🚀 How to prepare for a fundraise

3 tactics to prepare for a fundraise

📖 Start with your story, then the numbers

• Lock in the narrative of the fundraise:

  • Why this amount? 

  • Why now? 

  • What are the funds for?

  • What return should they unlock?

• Make the numbers support the narrative around GTM motion, market headwinds and tailwinds, and use of proceeds

  • Clearly account for the ramp-up time around headcount numbers, pipeline assumptions, and projected sales

  • E.g. Customers are loving the product with 100% retention and an expanding waitlist. Founder-led sales can’t keep up. Raising $1.5M to hire 4 AEs with expected $3M in incremental revenue over 12 months, accounting for  ramp-up costs, and sales enablement tools

🧪 Determine assumptions and stress-test the narrative

• Create a single tracker for all assumptions:

  • List down all the assumptions you make for the numbers that support your narrative to hold

  • Define what informs the assumptions (historicals, primary research, benchmarks, third-party data) and how each assumption impacts the numbers

• Default to being credible; the goal is to reduce suspicion

• Pre-mortem the 3 highest-risk assumptions (e.g. launch delays, churn spikes, pricing pressure) and run scenarios and sensitivity analysis:

  • Understand how each variable affects the outcome

  • How many months of slippage are sustainable?

  • How much buffer do you have?

• Prepare areas to adjust in case assumptions do not hold (e.g. change hiring pace, CAC limits, product scope cuts or segment focus)

• Ask a friendly investor to pressure-test your assumptions: 

  • What are they googling to double-check? 

  • Does anything trigger a yellow or red flag? 

• Refine assumptions and/or back them up so the narrative addresses how a compelling and defensible ROI can be delivered

🛠 Choreograph the investor experience with tight operations

• Put together a data room with an index and intuitive foldering; 

  • Consistent formatted model with conditionally formatted error checks; 

  • Data room has a table of contents; every doc is versioned and final

  • “Read Me” tab that explains your model’s structure and key drivers

  • Present information so investors never ask, “Where is this?” or “Why is this off?”

  • Ensure strategic and technical reviews are complete, with narrative and model telling the same story

• Centralize every document, question, investor conversation and follow-up in one tracker

  • Address diligence questions and keep the data room current

  • Capture feedback patterns

2 traps to avoid

🚨 Resist creating multiple scenarios (base, bear, bull, etc) for investors

• Plan for but avoid showing contingency trees to investors as that creates more confusion

• Present one aggressive and credible case and assume you will raise the target amount

• Investors will trim numbers as they wish; do not pre-discount your own story

🚨 Avoid overconfidence and false precision

• Be candid about unknowns and invite collaboration with investors on how to de-risk them

• Treat investors like future teammates, not adversaries to outsmart

1 tool to leverage

📖 Best practice on preparing for the fundraise

• Have your data room prepared before officially kicking off your fundraise

• Treat it as a living document and add materials as investor questions arise

• Leverage tools like Visible, Dropbox or Google Drive to share, structure, and organize your data room

Bonus: 1 trend to spark startup ideas

📈 AI is reinventing chemical process innovation

• Chemical manufacturing faces overcapacity, soft demand, and margin pressure, while R&D cycles remain painfully slow (18–24 months)

• Legacy processes rely on PDFs, electronic lab notebooks (ELNs), and scattered plant data, creating knowledge bottlenecks and slowing optimization

• 3 converging trends that make the chemicals industry ripe for AI disruption:

  • Data is finally usable: Data that chem companies have been collecting for years is now machine-readable, accessible and labeled well enough

  • Models have leveled up: Retrosynthesis and reaction prediction work, and hybrid physics plus ML is now production-grade

  • Operational urgency is real: The energy transition, tariffs and supply shocks, and talent gaps are forcing faster cycles in R&D and go-to-market

• Generic large language models aren’t built to handle the realities of chemistry; process innovation will come from chemistry-native models that understand structure, constraints, and feedback

• Opportunities exist across the stack:

  • Closed-loop R&D: Automate literature mining → hypothesis generation → high-throughput experiments → model updates → scale-up, creating a flywheel of proprietary data and faster cycle times

  • AI-native operations: Integrate lab information management systems and maintenance data across plants, generate guidance on business impact, and optimize modular production for yield, energy, and equipment effectiveness

  • Commercial AI layers: Reduce SKU matching from months to minutes, intelligent matching on top of marketplaces, and live feedback for R&D