Three numbers shaping biomedical R&D
Your peers are already using AI to move faster. The hard part for a biomedical team is doing it with your IP off the cloud and your audit trail beyond question, all inside your own lab. Three numbers first:
In the lab: the two problems
Formulas can't go to the cloud. Audits can't go wrong. Across drug discovery, regenerative medicine, and glycoengineering teams, two things come up before anything else.
- Your researchers run literature and analysis through ChatGPT and Claude every day. Paste in a formula or a protein structure, and no one can promise it won't become someone's next training data.
- Every FDA or TFDA inspection burns weeks just pulling together the audit trail, batch records, and e-signatures. The moment a record's integrity is questioned, the whole dataset is in doubt.
Research IP and data sovereignty: air-gap capable
ZS's 2025 survey of 127 senior biomedical, pharma, and life-sciences technology executives found that privacy and regulatory concerns make many companies hold back on open LLMs. On average, only about a third of staff in each department have company-approved access to gen-AI tools. Once a formula or a protein structure goes into a public-cloud model, you can't pull it back.
MICH keeps every byte of research data on the on-prem LowsiteOS box and the desk-side GB300 workstation, with zero cloud dependency. Models like OpenFold3 and Boltz-2 run on local GPUs and send nothing out. Model and agent updates come in offline, and the deployment can be fully air-gapped. Your researchers can still use ChatGPT for public lookups, while the confidential work stays entirely inside the lab.
How teams in Taiwan and the U.S. collaborate
For a multi-site biomedical, the research IP has to stay local, but the sites still need to share progress, and syncing raw data to the cloud or to each other's servers just doubles the IP exposure.
MICH deploys a GB300 workstation at each site, keeps the raw research data local, and syncs only structured conclusions through LowsiteOS's built-in Knowledge Sync, never the raw data. The sync runs over mTLS with customer-held keys, within HIPAA Business Associate boundaries.
What it solves: three scenarios
The AI-in-drug-discovery market is set to grow from about $1.7B in 2024 to about $8.5B by 2030 (~30% CAGR; market estimate). Here are the three scenarios where MICH puts that to work in the lab.
Compliant, connectable — and it won't stall in a pilot
Speed doesn't matter if it can't pass GxP or connect to the tools your researchers already use. This part is compliance and integration.
GxP and 21 CFR Part 11: built to the principles
GxP compliance is the first gate in any biomedical purchase, and data integrity is the most common finding in recent FDA inspections. Buyers need verifiable, mappable evidence; saying "compliant" isn't enough.
LowsiteOS is built to the principles of FDA 21 CFR Part 11, EU GMP Annex 11, and ICH Q9, and provides a Compliance Mapping Document, IQ/OQ/PQ validation templates, and a sample audit-trail export for inspectors to check against. It does not yet hold third-party GxP certification; formal certification is on the 2027 roadmap (with a body such as BSI or SGS).
Moving over from ChatGPT and Claude
Researchers have used ChatGPT and Claude for literature and early analysis for a while now, and the biggest barrier to moving them onto on-prem tools is the fear that all that accumulated context resets to zero.
MICH provides Memory Migration, importing the research conversations and reasoning from ChatGPT and Claude into LowsiteOS in structured form, so researchers keep their working context after switching to the local LLM. For non-confidential public lookups, they can keep using cloud tools; MICH doesn't force a full replacement.
Connecting LIMS, ELN, and document formats
MIT NANDA's State of AI in Business 2025 finds that buying an integrated solution from a specialist succeeds about two-thirds of the time, versus about one-third for assembling one in-house. Most biomedical teams have run AI pilots, and the place they stall is connecting to existing LIMS, ELN, and messy document formats.
MICH handles PDF/Word/Excel through LowsiteOS's built-in Document Parser, and connects LIMS and ELN via connectors (Benchling and LabArchives exports verified); handwritten batch records go through an OCR-plus-LLM cleanup pipeline (about 85% accuracy, still needs human review). Deployment starts from a single-scenario PoC that produces verifiable results on your real data, with no need to go broad all at once.
Cost, payback, and protecting the investment
Last comes money and the long term: what you spend, how fast it pays back, whether the next GPU generation makes it obsolete, and whether an overseas lab can get support.
On-prem vs cloud GPUs: where it saves, when it pays back
McKinsey's The cost of compute estimates cumulative global AI-infrastructure capex reaching $6.7 trillion between 2025 and 2030. Metered cloud compute has no ceiling, the bill climbs month over month, and your research IP still goes to the cloud.
MICH's on-prem deployment, with hardware and software in one purchase, is a one-time capital cost that is predictable from day one. Against on-demand cloud GPUs (e.g. AWS p4d, 8×A100 at about US$32.77/hr, ~US$78K/year at 200 hrs/month), on-prem costs less over a three-year amortization; payback typically lands around 12 months (a 9–14 month range, depending on utilization, based on customer pilots and internal estimates).
A first usable model in 8–12 weeks
6sense's survey of nearly 4,000 B2B buyers found 83% had largely decided what they needed before contacting a vendor, and biomedical's procurement and security-review cycles run longer than most. Buyers need a clear answer to "how soon will I see results."
MICH's biomedical PoC typically runs 8–12 weeks, with a first usable model running on your real data within that window, and the GxP compliance-mapping documents delivered within 12 weeks. The PoC includes a free M1 loaner with no commitment, so you validate first, then decide. Full pricing comes through booking a Demo, and the hardware tiers are on the AI Server product page.
Hardware upgrades and overseas support
Research institutions usually buy equipment on a 3–5 year cycle, and the real fear is buying in only to be left behind by the next GPU generation two years later, with no local support.
MICH's desk-side machines (Pro-6000, GB300) are modular, so GPU and CPU cards upgrade independently; rack-scale HGX, with its tightly integrated baseboard, needs a baseboard swap for a GPU upgrade. Support today covers Taiwan (NBD on-site), the U.S. (through the Supermicro distribution network, NBD parts plus remote), and Japan (partner distributor); other regions are scoped case by case.
Start with one scenario. See a first usable model in 8–12 weeks. Tell us the scenario you want to start with (sovereign R&D compute, document knowledge, or GxP audit logs) and the tools you run (LIMS/ELN/document formats), and we'll set up a live demo on real research data.