Clinical trial matching that saves coordinator hours.
Screen candidate patients against protocol criteria with cited evidence, explain uncertainty, and prepare a coordinator review queue.
Nothing is finalized until a human approves it.
Where the time and money actually go.
Eligibility criteria are complex, chart data is incomplete, coordinators manually search records, and potentially eligible patients are missed or contacted too late.
Research coordinators, investigators, patient navigators
Trigger to act: A research site has under-enrollment, multiple active protocols, feasibility pressure, or wants to scale prescreening without replacing investigator judgment.
The result you can model before you sign.
Illustrative only: 10 active studies × 25 coordinator hours/month saved × $42 loaded hourly cost = $10,500 monthly capacity. Enrollment value is protocol-specific and must not be assumed.
The outcome, plainly: Screen candidate patients against protocol criteria with cited evidence, explain uncertainty, and prepare a coordinator review queue.
Inputs in. A cited, review-ready result out. Your expert decides.
A Permission-aware RAG copilot. Every material fact is grounded in an allowed source and returned with its identifier, with no invented data.
Claude Sonnet 4.6 or GPT-5.6 Terra for complex grounded work; select by task-level evaluation; Gemini 2.5 Flash, GPT-5.4 mini or Claude Haiku 4.5 for high-volume routing and drafting. PostgreSQL + pgvector or managed vector store; Policy rules and evaluator service.
- Protocol inclusion/exclusion criteria
- Structured clinical data
- Notes
- Labs
- Medications
- Diagnoses
AI-Native, not autonomous. Judgment stays with your people.
The machine does the work; the human’s role narrows to the one thing that matters, judgment. That constraint is what makes it safe to deploy.
Research staff and the investigator determine eligibility and authorize contact; uncertain or sensitive criteria never resolve automatically.
A scorecard, not a demo. We baseline what breaks in production.
Every deployment ships with an evaluation suite. These are the numbers we baseline before launch and monitor after.
The category is crowded. Most of it isn’t built for your workflow.
Fixed-scope, tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in from day one. A price you own, not a subscription you rent.
It plugs into the stack you already run.
No rip-and-replace. Access is scoped to the minimum data necessary, isolated per tenant, and fully logged.
Transparent by design. The build price buys the workflow and the proof.
A fixed implementation fee plus a monthly bill that scales with volume and governance. No hidden seats.
- ✓ One process / scope
- ✓ Live workflow on your data
- ✓ Baseline evaluation suite
- ✓ Measured vs. current process
- ✓ Full scope & integration
- ✓ Human-review UI & audit trail
- ✓ Write-back to your systems
- ✓ Production evals & monitoring
- ✓ Multi-facility rollout
- ✓ Advanced security & compliance
- ✓ Custom control & escalation
- ✓ Dedicated evaluation program
10,000 to 35,000 grounded questions per month plus indexing and vector search.
Planning assumptions, not vendor quotations. Your CTMS and other platform licenses are separate and owned by you. Figures confirmed during scoping.
“His vast knowledge of technologies and a natural problem-solving mindset consistently lead us through complex challenges with clarity and confidence.”
Questions serious buyers ask.
Does the AI act on its own?
No. Research staff and the investigator determine eligibility and authorize contact; uncertain or sensitive criteria never resolve automatically. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: No final eligibility determination; no patient contact without IRB-approved process; no use outside approved protocol/data permissions; no promise of enrollment outcomes..
How do you stop it inventing facts?
Every material claim is grounded in an allowed source record and returned with its source identifier. The system separates observed facts, model inference, and missing information, and routes to a human whenever confidence is low, evidence conflicts, or an adverse outcome is possible.
What does it cost to run each month?
A usage bill of roughly $500 to $4,200 per month (10,000 to 35,000 grounded questions per month plus indexing and vector search), plus a $2,800 per month managed retainer for evaluation, monitoring and maintenance. Your existing platform licenses are separate and already yours. Exact figures are confirmed during scoping.
Do we need a ChatGPT or Claude subscription?
No consumer ChatGPT or Claude subscription is required for the production workflow. The client needs an approved API/cloud billing account. Workspace seats are optional for internal prototyping and administrator access.
How is this different from Deep 6 AI?
Tools like Deep 6 AI, TriNetX, TrialX are broad platforms you adapt to. This is a fixed-scope implementation tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in, and a transparent price instead of a seat subscription.
How long until it is live, and how do we prove it works?
This is a specialist opportunity. We baseline “Coordinator-review precision” first, then measure against that baseline. You see the scorecard before expanding scope: the evaluation suite ships with the system, not as an afterthought.
Bring your real numbers. Leave with a fixed-scope plan.
A 30-minute engineering-led working session, no slideware. You leave with a sized opportunity estimate, a fixed-scope pilot plan, and the integration & human-review path mapped.
VP of Growth at ViitorCloud · senior delivery owner confirmed before paid work