Who needs it first
Requests show which lMIC health teams and proposal writers need the first working loop.
Improves: A tighter request path for LMIC AI-health proposal copilot, with the next owner and outcome visible.
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Health
Shapes AI-health proposals for LMIC settings with fit, evidence, risk, and implementation logic.
In build
App status
In build
Access
Health
App field
What makes it useful
The app is useful when the people, evidence, action, and follow-up are clear enough for someone to move without a private explanation from the Rhiz team.
Useful inputs
The people this should help first
The outcome that would make the app worth using
Current blockers, manual work, or missed follow-through
Field notes, intake responses, feedback, and outcome updates
App flow
The app starts with a concrete need, turns it into a usable next move, and keeps the useful result close enough to reuse.
Collect intake
Capture field signal
Triage risk
Route owner
Record outcome
What gets better
Who needs it first
Requests show which lMIC health teams and proposal writers need the first working loop.
Improves: A tighter request path for LMIC AI-health proposal copilot, with the next owner and outcome visible.
Where people get stuck
Completed and blocked actions show which step is confusing, slow, or missing proof.
Improves: One clearer checklist item, prompt, handoff, or follow-up reminder inside the app.
What becomes reusable
Impact proof patterns strengthen Proposal studio.
Improves: A marketplace primitive that can improve related apps instead of staying trapped in one request.
How Rhiz helps
Shape proposals from objectives, eligibility, evidence, budget, implementation plan, risks, and reviewers.
Capture local observations, measurements, co-benefits, issues, receipts, and outcome proof from the field.
Run benchmarks, rubrics, scorecards, model tests, readiness checks, and reviewer decisions with provenance.
Build faster
Rhiz starts from proven open-source patterns where they fit, then wires the trust, access, and follow-through layer around the people using the app.
Evaluation workbench
Standard language-model benchmark task execution.
integrateField evidence ledger
Android offline field collection and submission workflows.
integrateEvaluation workbench
LLM app evaluation, red-team tests, and comparison workflows.
integrateEvaluation workbench
Large language model evaluation tasks, scorers, solvers, and logs.
integrateProposal studio
Governance proposal flows, participatory processes, and review stages.
studyProposal studio
Guided proposal questionnaires and generated document packets.
studyCompounds with
Related apps share primitives, outcomes, or operating lanes, so useful signal from one request can strengthen the next surface instead of disappearing into a separate backlog.
Field evidence ledger + Evaluation workbench
Shares Field evidence ledger and Evaluation workbench.
In buildProposal studio + Evaluation workbench
Shares Proposal studio and Evaluation workbench.
In buildEvaluation workbench + Proposal studio
Shares Evaluation workbench and Proposal studio.
In buildField evidence ledger + Evaluation workbench
Shares Field evidence ledger and Evaluation workbench.
In build