BioStack growth playbook

Rewrite the homepage in one sprint. H1: what BioStack is now (real-world clinical data and environments for healthcare AI post-training). Proof strip: 100M+ de-identified records; US, India, Brazil, Argentina; notes, labs, imaging, medications, outcomes; several hundred thousand scans; several million telehealth transcripts. The loop: data, environments, evals and reward functions, benchmarks, each with one line on what the buyer receives. Three audience rows (AI labs, human data companies, biotech) with a concrete deliverable each. Name the investors in text, add a privacy line linking the Trust Center, and offer two calls to action: "Request a sample" (modality and use case) and "Book a scoping call". Remove the cart icon, the microscopy image and the multi-agent offer.

Technical buyers decide in seconds whether a data vendor is serious. Numbers, a clear loop and a privacy statement answer their first three questions, and a sample request is a smaller first step than a consultation. The YC launch already offers to send samples, so the page only has to ask for it.

[ ] Remove the cart icon, microscopy image and multi-agent line
[ ] New H1 and sub
[ ] Proof strip with public numbers only
[ ] Four-step loop
[ ] Three audience rows
[ ] Request a sample form and Book a scoping call
[ ] Investors in text, alt text on logos, Trust Center link

Draft
H1: Real-world clinical data and environments for healthcare AI
Sub: Longitudinal, de-identified patient journeys (notes, labs, imaging, medications and outcomes) turned into datasets, evals, reward functions and RL environments.

Proof: 100M+ de-identified patient records | Hospitals in the US, Brazil, Argentina and India | Hundreds of thousands of linked X-ray, MRI and CT scans | Millions of telehealth transcripts

The loop: Data -> Environments -> Evals and rewards -> Benchmarks

For AI labs: clinical environments scored against real outcomes.
For human data companies: hard-to-find healthcare data, delivered ready to use.
For biotech: disease modeling, biomarker discovery and treatment response.

[Request a sample]  [Book a scoping call]
Backed by Peak XV, DCVC, Afore Capital, Y Combinator and others. Privacy and security: see our Trust Center.

• Publishing numbers that change

  Use the same rounded figures as the press release and date the strip.

• Losing biotech buyers when pre-clinical copy is removed

  Keep one biotech row about disease modeling and biomarker discovery.

• Your websiteHomepage (checked November 9, 2026): headline "Differentiated data that 10X-es AI"; offerings include "Build RL envs for post-training", "Annotate novel / public data" and "Access multi-agent reasoning infra"; audience cards for Biotech Startups, AI Labs and "Human Data Co."; a "Never doubt your data again" section over a fluorescence microscopy image; a cart icon linking to /cart; "Book a consultation" is the only call to action. There is no patient-record count, no hospitals or countries, no customer logos or testimonials and no de-identification claim; four "Backed by" logos carry no alt text.Source
• PressPulse 2.0 (October 23, 2026): a $9.5 million seed from Peak XV, Afore Capital, DCVC, Emerson Collective, SV Angel, Verdict Capital, Streamlined Ventures and Y Combinator; more than 100 million de-identified patient records; a network across the U.S., India, Brazil and Argentina; several hundred thousand X-rays, MRIs and CT scans combined with pathology and other clinical records; funds go to U.S. and international expansion, the platform, and engineering and research hiring.Source
• Y CombinatorLaunch YC post: "Healthcare AI is learning from a cleaned-up version of medicine that barely exists." BioStack turns longitudinal records (notes, labs, imaging, medications, vitals, outcomes) into environments where models diagnose, order tests or adjust medication and are scored against outcomes, guidelines and clinician review; it sells data, evals, reward functions, benchmarks and simulation environments; it reports 17 customers and prospects including top AI labs, after two earlier pivots (multi-agent biology research and wet-lab drug discovery data); it asks for introductions to model builders and to teams that need raw medical data, and offers to send samples.Source

In Squarespace's SEO settings, set a site title and meta description that match the new homepage, upload a 1200x630 share image with the headline and proof numbers, set the page language to English, and add a favicon. Then check previews in LinkedIn's Post Inspector and in a Slack message.

Most first visits to a seed-stage company come from shared links and branded searches. A clear preview raises click-through and tells AI assistants what BioStack does now.

• Description drifts from the page again

  Update both whenever the H1 changes.

• #1 Put the 100-million-record network on the homepage: it still sells the two pivots BioStack left behind

• Google PageSpeedHomepage test, November 9, 2026: mobile performance 61/100 and desktop 95; lab mobile LCP 13.7 s, but real visitors pass Core Web Vitals (field LCP 2.0 s, INP 82 ms, CLS 0); 1,946 KB page weight including 1,154 KB of JavaScript, mostly from the Squarespace platform; no meta description, social share image, page language or favicon.Source
• PressPulse 2.0 (October 23, 2026): a $9.5 million seed from Peak XV, Afore Capital, DCVC, Emerson Collective, SV Angel, Verdict Capital, Streamlined Ventures and Y Combinator; more than 100 million de-identified patient records; a network across the U.S., India, Brazil and Argentina; several hundred thousand X-rays, MRIs and CT scans combined with pathology and other clinical records; funds go to U.S. and international expansion, the platform, and engineering and research hiring.Source

Build a list of 40 teams that publish on clinical reasoning, medical agents or healthcare evals, from papers, model cards and job posts. Write to each with one sentence on their work, a one-page spec of a 50-patient longitudinal environment (task, tools, reward, verifier) and a two-week turnaround. Price it low and credit it against a full engagement.

Researchers judge data by running it. A pilot with a verifier gives them a result to show internally, and the Clinical Data Lead's scoping process starts with a concrete ask.

• Pilots eat engineering time

  One fixed template; customize only the task.

• Data use rules

  Use only records already cleared for this use and a standard data use agreement.

• Y CombinatorLaunch YC post: "Healthcare AI is learning from a cleaned-up version of medicine that barely exists." BioStack turns longitudinal records (notes, labs, imaging, medications, vitals, outcomes) into environments where models diagnose, order tests or adjust medication and are scored against outcomes, guidelines and clinician review; it sells data, evals, reward functions, benchmarks and simulation environments; it reports 17 customers and prospects including top AI labs, after two earlier pivots (multi-agent biology research and wet-lab drug discovery data); it asks for introductions to model builders and to teams that need raw medical data, and offers to send samples.Source
• HiringFounding Research Engineer, RL/Reasoning (San Francisco, $200K–$350K): builds healthcare RL environments (tasks, tool interfaces, reward functions, verifiers, evaluation harnesses), runs SFT, RLVR and RLHF experiments, and analyzes failures to improve environments, rewards and datasets, across clinical reasoning, longitudinal patient care, diagnostic decision-making and chronic disease management.Source
• HiringClinical Data Lead (San Francisco, $120K–$200K plus uncapped variable pay): owns customer success and data delivery from first conversation through scoped dataset delivery, engages hospitals and imaging groups, and in the first 90 days should deliver the company's first longitudinal dataset from a major US radiology group and improve sourcing, scoping, QC and delivery. Customers are "leading AI labs, human data companies, and frontier biotech teams".Source

Publish a short press and fact page: one-paragraph description, founding year, team size, investors, the approved numbers (records, scans, transcripts), countries and what happens in each, data types, use cases, a privacy summary and a media contact form. Link it from the footer, send it to everyone who covered the round, and use it to update the YC profile and data directories.

Buyers and journalists check consistency. One canonical page stops conflicting numbers from spreading and gives AI search a source it can quote.

• Revealing partner details

  Describe hospitals by country and type, never by name without consent.

• PressPulse 2.0 (October 23, 2026): a $9.5 million seed from Peak XV, Afore Capital, DCVC, Emerson Collective, SV Angel, Verdict Capital, Streamlined Ventures and Y Combinator; more than 100 million de-identified patient records; a network across the U.S., India, Brazil and Argentina; several hundred thousand X-rays, MRIs and CT scans combined with pathology and other clinical records; funds go to U.S. and international expansion, the platform, and engineering and research hiring.Source
• PressInc42: BioStack works with "multiple hospitals" across the US, Brazil and Argentina, and in India connects cancer patients with an investigational cancer vaccine program; it holds "several million" telehealth audio transcripts; named use cases are AI model evaluation, disease modeling, biomarker discovery, patient stratification and treatment-response analysis; it offers custom workflows to protect patient privacy.Source
• PressHealthcare IT Today: BioStack announced a "data infrastructure network to power the next era of medicine" that links notes, labs, imaging, medications, treatments and outcomes for population- and hospital-level research.Source

Set up form-submit events in Tag Manager for "Book a consultation" and the new sample request, capture UTM and referrer in hidden fields, and add a "How did you hear about us?" question. Tag every outbound link (press kit, outreach, social) with UTMs and review sources weekly.

A small team should double down on the two channels that create pilots. That needs per-source counts, not page views.

• Unused tags slow the page

  Keep one tag manager and remove any tag no one reads.

• Your websiteSite scan (November 9, 2026): Google Tag Manager, Google Analytics and a Google Ads tag load on the homepage; no newsletter signup, case studies, testimonials or social profile links were found; the site runs on Squarespace and the domain dates from October 2024.Source
• Y CombinatorLaunch YC post: "Healthcare AI is learning from a cleaned-up version of medicine that barely exists." BioStack turns longitudinal records (notes, labs, imaging, medications, vitals, outcomes) into environments where models diagnose, order tests or adjust medication and are scored against outcomes, guidelines and clinician review; it sells data, evals, reward functions, benchmarks and simulation environments; it reports 17 customers and prospects including top AI labs, after two earlier pivots (multi-agent biology research and wet-lab drug discovery data); it asks for introductions to model builders and to teams that need raw medical data, and offers to send samples.Source

Publish a "How an engagement works" page: 1) scoping call, 2) data plan with cohort, modalities and outcomes, 3) sample and QC report, 4) agreement, 5) delivery format (files, environment, eval harness), 6) support and refresh. Give typical timelines for each step and a downloadable scoping template the buyer can fill in before the call.

Visible process lowers perceived risk for a first purchase from a young vendor, and a pre-filled scoping template makes the first call shorter and the proposal faster.

• Promised timelines slip

  Publish ranges, not fixed dates.

• HiringClinical Data Lead (San Francisco, $120K–$200K plus uncapped variable pay): owns customer success and data delivery from first conversation through scoped dataset delivery, engages hospitals and imaging groups, and in the first 90 days should deliver the company's first longitudinal dataset from a major US radiology group and improve sourcing, scoping, QC and delivery. Customers are "leading AI labs, human data companies, and frontier biotech teams".Source
• Your websiteHomepage (checked November 9, 2026): headline "Differentiated data that 10X-es AI"; offerings include "Build RL envs for post-training", "Annotate novel / public data" and "Access multi-agent reasoning infra"; audience cards for Biotech Startups, AI Labs and "Human Data Co."; a "Never doubt your data again" section over a fluorescence microscopy image; a cart icon linking to /cart; "Book a consultation" is the only call to action. There is no patient-record count, no hospitals or countries, no customer logos or testimonials and no de-identification claim; four "Backed by" logos carry no alt text.Source

Add LinkedIn and X links to the header and footer. Post one short build note a week from a founder account: an environment design choice, a data quality lesson or a new modality, each ending with a link to request a sample. Add a simple email signup for the occasional update.

Consistent technical posts build trust with exactly the researchers who choose data vendors, at almost no cost.

• Sharing customer details

  Only general lessons; no customer or hospital names.

• Your websiteSite scan (November 9, 2026): Google Tag Manager, Google Analytics and a Google Ads tag load on the homepage; no newsletter signup, case studies, testimonials or social profile links were found; the site runs on Squarespace and the domain dates from October 2024.Source
• HiringFounding Research Engineer, RL/Reasoning (San Francisco, $200K–$350K): builds healthcare RL environments (tasks, tool interfaces, reward functions, verifiers, evaluation harnesses), runs SFT, RLVR and RLHF experiments, and analyzes failures to improve environments, rewards and datasets, across clinical reasoning, longitudinal patient care, diagnostic decision-making and chronic disease management.Source

Create a data card for each modality and cohort: what is included, approximate counts, countries, time span, linked outcomes, de-identification method, formats and a small synthetic example record. Put them on a /data page with a "request this sample" button on each.

Self-qualification saves the Clinical Data Lead's time and brings better-scoped requests. Detailed cards also rank for the specific searches researchers run.

• Revealing too much to competitors

  Rounded counts and no partner names.

• #1 Put the 100-million-record network on the homepage: it still sells the two pivots BioStack left behind

• PressInc42: BioStack works with "multiple hospitals" across the US, Brazil and Argentina, and in India connects cancer patients with an investigational cancer vaccine program; it holds "several million" telehealth audio transcripts; named use cases are AI model evaluation, disease modeling, biomarker discovery, patient stratification and treatment-response analysis; it offers custom workflows to protect patient privacy.Source
• Competitor researchTruveta's homepage: 140M+ patients "updated daily", longitudinally linked EHR, claims, images and more; an "AI training and validation" use case; 350+ publications; named health-system members and clients.See the research Source
• Competitor researchSegmed's homepage: "Real-World Data, Rooted in Imaging", 2,800+ healthcare partner sites, 150,000,000+ imaging studies, 50+ FDA-cleared products built on its network, a Vanta-hosted Trust Center, a "Join our global network of healthcare partners" path, an RSNA 2026 agenda and a white paper on decreasing bias in healthcare AI.See the research Source

Publish a plain-language "Privacy and data governance" page: how records are de-identified, who reviews it, how data use is agreed with each hospital, how cross-border data is handled, access controls and delivery environments, and what is in the Trust Center. Link it from the homepage, forms and data cards. Have counsel review every claim before it goes live.

Procurement and legal reviews are where data deals stall. Answering the standard questions in public shortens the cycle and separates BioStack from brokers that can't explain their sourcing.

• Overstating compliance

  Only publish what counsel approves; describe methods, not guarantees.

• #1 Put the 100-million-record network on the homepage: it still sells the two pivots BioStack left behind

• Your websiteHomepage (checked November 9, 2026): headline "Differentiated data that 10X-es AI"; offerings include "Build RL envs for post-training", "Annotate novel / public data" and "Access multi-agent reasoning infra"; audience cards for Biotech Startups, AI Labs and "Human Data Co."; a "Never doubt your data again" section over a fluorescence microscopy image; a cart icon linking to /cart; "Book a consultation" is the only call to action. There is no patient-record count, no hospitals or countries, no customer logos or testimonials and no de-identification claim; four "Backed by" logos carry no alt text.Source
• PressInc42: BioStack works with "multiple hospitals" across the US, Brazil and Argentina, and in India connects cancer patients with an investigational cancer vaccine program; it holds "several million" telehealth audio transcripts; named use cases are AI model evaluation, disease modeling, biomarker discovery, patient stratification and treatment-response analysis; it offers custom workflows to protect patient privacy.Source
• Competitor researchProtege's homepage: "Real World Data for AI Development" across pre-training, post-training and "evaluation and benchmarks", including healthcare evals; two paths, "Explore data" for model builders and "Become a data partner" for data owners; a SOC 2 badge; a $25M Series A and $30M extension led by a16z.See the research Source

Create a partner page for hospitals, imaging groups and clinics: what they get (revenue share or funded data infrastructure, research collaborations, de-identification handled for them), what BioStack needs, how privacy and consent are handled, and a short inquiry form routed to the Clinical Data Lead. Translate it into Spanish and Portuguese for Argentina and Brazil.

Every new data source widens what BioStack can sell. An inbound path lets supply grow beyond the founders' network and gives outbound sourcing emails a credible link.

• Low-quality inquiries

  Ask for volume and modality up front.

• #9 Explain on one page how patient data is de-identified, governed and delivered

• HiringClinical Data Lead (San Francisco, $120K–$200K plus uncapped variable pay): owns customer success and data delivery from first conversation through scoped dataset delivery, engages hospitals and imaging groups, and in the first 90 days should deliver the company's first longitudinal dataset from a major US radiology group and improve sourcing, scoping, QC and delivery. Customers are "leading AI labs, human data companies, and frontier biotech teams".Source
• Competitor researchProtege's homepage: "Real World Data for AI Development" across pre-training, post-training and "evaluation and benchmarks", including healthcare evals; two paths, "Explore data" for model builders and "Become a data partner" for data owners; a SOC 2 badge; a $25M Series A and $30M extension led by a16z.See the research Source
• Competitor researchSegmed's homepage: "Real-World Data, Rooted in Imaging", 2,800+ healthcare partner sites, 150,000,000+ imaging studies, 50+ FDA-cleared products built on its network, a Vanta-hosted Trust Center, a "Join our global network of healthcare partners" path, an RSNA 2026 agenda and a white paper on decreasing bias in healthcare AI.See the research Source

Design a partner program for human data and annotation companies: a standard license to include BioStack datasets or environments in their offers, co-branded delivery, partner pricing, and joint scoping for lab requests that need real records plus expert labeling. Rename the homepage card to say what it means ("Data and annotation providers").

Partners already hold the lab relationships and procurement paperwork. BioStack supplies what they can't easily source, and both sides win more lab budget.

• Partners compete for the same lab deals

  Deal registration and clear rules on direct accounts.

• HiringClinical Data Lead (San Francisco, $120K–$200K plus uncapped variable pay): owns customer success and data delivery from first conversation through scoped dataset delivery, engages hospitals and imaging groups, and in the first 90 days should deliver the company's first longitudinal dataset from a major US radiology group and improve sourcing, scoping, QC and delivery. Customers are "leading AI labs, human data companies, and frontier biotech teams".Source
• Your websiteHomepage (checked November 9, 2026): headline "Differentiated data that 10X-es AI"; offerings include "Build RL envs for post-training", "Annotate novel / public data" and "Access multi-agent reasoning infra"; audience cards for Biotech Startups, AI Labs and "Human Data Co."; a "Never doubt your data again" section over a fluorescence microscopy image; a cart icon linking to /cart; "Book a consultation" is the only call to action. There is no patient-record count, no hospitals or countries, no customer logos or testimonials and no de-identification claim; four "Backed by" logos carry no alt text.Source

When negotiating the radiology source agreement, secure rights for non-exclusive licensing. Package the delivered cohort as a named product with a data card, a standard QC report, an eval split and a fixed license price. Offer it to imaging-model teams and as a starting point for custom requests.

Standard products raise margin and shorten sales cycles, and US radiology data with longitudinal follow-up is in steady demand from imaging AI developers, Segmed's core market.

• First buyer wants exclusivity

  Price exclusivity separately and time-limit it.

• #8 Publish data cards for each modality so buyers can scope before the first call

• HiringClinical Data Lead (San Francisco, $120K–$200K plus uncapped variable pay): owns customer success and data delivery from first conversation through scoped dataset delivery, engages hospitals and imaging groups, and in the first 90 days should deliver the company's first longitudinal dataset from a major US radiology group and improve sourcing, scoping, QC and delivery. Customers are "leading AI labs, human data companies, and frontier biotech teams".Source
• Competitor researchSegmed's homepage: "Real-World Data, Rooted in Imaging", 2,800+ healthcare partner sites, 150,000,000+ imaging studies, 50+ FDA-cleared products built on its network, a Vanta-hosted Trust Center, a "Join our global network of healthcare partners" path, an RSNA 2026 agenda and a white paper on decreasing bias in healthcare AI.See the research Source

Define three offers with set scope and starting prices: a paid sample pack (small cohort plus QC report, credited against a later deal), a dataset license (standard or custom cohort) and an environment build (tasks, tools, reward, verifier, eval harness, with optional ongoing runs). Show them on a /work-with-us page with "from" prices or ranges.

Packages shorten negotiation, let buyers self-select and turn the sample into a paid first step that predicts larger deals.

• Public prices anchor too low

  Show ranges and "from" prices only.

• #6 Show the path from first call to delivered dataset, step by step

• Y CombinatorLaunch YC post: "Healthcare AI is learning from a cleaned-up version of medicine that barely exists." BioStack turns longitudinal records (notes, labs, imaging, medications, vitals, outcomes) into environments where models diagnose, order tests or adjust medication and are scored against outcomes, guidelines and clinician review; it sells data, evals, reward functions, benchmarks and simulation environments; it reports 17 customers and prospects including top AI labs, after two earlier pivots (multi-agent biology research and wet-lab drug discovery data); it asks for introductions to model builders and to teams that need raw medical data, and offers to send samples.Source
• HiringClinical Data Lead (San Francisco, $120K–$200K plus uncapped variable pay): owns customer success and data delivery from first conversation through scoped dataset delivery, engages hospitals and imaging groups, and in the first 90 days should deliver the company's first longitudinal dataset from a major US radiology group and improve sourcing, scoping, QC and delivery. Customers are "leading AI labs, human data companies, and frontier biotech teams".Source

Package a telehealth conversation dataset: de-identified transcripts (and audio where permitted) linked to notes, orders and outcomes, with evals for note quality and next-step accuracy. Publish a data card and a short technical note, then reach out to voice and scribe teams with a sample.

It opens a second buyer group using data BioStack already has, and linking conversations to outcomes sets it apart from generic medical speech corpora.

• Voice re-identification

  Offer text-only by default; audio only with strict controls.

• #8 Publish data cards for each modality so buyers can scope before the first call

• PressInc42: BioStack works with "multiple hospitals" across the US, Brazil and Argentina, and in India connects cancer patients with an investigational cancer vaccine program; it holds "several million" telehealth audio transcripts; named use cases are AI model evaluation, disease modeling, biomarker discovery, patient stratification and treatment-response analysis; it offers custom workflows to protect patient privacy.Source
• Your websiteHomepage (checked November 9, 2026): headline "Differentiated data that 10X-es AI"; offerings include "Build RL envs for post-training", "Annotate novel / public data" and "Access multi-agent reasoning infra"; audience cards for Biotech Startups, AI Labs and "Human Data Co."; a "Never doubt your data again" section over a fluorescence microscopy image; a cart icon linking to /cart; "Book a consultation" is the only call to action. There is no patient-record count, no hospitals or countries, no customer logos or testimonials and no de-identification claim; four "Backed by" logos carry no alt text.Source

Build a cross-country evaluation: the same clinical tasks run on matched US and Latin American cohorts, showing where models trained on US data break (local drug names, Spanish and Portuguese notes, different care pathways). Publish the method and headline findings as a technical note, and sell the full eval to labs.

It turns geographic breadth, which competitors lack, into a measurable problem buyers must solve before shipping abroad.

• Small cohorts give noisy results

  Report confidence intervals and only publish robust gaps.

• PressInc42: BioStack works with "multiple hospitals" across the US, Brazil and Argentina, and in India connects cancer patients with an investigational cancer vaccine program; it holds "several million" telehealth audio transcripts; named use cases are AI model evaluation, disease modeling, biomarker discovery, patient stratification and treatment-response analysis; it offers custom workflows to protect patient privacy.Source
• Competitor researchTruveta's homepage: 140M+ patients "updated daily", longitudinally linked EHR, claims, images and more; an "AI training and validation" use case; 350+ publications; named health-system members and clients.See the research Source
• Competitor researchSegmed's homepage: "Real-World Data, Rooted in Imaging", 2,800+ healthcare partner sites, 150,000,000+ imaging studies, 50+ FDA-cleared products built on its network, a Vanta-hosted Trust Center, a "Join our global network of healthcare partners" path, an RSNA 2026 agenda and a white paper on decreasing bias in healthcare AI.See the research Source

Each month, publish one technical note on the blog: a failure pattern found in a BioStack environment (e.g. ignoring a lab trend across visits), how the reward or verifier caught it, and what changed. Use synthetic or aggregated examples only. Share on social, in researcher communities and in outreach.

Researchers trust vendors who show their work. Each note doubles as a sample of BioStack's environments and as a job ad for the team.

• Giving away know-how

  Share the pattern, not the full environment.

• HiringFounding Research Engineer, RL/Reasoning (San Francisco, $200K–$350K): builds healthcare RL environments (tasks, tool interfaces, reward functions, verifiers, evaluation harnesses), runs SFT, RLVR and RLHF experiments, and analyzes failures to improve environments, rewards and datasets, across clinical reasoning, longitudinal patient care, diagnostic decision-making and chronic disease management.Source
• Y CombinatorCompany page: "Real world training envs for healthcare AI models" and "building the post-training lab for healthcare AI"; founded 2025; team of 11; San Francisco; batch P26; two open roles.Source

Choose two events per half-year where both imaging-AI developers and radiology groups attend. Book meetings in advance with a target list, bring a one-page data card and sample offer, and host a small dinner for imaging-model researchers. Follow up within 48 hours.

One trip covers both sides of the business: buyers for the radiology product and new imaging partners for supply.

• Booth spending with little return

  Meetings and dinners only, no booth in year one.

• #12 Turn the first US radiology dataset into a standard product sold more than once

• Competitor researchSegmed's homepage: "Real-World Data, Rooted in Imaging", 2,800+ healthcare partner sites, 150,000,000+ imaging studies, 50+ FDA-cleared products built on its network, a Vanta-hosted Trust Center, a "Join our global network of healthcare partners" path, an RSNA 2026 agenda and a white paper on decreasing bias in healthcare AI.See the research Source
• HiringClinical Data Lead (San Francisco, $120K–$200K plus uncapped variable pay): owns customer success and data delivery from first conversation through scoped dataset delivery, engages hospitals and imaging groups, and in the first 90 days should deliver the company's first longitudinal dataset from a major US radiology group and improve sourcing, scoping, QC and delivery. Customers are "leading AI labs, human data companies, and frontier biotech teams".Source

Publish a short referral page and investor update section: who BioStack wants to meet (model teams in clinical reasoning, imaging and voice; hospitals and imaging groups), what a good intro looks like, and a thank-you (a cash reward or credit on closed deals for non-investors, a regular "wins from your intros" note for investors). Track every intro to outcome.

Warm intros convert far better than cold outreach for a young data vendor, and a clear ask makes it easy for supporters to help.

• Low-quality intros

  Reward only qualified meetings or closed deals.

• Y CombinatorLaunch YC post: "Healthcare AI is learning from a cleaned-up version of medicine that barely exists." BioStack turns longitudinal records (notes, labs, imaging, medications, vitals, outcomes) into environments where models diagnose, order tests or adjust medication and are scored against outcomes, guidelines and clinician review; it sells data, evals, reward functions, benchmarks and simulation environments; it reports 17 customers and prospects including top AI labs, after two earlier pivots (multi-agent biology research and wet-lab drug discovery data); it asks for introductions to model builders and to teams that need raw medical data, and offers to send samples.Source
• PressPulse 2.0 (October 23, 2026): a $9.5 million seed from Peak XV, Afore Capital, DCVC, Emerson Collective, SV Angel, Verdict Capital, Streamlined Ventures and Y Combinator; more than 100 million de-identified patient records; a network across the U.S., India, Brazil and Argentina; several hundred thousand X-rays, MRIs and CT scans combined with pathology and other clinical records; funds go to U.S. and international expansion, the platform, and engineering and research hiring.Source

Offer a refresh subscription on every delivered cohort: quarterly updates with new events and outcomes for the same patients, new held-out cases for evals, and a short change report. Price it as a share of the original license and include it in renewals.

It turns one-off projects into recurring revenue, keeps BioStack inside the customer's training cycle and keeps eval sets uncontaminated.

• Source hospitals can't send updates

  Make refresh terms part of every new data agreement.

• #13 Package the loop into three offers: sample, dataset license and environment build

• PressPulse 2.0 (October 23, 2026): a $9.5 million seed from Peak XV, Afore Capital, DCVC, Emerson Collective, SV Angel, Verdict Capital, Streamlined Ventures and Y Combinator; more than 100 million de-identified patient records; a network across the U.S., India, Brazil and Argentina; several hundred thousand X-rays, MRIs and CT scans combined with pathology and other clinical records; funds go to U.S. and international expansion, the platform, and engineering and research hiring.Source
• Competitor researchTruveta's homepage: 140M+ patients "updated daily", longitudinally linked EHR, claims, images and more; an "AI training and validation" use case; 350+ publications; named health-system members and clients.See the research Source
• Y CombinatorLaunch YC post: "Healthcare AI is learning from a cleaned-up version of medicine that barely exists." BioStack turns longitudinal records (notes, labs, imaging, medications, vitals, outcomes) into environments where models diagnose, order tests or adjust medication and are scored against outcomes, guidelines and clinician review; it sells data, evals, reward functions, benchmarks and simulation environments; it reports 17 customers and prospects including top AI labs, after two earlier pivots (multi-agent biology research and wet-lab drug discovery data); it asks for introductions to model builders and to teams that need raw medical data, and offers to send samples.Source

Within Squarespace: compress and resize the hero and section images, remove unused blocks and the commerce cart (which loads shop scripts), limit Typekit fonts to the weights used, and keep only the tags someone reads. Re-test with PageSpeed after each change.

A lighter page loads faster on any phone and removes leftovers from earlier versions of the site at the same time.

• Platform limits gains

  Accept a good-enough score; real-visitor metrics already pass.

• #1 Put the 100-million-record network on the homepage: it still sells the two pivots BioStack left behind

• Google PageSpeedHomepage test, November 9, 2026: mobile performance 61/100 and desktop 95; lab mobile LCP 13.7 s, but real visitors pass Core Web Vitals (field LCP 2.0 s, INP 82 ms, CLS 0); 1,946 KB page weight including 1,154 KB of JavaScript, mostly from the Squarespace platform; no meta description, social share image, page language or favicon.Source
• Your websiteHomepage (checked November 9, 2026): headline "Differentiated data that 10X-es AI"; offerings include "Build RL envs for post-training", "Annotate novel / public data" and "Access multi-agent reasoning infra"; audience cards for Biotech Startups, AI Labs and "Human Data Co."; a "Never doubt your data again" section over a fluorescence microscopy image; a cart icon linking to /cart; "Book a consultation" is the only call to action. There is no patient-record count, no hospitals or countries, no customer logos or testimonials and no de-identification claim; four "Backed by" logos carry no alt text.Source

Build a small public benchmark (a few hundred de-identified or synthetic-from-real longitudinal cases) where models must order tests, interpret trends and choose treatment, scored against outcomes and clinician review. Publish a paper, a leaderboard and a harness; keep a much larger private set and custom versions as the paid product.

Benchmarks set the agenda in AI research. Every lab that reports a score becomes a warm lead for the private set and the environments behind it.

• Benchmark contamination

  Keep a hidden test split and rotate it.

• Privacy exposure

  Synthetic-from-real cases and formal re-identification checks.

• #16 Publish monthly research notes on where models fail on real patient journeys

• Y CombinatorLaunch YC post: "Healthcare AI is learning from a cleaned-up version of medicine that barely exists." BioStack turns longitudinal records (notes, labs, imaging, medications, vitals, outcomes) into environments where models diagnose, order tests or adjust medication and are scored against outcomes, guidelines and clinician review; it sells data, evals, reward functions, benchmarks and simulation environments; it reports 17 customers and prospects including top AI labs, after two earlier pivots (multi-agent biology research and wet-lab drug discovery data); it asks for introductions to model builders and to teams that need raw medical data, and offers to send samples.Source
• Competitor researchProtege's homepage: "Real World Data for AI Development" across pre-training, post-training and "evaluation and benchmarks", including healthcare evals; two paths, "Explore data" for model builders and "Become a data partner" for data owners; a SOC 2 badge; a $25M Series A and $30M extension led by a16z.See the research Source
• HiringFounding Research Engineer, RL/Reasoning (San Francisco, $200K–$350K): builds healthcare RL environments (tasks, tool interfaces, reward functions, verifiers, evaluation harnesses), runs SFT, RLVR and RLHF experiments, and analyzes failures to improve environments, rewards and datasets, across clinical reasoning, longitudinal patient care, diagnostic decision-making and chronic disease management.Source

Build a hosted service: labs connect their agent, run episodes against BioStack's clinical environments, and get rewards, verifier results and eval reports, billed by episode or by seat. Patient data stays in BioStack's controlled environment, which simplifies privacy reviews. Start with one environment and two design partners.

Usage-based environment access is recurring revenue, deepens lock-in with training pipelines and answers the privacy objection in data deals.

• Labs insist on local copies

  Offer hosted by default and on-premise for top tiers.

• #13 Package the loop into three offers: sample, dataset license and environment build

• HiringFounding Research Engineer, RL/Reasoning (San Francisco, $200K–$350K): builds healthcare RL environments (tasks, tool interfaces, reward functions, verifiers, evaluation harnesses), runs SFT, RLVR and RLHF experiments, and analyzes failures to improve environments, rewards and datasets, across clinical reasoning, longitudinal patient care, diagnostic decision-making and chronic disease management.Source
• Y CombinatorCompany page: "Real world training envs for healthcare AI models" and "building the post-training lab for healthcare AI"; founded 2025; team of 11; San Francisco; batch P26; two open roles.Source

Each year, publish aggregate statistics from the network: completeness by modality, outcome documentation rates, time between events, differences by country and setting, and what this means for training and evaluating models. Release it with a webinar and a short version for hospital partners.

An annual report earns press, citations and AI-search visibility, and it speaks to both buyers (labs) and supply (hospitals) at once.

• Partners object to publication

  Aggregate only, no named sites, sign-off before release.

• #4 Publish one fact sheet so every article and profile tells the same story

• PressPulse 2.0 (October 23, 2026): a $9.5 million seed from Peak XV, Afore Capital, DCVC, Emerson Collective, SV Angel, Verdict Capital, Streamlined Ventures and Y Combinator; more than 100 million de-identified patient records; a network across the U.S., India, Brazil and Argentina; several hundred thousand X-rays, MRIs and CT scans combined with pathology and other clinical records; funds go to U.S. and international expansion, the platform, and engineering and research hiring.Source
• Competitor researchTruveta's homepage: 140M+ patients "updated daily", longitudinally linked EHR, claims, images and more; an "AI training and validation" use case; 350+ publications; named health-system members and clients.See the research Source
• PressHealthcare IT Today: BioStack announced a "data infrastructure network to power the next era of medicine" that links notes, labs, imaging, medications, treatments and outcomes for population- and hospital-level research.Source

Offer a fixed-scope feasibility report: for a given set of inclusion and exclusion criteria, aggregate counts of matching patients by country and site type, typical treatment pathways and outcomes on standard of care. Deliver aggregates only, under the same governance as data products. Pilot it with one or two biotech customers.

It reuses existing data and pipelines for a buyer with a different budget (clinical operations) and builds relationships with biotech teams that may later buy training data.

• Distraction from the core AI-lab focus

  Time-box to one quarter and two pilots.

• Small cell counts

  Suppress counts below a threshold.

• #9 Explain on one page how patient data is de-identified, governed and delivered

• PressInc42: BioStack works with "multiple hospitals" across the US, Brazil and Argentina, and in India connects cancer patients with an investigational cancer vaccine program; it holds "several million" telehealth audio transcripts; named use cases are AI model evaluation, disease modeling, biomarker discovery, patient stratification and treatment-response analysis; it offers custom workflows to protect patient privacy.Source
• Your websiteHomepage (checked November 9, 2026): headline "Differentiated data that 10X-es AI"; offerings include "Build RL envs for post-training", "Annotate novel / public data" and "Access multi-agent reasoning infra"; audience cards for Biotech Startups, AI Labs and "Human Data Co."; a "Never doubt your data again" section over a fluorescence microscopy image; a cart icon linking to /cart; "Book a consultation" is the only call to action. There is no patient-record count, no hospitals or countries, no customer logos or testimonials and no de-identification claim; four "Backed by" logos carry no alt text.Source