{ "schemaVersion": 1, "title": "Galen pre-seed investor page", "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.", "disclosure": "This packet contains the public investor-page content. Approved investors receive detailed diligence materials, protocol details, round materials, and private strategy through the investor data room.", "publicUrls": { "canonical": "https://preseed.usegalen.com", "websiteFallback": "https://www.usegalen.com/preseed", "markdown": "https://preseed.usegalen.com/llms.txt", "json": "https://preseed.usegalen.com/investor-brief.json", "aiPrompt": "https://preseed.usegalen.com/ai-diligence-prompt.txt", "partnerMemo": "https://preseed.usegalen.com/partner-memo.md", "proofLedger": "https://preseed.usegalen.com/proof-ledger.md", "diligenceQuestions": "https://preseed.usegalen.com/diligence-questions.md", "introEmail": "https://preseed.usegalen.com/intro-email.txt" }, "publicPaths": { "markdown": "/preseed/llms.txt", "json": "/preseed/investor-brief.json", "aiPrompt": "/preseed/ai-diligence-prompt.txt", "partnerMemo": "/preseed/partner-memo.md", "proofLedger": "/preseed/proof-ledger.md", "diligenceQuestions": "/preseed/diligence-questions.md", "introEmail": "/preseed/intro-email.txt" }, "forwardableIntro": { "title": "Forwardable investor intro", "body": "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." }, "heroStats": [ { "label": "Current evidence", "value": "Causal recovery", "detail": "the model identifies intervention effects in realistic tests" }, { "label": "Next proof", "value": "18 months", "detail": "real lab study with predictions committed before results are known" }, { "label": "First product", "value": "Intervention ranking", "detail": "help teams choose which biological changes deserve lab budget" } ], "roundSnapshot": { "thesis": { "label": "round thesis", "title": "A pre-seed round built around prospective biology.", "body": "The financing advances one investor-underwritable claim: Galen can predict, choose, measure, and learn from real human-cell interventions before results are known." }, "items": [ { "label": "stage", "title": "Pre-seed diligence path", "body": "The public workspace gives investors a complete first-pass view before requesting private proof files, round materials, and founder follow-up." }, { "label": "capital use", "title": "18-month seed proof package", "body": "Capital funds the human-cell learning loop, model-selected experiments, scientific software, and partner-ready intervention-ranking workflow." }, { "label": "lead fit", "title": "AI-for-science platform conviction", "body": "Best-fit investors can help with biology leadership, design-partner access, wet-lab judgment, and seed-stage company building." }, { "label": "approved access", "title": "Terms, budget, and exact proof files", "body": "Approved investors can review use-of-proceeds detail, round materials, baselines, controls, validation contracts, and protocol-level notes." } ] }, "diligencePath": [ { "label": "01", "title": "Read the memo", "body": "Understand the thesis, the first paid wedge, and why intervention prediction is the valuable layer.", "href": "#memo", "time": "2 min", "action": "Open memo" }, { "label": "02", "title": "Inspect the evidence", "body": "See what Galen has validated, what is being built, and what the next proof must show.", "href": "#proof", "time": "3 min", "action": "Open ledger" }, { "label": "03", "title": "Review the proof plan", "body": "Connect pre-seed capital to the forward-looking human-cell study, model-selected experiments, and seed-ready outputs.", "href": "#roadmap", "time": "3 min", "action": "Open roadmap" }, { "label": "04", "title": "Request the private packet", "body": "Approved investors receive the proof files, round materials, use-of-proceeds detail, and founder follow-up.", "href": "#data-room", "time": "1 min", "action": "Request access" } ], "investorDecisionFrame": [ { "label": "category conviction", "title": "Intervention prediction is the valuable virtual-cell layer.", "decision": "Underwrite Galen if intervention prediction and experiment selection are the valuable layer above biological maps.", "publicSignal": "Why-now, product output, and wedge-to-platform sections.", "privateDiligence": "Commercial wedge, design-partner workflow, and round materials." }, { "label": "technical belief", "title": "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.", "publicSignal": "Proof scorecard: causal recovery, controls, experiment selection, and prospective plan.", "privateDiligence": "Setup, baselines, ablations, intervals, leakage checks, controls, and exclusions." }, { "label": "financing milestone", "title": "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.", "publicSignal": "18-month roadmap and capital-to-proof plan.", "privateDiligence": "Budget, hiring sequence, validation contract, and financing materials." }, { "label": "fit", "title": "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.", "publicSignal": "Investor-fit section and private-packet preview.", "privateDiligence": "Partner pipeline, technical diligence priorities, and round participation." } ], "whyNow": [ { "label": "category", "title": "Virtual cells are becoming inevitable.", "body": "Large biological datasets, single-cell atlases, perturbation screens, and foundation-model investment have made the category legible to customers and investors." }, { "label": "gap", "title": "Intervention prediction is the valuable layer.", "body": "Discovery teams already have maps of biological state. The scarce decision is what to perturb, what response to expect, and which experiment resolves uncertainty fastest." }, { "label": "timing", "title": "The next proof can be scored before results are known.", "body": "Galen's pre-seed plan converts controlled modeling evidence into a forward-looking human-cell study with predictions, comparison models, and scoring fixed in advance." }, { "label": "wedge", "title": "Intervention ranking is useful before the platform is broad.", "body": "A focused model can help teams prioritize expensive follow-up work while each deployment strengthens the same causal virtual-cell machinery." } ], "publicProofScorecard": [ { "label": "architecture proof", "value": "~90%", "title": "Hidden mechanism support recovered in the strongest public summary tests.", "body": "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.", "packet": "Exact setup, baselines, confidence intervals, ablations, and leakage checks are in the approved proof packet." }, { "label": "control discipline", "value": "15 / 15", "title": "Control settings behaved as expected.", "body": "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.", "packet": "Approved investors can review the control settings, control outcomes, and claim-boundary notes." }, { "label": "experiment choice", "value": "built", "title": "The experiment-selection software is already part of the system.", "body": "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.", "packet": "The packet includes the active-design verdict, comparison policies, and the real-lab scoring plan." }, { "label": "next proof", "value": "prospective", "title": "The round funds predictions committed before results.", "body": "The next investor-grade evidence is forward-looking: define the benchmark, lock the baselines, run the experiment, and score the model after biology answers.", "packet": "Approved investors receive the validation contract, use-of-proceeds detail, and protocol-level exclusions." } ], "customerPain": [ { "label": "search cost", "title": "Every candidate consumes scarce lab budget.", "body": "Target, intervention, donor, dose, state, and combination choices expand faster than a physical lab can test them with equal discipline." }, { "label": "uncertainty", "title": "Teams need confidence with every recommendation.", "body": "A useful system must say which prediction is strong, which one is fragile, and which experiment would make the next decision clearer." }, { "label": "cycle time", "title": "Slow validation weakens the discovery loop.", "body": "When every decision waits on broad physical search, teams spend months learning which parts of the search space deserved attention." } ], "commercialMotion": [ { "label": "first buyer", "title": "Teams with expensive intervention choices.", "body": "Functional-genomics, immunology, target-discovery, and cell-engineering groups already spend budget deciding which targets, perturbations, donors, states, and readouts deserve reality." }, { "label": "first offer", "title": "A paid prioritization program with a validation plan.", "body": "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." }, { "label": "expansion", "title": "Each deployment improves the same virtual-cell asset.", "body": "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." } ], "wedgeToPlatform": [ { "label": "01", "title": "Rank interventions", "body": "Start with customer decisions that already exist: which targets, perturbations, or follow-up experiments deserve the next tranche of lab budget." }, { "label": "02", "title": "Commit predictions", "body": "Record expected response, confidence, comparison models, and scoring rules before the result is known." }, { "label": "03", "title": "Measure and update", "body": "Use the lab result to improve the model, the measurement contract, and the next experiment queue." }, { "label": "04", "title": "Compound the virtual cell", "body": "Expand reliable prediction across contexts, donors, cell states, interventions, and customer workflows." } ], "proofLedger": [ { "claim": "Causal recovery in realistic tests", "status": "validated", "evidence": "In realistic test settings based on measurements from individual cells, Galen recovered the causal relationships behind cell responses, beat matched comparison models in two positive settings, and correctly handled 15 control settings.", "interpretation": "The model can recover and use cause-and-effect structure when the data contains enough signal to identify it.", "scope": "Current evidence supports controlled causal recovery under realistic measurement constraints.", "next": "Run the same kind of test on actual intervention data, with predictions and scoring rules fixed before results are known." }, { "claim": "The cause-and-effect path matters", "status": "validated", "evidence": "When Galen used its full path for inferring causes, combining effects, and predicting the resulting cell state, it outperformed comparison models built to test whether the signal was real and correctly labeled.", "interpretation": "The result supports Galen's thesis that causal structure improves intervention prediction.", "scope": "Current evidence is strongest in controlled settings where the evaluator can score the true causal structure.", "next": "Carry the same discipline into the first real human-cell study." }, { "claim": "Experiment-selection software", "status": "building", "evidence": "In a software benchmark, Galen chose a same-budget experiment that taught the model more than random selection.", "interpretation": "The experiment-selection rule is worth testing in the lab.", "scope": "This result supports the lab study design for testing model-selected experiments under real experiment cost and noise.", "next": "Compare model-selected experiments against random, diverse, uncertainty-driven, and conventional plans under real experiment cost and noise." }, { "claim": "Integrated virtual-cell machinery", "status": "building", "evidence": "The codebase connects cell-response modeling, uncertainty estimates, experiment selection, and reporting into one workflow.", "interpretation": "Galen is building a coherent system for prediction, experiment choice, measurement, and learning.", "scope": "The current system is ready to be refined against real human-cell data.", "next": "Use real human-cell data to decide which parts of the system deserve scale-up." }, { "claim": "Real human-cell learning loop", "status": "next", "evidence": "The pre-seed roadmap defines a real human-cell study with predictions recorded before results, comparison models fixed in advance, model-selected experiments, and measured outcomes.", "interpretation": "This is the next value-creating proof.", "scope": "This study turns Galen's modeling evidence into a living-cell learning loop.", "next": "Run the first forward-looking study in human CD4+ T cells, a central immune-cell type, and score previously unseen interventions before revising the model." }, { "claim": "General programmable virtual-cell platform", "status": "long-term", "evidence": "The platform map defines predict, design, and verify as the long-term product layer as focused models become reliable before results are known.", "interpretation": "The company can become much larger if the loop compounds across contexts.", "scope": "Galen earns platform breadth by expanding reliable prediction across contexts, interventions, and customer workflows.", "next": "Earn platform language through real performance before results are known, reliable confidence estimates, learning efficiency, and customer workflow integration." } ], "roadmap": [ { "window": "Months 0-3", "theme": "Fundraise story and first validation plan", "exit": "Investor evidence pack, first partner workflow, scoring plan fixed before results, candidate experiment queue, and clear data-quality gates.", "risk": "Gives investors a precise story, a concrete first product, and a measurable validation plan." }, { "window": "Months 3-6", "theme": "Reliable measurement setup", "exit": "Repeatable path from planned cell intervention to measured outcome, quality checks, data provenance, first lab-run plan, and a qualified partner workflow.", "risk": "Creates reliable measurements before the model learns from living biology." }, { "window": "Months 6-9", "theme": "First real lab learning cycle", "exit": "One cycle where the model predicts, the lab measures, the model updates, and the comparison plan is fixed before results are known.", "risk": "Moves Galen from past-data proof to forward-looking learning." }, { "window": "Months 9-12", "theme": "Experiment selection on real data", "exit": "Same-budget comparison, second experiment queue, and one paid or formally committed partner validation path if timing permits.", "risk": "Shows whether model-selected experiments produce more learning than volume under real cost and noise." }, { "window": "Months 12-15", "theme": "Test across contexts and partners", "exit": "Shared intervention data across cell states or donors, partner validation underway, and previously unseen context evaluation if the data supports it.", "risk": "Shows how reusable the learned biology is across cell states, donors, and partner questions." }, { "window": "Months 15-18", "theme": "Seed-ready proof package", "exit": "Previously unseen result scored after predictions are committed, model-selection result, repeatable partner package, and evidence-based scale plan.", "risk": "Creates the seed-stage claim from measured learning efficiency and partner-ready execution." } ], "capitalPlan": [ { "label": "01", "title": "Forward-looking cell proof", "body": "Run the first real human-cell learning loop with predictions, comparison models, and scoring rules committed before results are known." }, { "label": "02", "title": "Experiment-selection engine", "body": "Improve the causal model, confidence estimates, and experiment-selection software around the first validation task." }, { "label": "03", "title": "Partner-ready package", "body": "Turn the proof loop into a repeatable intervention-ranking workflow: intake, report, scoring, and next-experiment recommendation." }, { "label": "04", "title": "Reusable scientific system", "body": "Build the data provenance, quality standards, and lab-operating practices that make each measured result useful for the next model." } ], "investorMomentum": [ { "label": "buyers", "title": "Built for teams with expensive prioritization decisions.", "body": "Functional genomics, immunology, target discovery, and cell-engineering teams already decide which biological changes deserve follow-up." }, { "label": "round", "title": "Capital converts thesis into measured proof.", "body": "Capital goes into the first real human-cell learning loop, model-selected experiments, scientific software, and partner validation." }, { "label": "seed story", "title": "Seed readiness is evidence-based.", "body": "The planned output gives Galen a seed-stage story built on forward-looking prediction, measured learning efficiency, and a repeatable partner package." } ], "investorBeliefs": [ { "label": "belief 1", "title": "Intervention prediction is the highest-value layer.", "body": "Maps of biology become more valuable when a team can predict how a cell responds when biology is changed." }, { "label": "belief 2", "title": "Focused reliability can become a platform.", "body": "A focused paid use case can improve the same machinery that later supports broader virtual-cell capabilities." }, { "label": "belief 3", "title": "Galen knows the next proof that matters.", "body": "The round is organized around a forward-looking human-cell study, calibrated confidence, and model-selected experiments." }, { "label": "belief 4", "title": "The team can bridge model, biology, and evidence.", "body": "Galen's founding context combines medicine, machine learning, computational biology, and the discipline to score claims before results are known." } ], "dataRoomTiers": [ { "label": "public", "title": "Public investor workspace", "body": "The thesis, first product, proof ledger, roadmap, founder context, FAQ, and public diligence packet." }, { "label": "approved", "title": "Approved investor packet", "body": "Private proof files, technical model summaries, round materials, use-of-proceeds detail, and founder follow-up." }, { "label": "technical", "title": "Technical diligence", "body": "Protocol-level details, scoring plans, implementation notes, exclusion rules, and materials that protect Galen's intellectual property." } ], "accessPath": [ { "label": "01", "title": "Request review", "body": "We review the request, firm context, and stated diligence need before sharing private proof files or round materials." }, { "label": "02", "title": "Matched packet", "body": "Approved investors receive the right package for their intent: proof files, technical diligence, round materials, design-partner detail, or founder follow-up." }, { "label": "03", "title": "Founder diligence", "body": "The first conversation focuses on the current proof boundary, the human-cell validation plan, model-selected experiments, and seed-readiness evidence." } ], "investorFit": [ { "label": "ideal investor", "title": "AI-for-science and bio-platform conviction.", "body": "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." }, { "label": "useful help", "title": "Capital plus scientific and commercial leverage.", "body": "The most helpful investors can support senior biology hiring, design-partner introductions, wet-lab execution judgment, and later seed-stage storytelling." }, { "label": "round outcome", "title": "A seed-ready proof package.", "body": "The pre-seed round is designed to produce prospective prediction evidence, measured learning efficiency, and a repeatable partner package." } ], "packetIndex": [ { "label": "proof", "title": "Technical proof packet", "request": "Technical diligence", "bestFor": "Investors underwriting model quality, comparison plans, controls, and scientific validity.", "body": "For a technical read on causal recovery, prediction scoring, confidence, controls, ablations, and the first human-cell validation contract.", "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" ] }, { "label": "round", "title": "Round materials packet", "request": "Round materials", "bestFor": "Leads and serious followers evaluating ownership, budget, hiring sequence, and seed-readiness plan.", "body": "For financing diligence on how the pre-seed converts into a prospective proof package, first partner workflow, and seed-stage evidence base.", "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" ] }, { "label": "market", "title": "Commercial wedge packet", "request": "Design partner", "bestFor": "Investors helping with customer access, buyer workflow diligence, or first design-partner introductions.", "body": "For a market read on the first paid prioritization workflow, buyer pain, customer intake, and the path from bounded wedge to recurring virtual-cell access.", "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" ] }, { "label": "founder", "title": "Founder conversation packet", "request": "Founder conversation", "bestFor": "Investors ready to pressure-test the current proof boundary, next experiment, and founder-market fit.", "body": "For a focused founder call around technical judgment, scientific prioritization, first hires, and the operating plan for prospective validation.", "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" ] } ], "privatePacketPreview": [ { "label": "proof", "title": "Evidence packet", "body": "Full proof summaries, model-card excerpts, verdict interpretation, baselines, control logic, and claim boundaries." }, { "label": "plan", "title": "Use-of-proceeds and milestone plan", "body": "Hiring priorities, build sequence, validation contract, capital allocation by proof milestone, and the expected seed-stage evidence package." }, { "label": "market", "title": "Commercial wedge materials", "body": "First buyer profile, design-partner workflow, first paid deployment shape, and the path from bounded causal triage to recurring virtual-cell access." }, { "label": "call", "title": "Founder follow-up", "body": "A focused diligence conversation around the current proof, the next prospective experiment, and the investor's specific concerns." } ], "faq": [ { "question": "What is a virtual cell?", "answer": "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." }, { "question": "What is Galen's first product?", "answer": "The first product helps biology teams rank which targets, interventions, or follow-up experiments deserve priority lab budget." }, { "question": "What has Galen proven?", "answer": "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." }, { "question": "What is Galen proving next?", "answer": "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." }, { "question": "Why primary CD4+ T cells first?", "answer": "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?" }, { "question": "Is Galen a wet-lab company or a software company?", "answer": "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." }, { "question": "What is Galen's differentiated virtual-cell thesis?", "answer": "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." }, { "question": "What does the pre-seed round fund?", "answer": "The round funds the first real human-cell learning loop, model-selected experiments, scientific software and data infrastructure, and the first paid partner validation." } ], "diligenceQuestions": [ { "title": "Meeting decision", "purpose": "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?" ], "privatePacketFocus": [ "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.", "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." ] }, { "title": "Technical proof", "purpose": "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?" ], "privatePacketFocus": [ "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." ] }, { "title": "Prospective proof plan", "purpose": "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?" ], "privatePacketFocus": [ "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." ] }, { "title": "Commercial wedge", "purpose": "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?" ], "privatePacketFocus": [ "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." ] }, { "title": "Capital to proof", "purpose": "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?" ], "privatePacketFocus": [ "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." ] }, { "title": "Investor fit and access path", "purpose": "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?" ], "privatePacketFocus": [ "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." ] } ], "primaryAction": { "label": "Request private proof packet", "href": "https://preseed.usegalen.com#data-room" } }