Customer discovery

Find the first painful workflow.

The goal is not to ask whether people like the idea. The goal is to discover where breeding teams currently suffer: cross planning, diversity preservation, genomic prediction workflow, or CRISPR/edit prioritization.

Target list

Segment	Who to find	Why
Plant breeders	Crop geneticists, quantitative genetics labs	Core user for cross decisions and long-term selection strategy.
Breeding data scientists	People building genomic selection workflows	Likely first champions; they already feel the gap between data and decisions.
Precision breeding / CRISPR	Plant gene-editing teams	Validate edit prioritization and edit-to-breeding strategy.
Agtech / seed startups	Founders, R&D leads	Faster feedback than large incumbents.
Microbial strain engineering	Directed evolution / fermentation teams	Possible second beachhead with short generation cycles.

Interview questions

How do you currently decide which crosses to make?
Do you simulate selection strategies across future generations?
How do you manage inbreeding and diversity loss?
Which tools do you use for genomic selection today?
What part of the workflow breaks most often?
What data do you usually have: genotype, phenotype, pedigree, environment?
Who owns the final breeding decision?
Would comparing aggressive vs diversity-preserving strategies be useful?
For CRISPR-enabled breeding: how do you choose edit targets and genetic backgrounds?
What demo would make this immediately credible?

Email template

Subject: Quick question on breeding decision tools

Hi [Name],

I’m working on an early concept called BreedOS — an AI decision engine for artificial selection and CRISPR-enabled breeding.

The core idea is to help breeding teams compare selection strategies across future generations, including genetic gain, inbreeding, allele-frequency drift, and diversity loss.

I’m not trying to sell anything. I’m doing early customer discovery and would be grateful for 15 minutes of your perspective.

The main question I’m exploring is:

How do breeding teams currently decide what to cross, what to keep, and how to manage long-term diversity while improving target traits?

Best,
[Your Name]

LinkedIn template

Hi [Name], I’m exploring a startup idea at the intersection of breeding, population genetics, CRISPR, and AI.

The idea is a decision engine that helps compare selection strategies across generations: genetic gain, inbreeding, drift, and diversity loss.

I’m not selling anything — just trying to understand current workflows. Would you be open to a 15-minute call?