# Galen pre-seed diligence questions

Pre-seed investor workspace: https://preseed.usegalen.com (live mirror: https://www.usegalen.com/preseed)
Pre-seed public Markdown packet: https://preseed.usegalen.com/llms.txt (live mirror: https://www.usegalen.com/preseed/llms.txt)
Pre-seed structured JSON brief: https://preseed.usegalen.com/investor-brief.json (live mirror: https://www.usegalen.com/preseed/investor-brief.json)
Pre-seed AI diligence prompt: https://preseed.usegalen.com/ai-diligence-prompt.txt (live mirror: https://www.usegalen.com/preseed/ai-diligence-prompt.txt)
Pre-seed concise partner memo: https://preseed.usegalen.com/partner-memo.md (live mirror: https://www.usegalen.com/preseed/partner-memo.md)
Pre-seed public proof ledger: https://preseed.usegalen.com/proof-ledger.md (live mirror: https://www.usegalen.com/preseed/proof-ledger.md)
Pre-seed first-call diligence questions: https://preseed.usegalen.com/diligence-questions.md (live mirror: https://www.usegalen.com/preseed/diligence-questions.md)
Pre-seed forwardable intro email: https://preseed.usegalen.com/intro-email.txt (live mirror: https://www.usegalen.com/preseed/intro-email.txt)
Private packet request: https://preseed.usegalen.com#data-room

Use this public-safe checklist to prepare a first founder call, partner discussion, or AI-assisted diligence pass. The questions deliberately separate public evidence from private proof files and round materials.

Round snapshot:
A pre-seed round built around prospective biology. The financing advances one investor-underwritable claim: Galen can predict, choose, measure, and learn from real human-cell interventions before results are known.

## Meeting decision

Decide whether the public case is strong enough to justify a founder conversation.

### Questions
- Do we believe intervention prediction and experiment selection are the valuable layer above biological maps?
- Which part of Galen's public proof most changes our willingness to take the meeting?
- Which uncertainty should the first founder call resolve first: technical proof, commercial wedge, capital plan, or team fit?

### Private packet focus
- A pre-seed round built around prospective biology: The financing advances one investor-underwritable claim: Galen can predict, choose, measure, and learn from real human-cell interventions before results are known.
- Pre-seed diligence path: The public workspace gives investors a complete first-pass view before requesting private proof files, round materials, and founder follow-up.
- 18-month seed proof package: Capital funds the human-cell learning loop, model-selected experiments, scientific software, and partner-ready intervention-ranking workflow.
- AI-for-science platform conviction: Best-fit investors can help with biology leadership, design-partner access, wet-lab judgment, and seed-stage company building.
- Terms, budget, and exact proof files: Approved investors can review use-of-proceeds detail, round materials, baselines, controls, validation contracts, and protocol-level notes.
- Atlases and foundation models make biology legible: Galen uses that legibility to predict how the state changes under intervention and how much confidence each recommendation deserves.
- Wet-lab execution turns predictions into measured learning: Galen chooses the experiments with the highest learning value, records predictions before results, and turns each measurement into model improvement.
- Discovery workflow tools organize decisions: Galen returns a ranked intervention plan, expected response, confidence, and the smallest useful validation set.
- A focused wedge compounds into programmable biology: Galen starts with causal intervention ranking and expands the same machinery into predict, design, and verify capabilities as evidence accumulates.
- Intervention prediction is the valuable virtual-cell layer: Commercial wedge, design-partner workflow, and round materials.
- Causal structure improves prediction before results are known: Setup, baselines, ablations, intervals, leakage checks, controls, and exclusions.
- Pre-seed capital buys a seed-ready proof package: Budget, hiring sequence, validation contract, and financing materials.
- The right investor can increase the slope of the proof: Partner pipeline, technical diligence priorities, and round participation.

## Technical proof

Separate what the public evidence supports from what the private proof packet must establish.

### Questions
- What does the current causal-recovery evidence prove, and where is the claim boundary?
- Which baselines, leakage checks, ablations, confidence intervals, and control outcomes should we inspect before forming a technical view?
- What would convince us that causal structure improves prediction before results are known?

### Private packet focus
- Hidden mechanism support recovered in the strongest public summary tests: Exact setup, baselines, confidence intervals, ablations, and leakage checks are in the approved proof packet.
- Control settings behaved as expected: Approved investors can review the control settings, control outcomes, and claim-boundary notes.
- The experiment-selection software is already part of the system: The packet includes the active-design verdict, comparison policies, and the real-lab scoring plan.
- The round funds predictions committed before results: Approved investors receive the validation contract, use-of-proceeds detail, and protocol-level exclusions.

## Prospective proof plan

Evaluate whether the pre-seed plan creates a seed-ready proof package.

### Questions
- Are the predictions, comparison models, scoring rules, and data-quality gates fixed before the human-cell results are known?
- What is the minimum prospective result that makes the seed story credible?
- How will the second experiment queue show that Galen learns more per experiment than a same-budget alternative?

### Private packet focus
- Months 0-3: Investor evidence pack, first partner workflow, scoring plan fixed before results, candidate experiment queue, and clear data-quality gates.
- Months 3-6: Repeatable path from planned cell intervention to measured outcome, quality checks, data provenance, first lab-run plan, and a qualified partner workflow.
- Months 6-9: One cycle where the model predicts, the lab measures, the model updates, and the comparison plan is fixed before results are known.
- Months 9-12: Same-budget comparison, second experiment queue, and one paid or formally committed partner validation path if timing permits.
- Months 12-15: Shared intervention data across cell states or donors, partner validation underway, and previously unseen context evaluation if the data supports it.
- Months 15-18: Previously unseen result scored after predictions are committed, model-selection result, repeatable partner package, and evidence-based scale plan.

## Commercial wedge

Test whether the first product maps to a buyer's existing budget and urgent decision.

### Questions
- Which discovery teams already pay to rank targets, perturbations, donors, states, combinations, or follow-up experiments?
- What does the first paid prioritization program deliver, and how does it expand into recurring virtual-cell workflow access?
- What design-partner introduction would most increase the slope of Galen's proof?

### Private packet focus
- Teams with expensive intervention choices: Functional-genomics, immunology, target-discovery, and cell-engineering groups already spend budget deciding which targets, perturbations, donors, states, and readouts deserve reality.
- A paid prioritization program with a validation plan: A customer brings a candidate set and a consequential biological decision. Galen returns ranked interventions, expected response, confidence, explicit abstentions, and the smallest useful follow-up experiment set.
- Each deployment improves the same virtual-cell asset: The wedge begins as bounded causal triage. As prospective reliability broadens, Galen can expand into recurring workflow access and programmable predict, design, and verify interfaces.

## Capital to proof

Confirm that every use of proceeds maps to a milestone investors can underwrite.

### Questions
- Which hires, contracts, and build milestones are required for the prospective human-cell loop?
- What should be true at month 18 for the next financing to be evidence-based rather than narrative-based?
- Which milestone would make us want to lead, follow, or pass at seed?

### Private packet focus
- Forward-looking cell proof: Run the first real human-cell learning loop with predictions, comparison models, and scoring rules committed before results are known.
- Experiment-selection engine: Improve the causal model, confidence estimates, and experiment-selection software around the first validation task.
- Partner-ready package: Turn the proof loop into a repeatable intervention-ranking workflow: intake, report, scoring, and next-experiment recommendation.
- Reusable scientific system: Build the data provenance, quality standards, and lab-operating practices that make each measured result useful for the next model.

## Investor fit and access path

Clarify whether the investor can help Galen beyond capital and what diligence path they should request.

### Questions
- Can we help with senior biology hiring, design-partner access, wet-lab execution judgment, or seed-stage storytelling?
- Which packet do we need next: proof files, technical diligence, round materials, commercial wedge detail, or founder follow-up?
- What would we ask Galen to send before a partner meeting?

### Private packet focus
- AI-for-science and bio-platform conviction: The best-fit investor already believes virtual-cell companies can be large and wants a precise view of Galen's wedge, proof stack, and next falsifiable milestone.
- Capital plus scientific and commercial leverage: The most helpful investors can support senior biology hiring, design-partner introductions, wet-lab execution judgment, and later seed-stage storytelling.
- A seed-ready proof package: The pre-seed round is designed to produce prospective prediction evidence, measured learning efficiency, and a repeatable partner package.
- Request review: We review the request, firm context, and stated diligence need before sharing private proof files or round materials.
- Matched packet: Approved investors receive the right package for their intent: proof files, technical diligence, round materials, design-partner detail, or founder follow-up.
- Founder diligence: The first conversation focuses on the current proof boundary, the human-cell validation plan, model-selected experiments, and seed-readiness evidence.
- Technical proof packet: Technical diligence. Investors underwriting model quality, comparison plans, controls, and scientific validity.
- Round materials packet: Round materials. Leads and serious followers evaluating ownership, budget, hiring sequence, and seed-readiness plan.
- Commercial wedge packet: Design partner. Investors helping with customer access, buyer workflow diligence, or first design-partner introductions.
- Founder conversation packet: Founder conversation. Investors ready to pressure-test the current proof boundary, next experiment, and founder-market fit.
- Evidence packet: Full proof summaries, model-card excerpts, verdict interpretation, baselines, control logic, and claim boundaries.
- Use-of-proceeds and milestone plan: Hiring priorities, build sequence, validation contract, capital allocation by proof milestone, and the expected seed-stage evidence package.
- Commercial wedge materials: First buyer profile, design-partner workflow, first paid deployment shape, and the path from bounded causal triage to recurring virtual-cell access.
- Founder follow-up: A focused diligence conversation around the current proof, the next prospective experiment, and the investor's specific concerns.

## Public FAQ anchors

### What is a virtual cell?
A virtual cell is a computational model intended to predict how a living cell responds when biology is changed, such as suppressing a gene, adding a molecule, shifting state, or testing a combination.

### What is Galen's first product?
The first product helps biology teams rank which targets, interventions, or follow-up experiments deserve priority lab budget.

### What has Galen proven?
Galen has shown, in controlled realistic tests, that its model can recover masked cause-and-effect structure and use it to predict previously unseen cell responses better than matched comparison models. Galen has also built software that selects experiments expected to teach the model more.

### What is Galen proving next?
Galen's next proof is a real human-cell learning loop: predictions recorded before results, model-selected experiments, measured outcomes, and a second round of experiments chosen from what the model learns.

### Why primary CD4+ T cells first?
CD4+ T cells are a central human immune-cell type. They connect Galen's modeling work to commercially legible questions in immunology, inflammatory disease, oncology, and cell engineering: which interventions improve function, persistence, activation, or dysfunctional state, and how confident is the model in each recommendation?

### Is Galen a wet-lab company or a software company?
Galen is a virtual-cell company. The model is the compounding product, the wet lab is the learning engine, and software is the interface through which predictions, prioritization, and experiment selection become useful.

### What is Galen's differentiated virtual-cell thesis?
Galen's thesis is that intervention prediction needs causal structure, uncertainty estimates, and proof before results are known. Large datasets and foundation models validate the category, and Galen differentiates through focused real-world performance, calibrated confidence, learning efficiency, and customer workflow integration.

### What does the pre-seed round fund?
The round funds the first real human-cell learning loop, model-selected experiments, scientific software and data infrastructure, and the first paid partner validation.

## Ground rules
- Use the public page and artifacts for meeting preparation.
- Request the private proof packet before asking for exact protocol details, baselines, intervals, budget, round terms, or proprietary implementation details.
- Treat uncertain points as diligence questions, not assumptions.