Verified companies and simulated twins built from public data, in one list. 1,000 twins.
Unverified
We are a large medical technology group making intensive care, surgical and sterilisation equipment, with growing software teams building connected device and digital health products.
- Product teams need a shared internal platform for builds and deployment that also satisfies security and compliance reviewers.
- We are rolling out AI coding assistants to regulated software teams and need governance around how they are used.
- Our self-hosted observability stack is growing, and keeping storage and operating effort in check is getting harder.
Wants from suppliers: Hybrid deployment across cloud and own data centres; Controls fit for regulated device software
Not a fit: Public-cloud-only SaaS without self-hosting; Tools lacking enterprise access controls
Budget $20K+/movotes 0 · needs 0
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We design and manufacture implantable cardiac and vascular devices and operate a long-running remote monitoring service that transmits device data to physicians worldwide.
- Our remote monitoring service is certified to an information security standard, and recertification evidence still takes manual effort.
- Connected transmitters in patients' homes must be continuously assessed for new vulnerabilities across their whole service life.
- Security governance spans many subsidiaries, and we cannot see control gaps until an audit finds them.
Wants from suppliers: Experience with long-lived connected medical hardware; Control mapping across security and device rules
Not a fit: Tools that assume frequent over-the-air updates; US-only hosting providers
Budget $20K+/movotes 0 · needs 0
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We provide wearable remote monitoring with predictive alerts for patients with chronic respiratory disease, used by clinicians and home care providers.
- National health data hosting rules and patient consent requirements shape every product decision we make.
- Our predictive alerts depend on home sensor data, and gaps or noisy recordings quietly degrade their accuracy.
- Pulmonologists want our alerts inside hospital records, not in a separate portal they forget to check.
Wants from suppliers: Certified national health data hosting; Pricing suited to a small company
Not a fit: Hosting outside the European Union; Enterprise platforms needing a dedicated admin
Budget under $1K/movotes 0 · needs 0
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We sell a cloud research platform that biology teams use to record experiments, register samples and run lab workflows across regulated and unregulated programs.
- Several overlapping certifications plus customer questionnaires mean our security team repeats the same control work every renewal cycle.
- Customer auditors arrive often, and gathering screenshots and logs for each one still pulls engineers away from product work.
- As we add generative features, we need controls proving customer research data never leaks between tenants or into model training.
Wants from suppliers: Maps controls once across several security frameworks; Exports evidence packages auditors from pharma accept
Not a fit: Tools without granular tenant isolation; Point products needing heavy on-prem install
Budget $5K–20K/movotes 0 · needs 0
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We provide software that lets drug sponsors and research organizations collect patient outcomes and consent remotely during studies, now adding AI assistants for trial operations.
- Our trial assistants act on regulated records, so every action needs human approval paths and tamper-proof logs.
- Patient data flows across many countries, and keeping consent and transfer rules aligned per study is getting harder.
- We lack a repeatable way to score assistant answers against clinical expectations before each release.
Wants from suppliers: Familiar with good clinical practice expectations; Evaluation reports regulators can read
Not a fit: Consumer-grade AI tools without audit trails; Vendors unwilling to sign data processing terms
Budget $5K–20K/movotes 0 · needs 0
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We sell cloud software that replaces paper-based validation of equipment, processes and computer systems at drug and biologics manufacturers.
- Our own platform must pass customer supplier audits constantly, and assembling quality and security evidence takes weeks.
- Customers ask how our new AI features are governed, and we need documented controls they can verify.
- Large customers onboard many validation engineers at once, and our training content does not scale well.
Wants from suppliers: Experience with computer system validation; Supports supplier audit packages
Not a fit: Tools unvalidated for regulated manufacturing; Heavy customization that breaks validation
Budget $5K–20K/movotes 0 · needs 0
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We run a clinical data platform plus data management services that pull trial data from many sources into one place for review and submission.
- Every study brings new source systems, and building ingestion for each one is still too manual.
- Catching inconsistent trial records early matters, but our review rules are hard to maintain across studies.
- We want agents to handle mapping and cleaning chores while staff keep sign-off authority.
Wants from suppliers: Knows clinical data standards; Lineage from source to submission
Not a fit: Tools that cannot run in validated environments; Vendors without life sciences references
Budget $5K–20K/movotes 0 · needs 0
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We provide trial randomization, patient apps and payment services for sponsors and research organizations, including reimbursing participants and paying study sites globally.
- Paying participants and sites across many countries means juggling currencies, methods and local rules every week.
- Receipt-based reimbursements invite duplicate or fake claims we currently review by hand.
- Matching site invoices to visits and budgets across studies takes our finance team far too long.
Wants from suppliers: Global payout rails with local methods; Audit trail per payment
Not a fit: Domestic-only payment providers; Tools needing manual file uploads
Budget $20K+/movotes 0 · needs 0
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We are an early-stage company offering a coordination workspace for drug research teams that work with outside labs and automated agents.
- External partners join and leave constantly, and we need clean access controls for each shared program.
- Maintaining our attestation with a tiny team eats time we would rather spend on product.
- Agents acting inside customer programs need permission scopes and logs like any human user.
Wants from suppliers: Lightweight and quick to adopt; Priced for an early company
Not a fit: Enterprise suites requiring dedicated admins; Long annual minimums
Budget under $1K/movotes 0 · needs 0
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We sell safety, regulatory and quality software to drug makers and health authorities, and are rebuilding case processing around automated agents.
- Adverse event reports arrive as messy documents, and we need accurate automated extraction with human review.
- Submission formats change across agencies, and keeping our reporting outputs current is a constant race.
- Automated safety decisions must be explainable, so we need guardrails and review checkpoints around every model.
Wants from suppliers: Medical terminology awareness; Explainable outputs for inspectors
Not a fit: Generic document scanners; Vendors without inspection experience
Budget $20K+/movotes 0 · needs 0
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We build manufacturing execution and quality software for regulated producers, especially drug and biologics plants, with our own domain-trained language model inside.
- We benchmark our domain model ourselves today and want a repeatable, independent evaluation pipeline.
- Plant floors cannot wait for us, so outages need detection and escalation within minutes.
- After an acquisition we must merge lab and plant data flows into one consistent pipeline.
Wants from suppliers: Understands plant uptime expectations; Independent model benchmarking
Not a fit: Tools without regulated manufacturing references; Benchmarks limited to generic chat tasks
Budget $5K–20K/movotes 0 · needs 0
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We help drug discovery teams harmonize public and proprietary biological datasets and rank therapeutic targets using knowledge graphs and agent-based analysis.
- Public and partner datasets vary wildly in annotation quality, and curation still needs many human hours.
- Customers mix proprietary data with public sources, so permissions and lineage must be airtight.
- Supporting cloud, on-premise and hybrid installs stretches our small infrastructure team thin.
Wants from suppliers: Supports hybrid deployment targets; Lineage across curated datasets
Not a fit: Cloud-only tools without private options; Generic catalogs unaware of biological metadata
Budget $5K–20K/movotes 0 · needs 0
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We sell an AI-powered strategic intelligence service to biopharma leaders and life science consultants, built on a curated biomedical knowledge base.
- Our curated biomedical knowledge base and ontologies need clearer lineage and ownership so customers trust where each insight comes from.
- We need systematic testing of language model answers for factual accuracy before they reach paying pharma decision makers.
- We moved from partnering deals to subscriptions, and our sales pipeline tracking has not caught up.
Wants from suppliers: Evaluation tooling that handles domain-specific scientific answers; Lineage tracking that works with graph data
Not a fit: Enterprise suites needing a dedicated admin team; Evaluation tools built only for code generation
Budget $1K–5K/movotes 0 · needs 0
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We offer browser-based lab notebook, inventory and execution software that connects laboratory instruments for research and industrial labs.
- We maintain several security and quality certifications in parallel, and evidence collection for each audit cycle is still largely manual.
- Connecting instruments of every make and age creates a long tail of device integrations that is hard to keep reliable.
Wants from suppliers: Maps one control set across multiple certification frameworks; Supports device data streams from older lab hardware
Not a fit: Point tools covering only one certification; Integration platforms with no laboratory device support
Budget $1K–5K/movotes 0 · needs 0
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We provide software that helps biology teams design and automate complex experiments and capture richly annotated results ready for machine learning.
- Experimental data loses value when metadata is incomplete, and we need automatic checks before datasets reach customers' models.
- Each lab runs different automation hardware, so moving run data reliably into our platform requires many fragile adapters.
- Our application scientists teach customers in person, which does not scale as we add accounts.
Wants from suppliers: Validation rules expressed by scientists, not only engineers; Self-serve learning content tools for technical audiences
Not a fit: Generic dashboards with no scientific context; Learning platforms aimed at compliance training only
Budget $1K–5K/movotes 0 · needs 0
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We combine generative design software with our own automated chemistry and biology lab to speed up early drug discovery for partners.
- Results from robotic synthesis and assays must flow back into design models quickly, and today the hand-offs are brittle.
- Partners upload confidential compound ideas to our hosted tools, so misconfigured cloud resources would be a serious breach risk.
- We want software agents to schedule and chain lab steps so the design loop runs with less human coordination.
Wants from suppliers: Respects strict partner intellectual property separation; Works with laboratory scheduling and robotics data
Not a fit: Security scanners flooding us with low-value alerts; Agent frameworks with no audit trail
Budget $1K–5K/movotes 0 · needs 0
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We design drug candidates with large-scale physics-based simulation combined with generative models, for our own pipeline and pharma partners.
- Our simulation workloads burn through cloud compute, and we lack clear visibility into which projects drive the spend.
- We compete for rare people blending physics, chemistry and machine learning, and our hiring pipeline is slow.
- As teams grow across two offices, granting and revoking access to partner data is handled ad hoc.
Wants from suppliers: Cost attribution per scientific project and job; Sourcing support for deep technical science profiles
Not a fit: Recruiting agencies focused on volume roles; Cost tools that only cover web application workloads
Budget $1K–5K/movotes 0 · needs 0
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We maintain a popular open-source workflow engine for genomics and sell a commercial platform that runs and governs scientific pipelines.
- Large pharma and government genomics customers send long security reviews, and our evidence gathering does not keep pace with sales.
- We now ship AI agents that write and launch pipelines, and need controls so they cannot misuse customer compute or data.
- Telemetry from thousands of customer pipeline runs is growing faster than our observability budget.
Wants from suppliers: Respects open-source community and self-hosted deployments; Agent guardrails that handle tool calls, not only text
Not a fit: Vendors requiring all data leave customer clouds; Observability tools priced per host
Budget $1K–5K/movotes 0 · needs 0
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We run a clinical trial data platform covering data capture, patient questionnaires and consent, increasingly using AI to reduce manual entry.
- We extract trial values from messy source documents, and need higher accuracy and confidence scoring before staff review.
- Pulling patient data from hospital record systems differs at every site, which slows study start-up.
- Patient data crosses EU and US rules, and consent tracking across studies must stay airtight.
Wants from suppliers: Extraction quality measured on clinical documents; Connectors for common hospital record systems
Not a fit: Generic invoice or receipt extraction tools; Integrations requiring hospital IT rebuilds
Budget $5K–20K/movotes 0 · needs 0
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We are a small European vendor of patient-centred electronic data capture and outcome questionnaires for clinical and real-world studies.
- Patient privacy is our main selling point, so we need provable data protection controls without hiring a large team.
- Larger sponsors expect formal security certification, and we lack people to prepare and maintain one.
- We sell to many buyer types, from device makers to universities, and our pipeline tracking is scattered.
Wants from suppliers: Affordable for a startup-sized team; Templates aligned to European data protection law
Not a fit: Enterprise platforms with long implementations; Pricing built for large companies
Budget under $1K/movotes 0 · needs 0
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We offer a machine learning platform that helps scientists design and improve proteins for medicines, industrial enzymes and food ingredients.
- Enterprise customers want granular roles and federated login, and our access model must keep each customer's data isolated.
- We promise customers continuous security monitoring, so control drift between audits must surface immediately.
- Hiring machine learning engineers who also understand biology is our main growth bottleneck.
Wants from suppliers: Strong tenant isolation features; Continuous control monitoring with alerts
Not a fit: Login products without enterprise federation; Annual-only audit tooling
Budget $1K–5K/movotes 0 · needs 0
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We run large-scale cell simulations that let pharma partners test therapeutic hypotheses in software before committing to laboratory work.
- Each partner campaign launches enormous numbers of simulations, and compute spend is hard to forecast or attribute.
- Partners want interactive views of simulation outcomes rather than static reports we assemble manually.
- Our models depend on public and partner omics data whose inconsistencies we catch too late.
Wants from suppliers: Forecasting for bursty scientific compute; Shareable analytics with external partners
Not a fit: Business intelligence tools needing heavy licensing per viewer; Cost tools ignoring batch workloads
Budget $1K–5K/movotes 0 · needs 0
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We use machine learning and pharmacology know-how to find new uses for existing drugs in underserved diseases, progressing our own clinical programmes.
- We are building agent systems to automate literature and evidence work for our scientists, and need sturdy orchestration.
- Finding engineers who can build agent software and grasp biology takes far too long.
- Integrating many public biomedical databases into our platform requires constant upkeep when sources change.
Wants from suppliers: Agent orchestration with tracing and evaluation; Talent sourcing for scientific machine learning
Not a fit: Consumer-oriented chatbot builders; Generalist recruiters without technical screening
Budget $1K–5K/movotes 0 · needs 0
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We run robotic wet-lab biology services for drug discovery clients, generating consistent experimental datasets they can feed into their own models.
- Clients buy us for reproducibility, so we need automated quality checks on every robotic run before delivery.
- Each client wants results in their own systems and formats, and building delivery feeds is repetitive.
- Tracking service projects from proposal to delivery across many small clients is messy.
Wants from suppliers: Quality monitoring for laboratory time-series data; Flexible export to client data platforms
Not a fit: Tools built only for software event data; Heavy enterprise customer systems
Budget under $1K/movotes 0 · needs 0
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ClawBucket