 3 weeks to 4 days (82% improvement)
- Cut inference costs by 63% in the first quarter
- Maintained enterprise-grade security with zero compliance issues
- Seamless integration with their existing ML toolchain
Their VP of Engineering noted: "This solution paid for itself in the first 60 days through cost savings alone, with the accelerated time-to-market creating even greater business value."
I've attached a brief case study with technical implementation details.
Would you be available for a 15-minute call this week to discuss how we might achieve similar results for [Company Name]?
Best regards,
[Your Name]
Email 3: Technical Deep Dive
Subject: Technical Architecture: How Our Solution Reduces ML Deployment Time by 80%
Dear [Name],
For engineering leaders focused on technical excellence, I wanted to share specifically how our platform achieves the 80% deployment acceleration and 60% cost reduction mentioned in my previous emails.
Our architecture includes three key innovations:
- Containerized Model Packaging: Automatic dependency resolution and environment configuration reduces deployment preparation from days to minutes.
- Dynamic Resource Allocation: ML-powered resource scaling that predicts usage patterns and pre-allocates only necessary computing resources.
- Enterprise Integration Layer: Pre-built connectors for all major enterprise systems with role-based access controls and audit logging.
We've prepared a technical whitepaper specifically for [Company Name] that includes implementation architecture and ROI calculations based on your scale.
Would you like me to send this over, or would you prefer a technical demo with our CTO?
Best regards,
[Your Name]
Feedback & Refinement
Add New Prompt
Resources
Access guides, templates, and best practices to support your portfolio companies.
PromptOps Libraries
AI prompt engineering guides and best practices
Templates
Term sheets, hiring rubrics, GTM calendars
Custom Content
Fund-specific resources and guides
Resource Library
| Resource Name | Category | Last Updated | Format | Actions |
|---|---|---|---|---|
|
Seed Round Term Sheet Template Standard terms for early-stage investments |
Templates | 2 weeks ago |
PDF
|
|
|
Advanced Prompt Engineering Guide Techniques for crafting effective AI prompts |
PromptOps | 3 days ago |
DOCX
|
|
|
GTM Strategy Framework Go-to-market planning template for B2B SaaS |
Templates | 1 week ago |
XLSX
|
|
|
How to Craft Your Seed Update Video guide for effective investor updates |
Custom Content | 1 month ago |
MP4
|
|
|
Technical Hiring Rubric Evaluation framework for engineering candidates |
Templates | 2 months ago |
DOCX
|
|
Recently Added
AI Prompt Templates for GTM
Collection of proven prompts for marketing, sales, and customer success teams.
Series A Due Diligence Checklist
Comprehensive checklist for Series A fundraising preparation and investor due diligence.
SaaS Metrics Dashboard
Excel template for tracking key SaaS metrics including MRR, churn, CAC, and LTV.
AI Strategy Workshop Recording
Recording of our recent workshop on implementing AI in your product strategy.
Most Popular
Top Templates
TemplatesTop PromptOps Resources
PromptOpsAdvanced Prompt Engineering Guide
Advanced Prompt Engineering Guide
This guide provides techniques for crafting effective AI prompts to get the most out of your AI assistants and copilots.
1. Be Specific and Clear
The more specific your prompt, the better the results. Include relevant details and context that help the AI understand exactly what you need.
Example:
Instead of: "Write an email."
Better: "Write a follow-up email to a potential enterprise customer who expressed interest in our AI infrastructure product during a demo last week but hasn't responded to my initial follow-up."
2. Use the Role-Context-Task Framework
Structure your prompts with these three elements:
- Role: The perspective or expertise you want the AI to adopt
- Context: Relevant background information
- Task: What you want the AI to do
Example:
"As a senior product marketing manager (role) for an AI infrastructure startup targeting enterprise customers who are struggling with ML model deployment times (context), create a one-page competitive analysis comparing our solution to the market leaders, highlighting our key differentiators (task)."
3. Use Examples (Few-Shot Learning)
Provide examples of the kind of output you want to guide the AI's response format and style.
Example:
"Write three LinkedIn post ideas for our new product launch. Each post should be 2-3 sentences, include a clear call-to-action, and use a professional but conversational tone. Here's an example of the style I'm looking for:
Excited to announce the launch of our new AI infrastructure platform that reduces ML deployment time by 80%! We've been working with beta customers for months to perfect this solution. DM me to learn how you can skip the waitlist."
4. Iterative Refinement
Don't expect perfect results on the first try. Use a conversational approach to refine outputs.
Example sequence:
- Initial prompt: "Write a cold email to a VP of Engineering about our AI infrastructure solution."
- Refinement: "That's too generic. Make it more specific to a VP of Engineering at a fintech company who's struggling with ML model deployment times."
- Further refinement: "Great, now make it shorter (max 150 words) and add a specific call-to-action for a 15-minute demo."
5. Advanced Techniques
Chain-of-Thought Prompting
Ask the AI to "think step by step" to solve complex problems or generate more thoughtful responses.
"Analyze whether our startup should prioritize feature A or feature B for the next quarter. Think step by step about the pros and cons of each, considering development time, customer impact, competitive advantage, and revenue potential."
Persona-Based Prompting
Ask the AI to adopt specific personas to get different perspectives.
"I need feedback on our product pricing strategy. Please provide feedback from three different perspectives: 1) A price-sensitive startup founder, 2) An enterprise procurement officer, and 3) A competitor's sales representative."
6. Common Pitfalls to Avoid
- Being too vague or ambiguous
- Providing contradictory instructions
- Overloading with too many requirements at once
- Not providing enough context
- Using jargon without explanation
7. Prompt Templates for Common Tasks
See the accompanying document for ready-to-use prompt templates for common business tasks like:
- Email sequences
- Marketing copy
- Product messaging
- Competitive analysis
- Customer interview questions
- Investor updates
For more advanced techniques and examples, refer to our PromptOps Library or schedule a session with our AI Strategy team.
Account Settings
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Profile Information
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Security
Two-Factor Authentication
Add an extra layer of security
Password
Last changed 30 days ago
Active Sessions
3 active sessions
AI Permissions
Control what data AI copilots can access
Portfolio Data
Company metrics, updates, financials
Deal Flow Data
Sourcing, diligence, pipeline
LP Communications
Reports, updates, presentations
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Connected
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Connected
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