Utilization review documentation copilot without the backlog.
Build evidence-cited medical-necessity reviews and level-of-care summaries faster while preserving physician-advisor judgment.
Nothing is finalized until a human approves it.
Where the time and money actually go.
Reviewers navigate long records and changing criteria, reconstruct timelines, and manually document why a case meets or fails a level-of-care standard.
UR nurses, case managers, physician advisors
Trigger to act: Concurrent-review queues are late, avoidable days are rising, peer-to-peer workload is high, or audit disputes reveal inconsistent documentation.
The result you can model before you sign.
Illustrative only: 1,200 reviews/month × 8 minutes removed × $48 loaded hourly cost ÷ 60 = $7,680 monthly capacity. Avoidable-day or denial impact requires a controlled local study.
The outcome, plainly: Build evidence-cited medical-necessity reviews and level-of-care summaries faster while preserving physician-advisor judgment.
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, 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.
- Admission and progress notes
- vitals
- labs/imaging
- treatment course
- discharge barriers
- applicable criteria
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.
UR nurse or physician advisor approves every determination and external communication; licensed criteria are used under client contract.
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
10,000–35,000 grounded questions/month plus indexing and vector search.
Planning assumptions, not vendor quotations. Your EHR 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. UR nurse or physician advisor approves every determination and external communication; licensed criteria are used under client contract. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: No autonomous admission-status decision; no replacement of licensed criteria; no external submission without approval; no inference from undocumented facts..
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 $500–4,200/month (10,000–35,000 grounded questions/month plus indexing and vector search), plus a $2,800/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 MCG?
Tools like MCG, InterQual/Optum, Cohere Health 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’s live, and how do we prove it works?
This is a specialist opportunity. We baseline “Review minutes” 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