AI clinical research · Agent-to-agent
We don't design molecules. We prove them. Our agents work the same trial from both sides of the hospital wall. No patient record moves.
Hospital agents
Next to the source data.
Sponsor agents
Next to the decision and the budget.
Why this matters
The clinical stage holds most of the time and most of the money. It is still run by people reading documents.
Fig. 01 · the arithmetic of one approved medicineclinical stage in teal
10 years to approval
Six are clinical. A year late costs a year of patent-protected sales.
$1B per approved drug
Over half is clinical. Site monitoring alone takes a quarter of trial cost.
1 in 10 reaches the market
Nine in ten never finish, usually only after running to the end.
Everyone sells cost reduction. The real number is time, and it is two orders of magnitude larger.
Fig. 02 · what one Phase III decision is worthsame linear scale, $0 to $5B
Cost leverCut 20–30% off a Phase III
Time leverReach the market one year earlier
≈ 100× the cost lever · and a year of patients treated
A2A · agent to agent
Evidence sits in hospitals. Decisions and budgets sit in sponsors. We left the wall between them standing and put agents on both sides.
Fig. 03 · the A2A layerfive message types cross · four never do
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Automated source-data verification never writes back into the medical record. A person resolves every query.
The network
Five are validated end-to-end in live, paid deployments. The rest are being validated one at a time.
★ Validated end-to-end in production. 22 specialty agents live at a leading tertiary GCP centre.
The platform
By hand, a study database costs a data manager three to four weeks. We parse the protocol once, and everything else falls out of it.
Fig. 04 · protocol to EDC-ready databaseone pass, no hand-written rules
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Building it by hand
3–4 weeks
Read the protocol, design the tables, write the rules, debug.
On the platform
under 3 hours
One parse. Each square is a working day.
THGAgents
Traceable causal graphs, reasoning over paths, hypotheses that iterate. Built for what general models handle worst, literature that contradicts itself.
AutoBioResearch · BioOpenClaw
Generates hypotheses, verifies them in parallel, catches its own contradictions, and writes up once the evidence converges.
Clinical rules live outside the model. The model only learns judgement. That is what makes the output explainable, portable, and safe to put in front of a regulator.
Evidence
Not pilots. Three constituencies, each with something at stake.
01 · Hospital
A leading tertiary GCP centre
40+ agents live against real trials, across six disease areas.
02 · Sponsors
Global top-10 pharmaceutical companies
Five of the world's ten largest buy from us, at 100% renewal.
03 · CRO
A supplier, not a competitor
CROs sell people. We supply agents, and we sell them to CROs too.
Fig. 05 · before / after
Recruitment window
Data-entry time
90%+ precision and recall on structuring, patient matching up 41%, zero records out.
Fig. 06 · before / after
Protocol draft to sign-off
Investigator review effort
First draft in 30 minutes, 99% accuracy on three-way verified review, 100k+ scattered preclinical records made queryable.
Fig. 07 · before / after
Adverse event report processing
0
people supplied
Priced per field of output, not per person-month or per visit.
The moat
Not a list of strengths, a list of gates. Each one has stopped a well-funded competitor.
Getting into the hospital
Six to twelve months through IT, ethics and clinical review. Live at a leading tertiary GCP centre.
Compliance and data governance
Data cannot leave, and structuring free text is unglamorous work. Models train locally at 90%+ precision and recall.
Drug development, medicine and AI at once
Each field locks the other two out. A pharma R&D veteran, a Stanford AI and ex-Google scientist, and senior advisors, in one team.
The investigator's trust
If the investigator does not sign, the AI has done nothing. Review effort down 60%, 99% verified accuracy.
Sponsors who actually pay
Twelve to twenty-four months of procurement, legal and audit. Five of the global top ten pay, at 100% renewal.
Only AI, and you never get through the door. Only hospital relationships, and you never ship. No investigator, no sponsor, and it was only ever a demo.
Who we work with
This only works if both ends are real.
Pharmaceutical partners
Academic & hospital partners
The hospital gets research capacity and GCP throughput. We get the right to develop against the data. Both are written in before anything is installed.
Where this goes
Every trial leaves data behind, real records plus the judgement of the physician who corrected the draft. Each step moves us earlier.
Fig. 08 · where each capability actsthe intervention moves earlier
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Team
A hard team to assemble, which is most of why it works.
Founder & CEO
Xincheng Zhang
Co-founder & Chief Scientist
Kevin Jin
Medical advisor
Twenty years in multinational pharma. Previously head of medical affairs at a top-10 company.
Scientific advisor
Chair professor of cell biology at a leading European university. An authority on Alzheimer's and ageing.
Hospital partnerships
Fifteen years of hospital partnerships. Previously an executive at a major hospital IT company.
Our vision
Better medicines, reaching the patients who need them, sooner.
Every month a trial runs longer is a month someone waits. Everything else on this page is a way of getting to that number.