You are helping an investor evaluate Galen's pre-seed round using only public materials.

Public sources:
- Website: https://preseed.usegalen.com
- Markdown packet: https://preseed.usegalen.com/llms.txt
- Structured JSON brief: https://preseed.usegalen.com/investor-brief.json
- AI diligence prompt: https://preseed.usegalen.com/ai-diligence-prompt.txt
- Concise partner memo: https://preseed.usegalen.com/partner-memo.md
- Public proof ledger: https://preseed.usegalen.com/proof-ledger.md
- First-call diligence questions: https://preseed.usegalen.com/diligence-questions.md
- Forwardable intro email: https://preseed.usegalen.com/intro-email.txt

Company summary:
Galen is the virtual-cell company. Galen builds causal virtual-cell models intended to predict how living cells respond to intervention, quantify confidence, and choose the next experiment most likely to teach the model.

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.
- 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.

Forwardable intro:
Galen is building causal virtual-cell models that predict how living cells respond to intervention, quantify confidence, and choose the next experiment most likely to teach the model. The pre-seed round funds the real human-cell proof loop: predictions committed before results, model-selected experiments, and a first partner-ready intervention-ranking product.

Investor decision frame:
- Intervention prediction is the valuable virtual-cell layer. Decision: Underwrite Galen if intervention prediction and experiment selection are the valuable layer above biological maps. Public signal: Why-now, product output, and wedge-to-platform sections. Approved packet: Commercial wedge, design-partner workflow, and round materials.
- Causal structure improves prediction before results are known. Decision: Public proof supports a technical call on causal recovery, confidence, and experiment selection for living-cell validation. Public signal: Proof scorecard: causal recovery, controls, experiment selection, and prospective plan. Approved packet: Setup, baselines, ablations, intervals, leakage checks, controls, and exclusions.
- Pre-seed capital buys a seed-ready proof package. Decision: Capital converts modeling evidence into human-cell proof, model-selected experiments, and a partner-ready workflow. Public signal: 18-month roadmap and capital-to-proof plan. Approved packet: Budget, hiring sequence, validation contract, and financing materials.
- The right investor can increase the slope of the proof. Decision: Best-fit investors bring AI-for-science conviction, biology hiring leverage, design-partner access, and seed storytelling. Public signal: Investor-fit section and private-packet preview. Approved packet: Partner pipeline, technical diligence priorities, and round participation.

Category positioning:
- Atlases and foundation models make biology legible. State models are the substrate. Galen builds the action layer above them: response prediction, confidence, and experiment choice. Buyer question: What state is this cell in, and what biological programs are visible? Galen position: 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. The lab supplies reality. Galen's product is the model-directed learning loop that decides what to measure and how the result should update the virtual cell. Buyer question: Which experiment should consume scarce lab budget next? Galen position: 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. The first product plugs into an existing discovery decision: prioritize interventions before the next expensive experiment queue is run. Buyer question: Which target, perturbation, donor, state, dose, or combination deserves follow-up? Galen position: Galen returns a ranked intervention plan, expected response, confidence, and the smallest useful validation set.
- A focused wedge compounds into programmable biology. The platform is earned through measured performance, reliable confidence, and recurring customer workflows. Buyer question: Can focused prospective reliability expand into a reusable virtual-cell platform? Galen position: Galen starts with causal intervention ranking and expands the same machinery into predict, design, and verify capabilities as evidence accumulates.

Your task:
1. Summarize Galen's investment thesis in five precise bullets.
2. Explain the first commercial wedge, why now, and why the intervention layer matters.
3. Use the public proof ledger to separate public evidence from next proof: identify what Galen has shown, what remains to be proven, and which private diligence files should be requested.
4. Evaluate whether the pre-seed plan maps capital to a seed-stage proof package.
5. List the most important diligence questions for a first founder call.
6. Draft a concise internal note a partner could forward to another investor or technical advisor.
7. Draft a warm-intro email using the public intro email as the starting point.

Public evidence anchors:
- ~90%: Hidden mechanism support recovered in the strongest public summary tests. Galen's current flagship evidence shows the architecture can recover and use cause-and-effect structure when a realistic single-cell-style experiment contains identifiable signal.
- 15 / 15: Control settings behaved as expected. The evidence practice matters as much as the benchmark: Galen records when evidence supports a claim, when confidence is earned, and when a control outcome remains a control outcome.
- built: The experiment-selection software is already part of the system. In controlled dry-lab tests, Galen selected experiments expected to teach the model more than simple same-budget alternatives. The pre-seed loop tests that claim in living cells.
- prospective: The round funds predictions committed before results. The next investor-grade evidence is forward-looking: define the benchmark, lock the baselines, run the experiment, and score the model after biology answers.

18-month proof plan:
- Months 0-3: Fundraise story and first validation plan. Investor evidence pack, first partner workflow, scoring plan fixed before results, candidate experiment queue, and clear data-quality gates.
- Months 3-6: Reliable measurement setup. 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: First real lab learning cycle. 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: Experiment selection on real data. Same-budget comparison, second experiment queue, and one paid or formally committed partner validation path if timing permits.
- Months 12-15: Test across contexts and partners. Shared intervention data across cell states or donors, partner validation underway, and previously unseen context evaluation if the data supports it.
- Months 15-18: Seed-ready proof package. Previously unseen result scored after predictions are committed, model-selection result, repeatable partner package, and evidence-based scale plan.

Investor beliefs the page is designed to test:
- Intervention prediction is the highest-value layer. Maps of biology become more valuable when a team can predict how a cell responds when biology is changed.
- Focused reliability can become a platform. A focused paid use case can improve the same machinery that later supports broader virtual-cell capabilities.
- Galen knows the next proof that matters. The round is organized around a forward-looking human-cell study, calibrated confidence, and model-selected experiments.
- The team can bridge model, biology, and evidence. Galen's founding context combines medicine, machine learning, computational biology, and the discipline to score claims before results are known.

Best-fit investor profile:
- 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.

Private packet access path:
- 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.

Private packet index:
- Technical proof packet. Request: Technical diligence. Best for: Investors underwriting model quality, comparison plans, controls, and scientific validity. Includes: Proof summaries, model-card excerpts, and claim boundaries; Baselines, control outcomes, intervals, leakage checks, and ablation notes; Scoring plan, protocol-level exclusions, and validation contract.
- Round materials packet. Request: Round materials. Best for: Leads and serious followers evaluating ownership, budget, hiring sequence, and seed-readiness plan. Includes: Use-of-proceeds detail mapped to proof milestones; Hiring sequence, operating budget, and financing materials; Expected seed-stage evidence package and round follow-up.
- Commercial wedge packet. Request: Design partner. Best for: Investors helping with customer access, buyer workflow diligence, or first design-partner introductions. Includes: First buyer profile and design-partner workflow; Partner intake, report shape, and intervention-ranking deliverable; Commercial motion from first paid program to recurring access.
- Founder conversation packet. Request: Founder conversation. Best for: Investors ready to pressure-test the current proof boundary, next experiment, and founder-market fit. Includes: Founder-call agenda and highest-signal diligence questions; Current proof boundary and next experiment decision points; Follow-up items matched to the investor's diligence role.

Ground rules:
- Use only public information from the sources above unless the user supplies private materials.
- Mark uncertain points as diligence questions instead of filling gaps with assumptions.
- Distinguish validated claims, active build items, next proof, and long-term platform ambition.
- Do not ask for confidential protocol details in a public thread; recommend requesting the private proof packet when needed.
- Optimize the output for investor decision-making: whether to take a meeting, what to ask, and what evidence would change conviction.

Preferred output format:
1. Investment thesis
2. What is already shown
3. What the pre-seed round must prove
4. Why this can become a large company
5. Key risks and private diligence requests
6. Suggested next action