AI-DNO models facilities, devices, transport routes, and demand so you can design cost-efficient diagnostic networks—whether you are a Pro or Ultra solo planner or an Enterprise team.
Placing devices and routes without scenario math means higher spend, longer turnaround, and communities left behind.
Facility lists and legacy placements decide where diagnostics live—while demand, transport, and equity stay invisible.
Blind device placement
Machines sit idle in some sites while others queue samples for days.
Hidden cost trade-offs
Budget debates happen without clear coverage vs. spend scenarios to compare.
Inequitable access
Hard-to-reach populations wait longest for testing that should be nearest.
Simulate facilities, devices, hubs, and routes—then choose designs that balance cost, turnaround, and access.
Scenario modeling
Compare baseline vs optimized networks before you commit to a redesign.
Cost & access clarity
See coverage, TAT, and budget impact side by side—not in separate spreadsheets.
Agent-grounded recommendations
Ask the DNO agent for decisions tied to your project data, not generic advice.
AI-DNO makes placement, routes, and budget trade-offs visible—so you redesign diagnostic networks with evidence, not guesswork.
Legacy networks leave machines idle in one place and queues elsewhere. Scenario modeling puts capacity where demand and equity gaps actually are, before you commit spend.
Compare baseline vs optimized placements before you move a single device.
See coverage, turnaround, and budget impact together—not in separate sheets.
Model sample flow so hard-to-reach sites stop waiting the longest.
AI-DNO turns facilities, devices, and routes into scenarios you can compare before redesign.
Put capacity where demand and equity gaps actually are—shorten queues where it matters.
Work solo on Pro or Ultra, or with your org on Enterprise—the same optimization engine, the right collaboration model.
Ask for recommendations tied to your project network—not generic templates.
AI-DNO makes placement, routes, and budget trade-offs visible before you redesign diagnostic capacity.
01
Capture facilities, devices, transport routes, demand, and existing diagnostic capacity.
02
Compare baseline vs optimized designs for cost, turnaround time, and equitable access.
03
See where machines sit idle, where queues form, and which moves close access gaps.
04
Share plans in individual or enterprise workspaces so stakeholders align before spend.
Every idle device and every delayed sample is a design choice. AI-DNO makes those trade-offs visible before you redesign.
| Capability | Status-quo networks | With AI-DNO |
|---|---|---|
| Device placement | History and politics decide locations | Baseline vs optimized scenario modeling |
| Budget decisions | Cost debates without coverage math | Side-by-side cost, TAT, and access impact |
| Equity | Hard-to-reach sites wait the longest | Access gaps surfaced in network design |
| Transport | Fixed routes that ignore demand shifts | Hub and route designs matched to flow |
| Collaboration | One planner’s spreadsheet, hard to share | Pro, Ultra, or Enterprise workspaces |
AI-DNO supports individual and enterprise workspaces so facility and device data stays under your control.
Pro, Ultra, and Enterprise plans stay separated so organizational data is not mixed by default.
Share scenarios with the right stakeholders—without exposing raw network details broadly.
Device, route, and demand inputs are treated as operational assets, not public artifacts.
Scenario comparisons stay transparent so budget and placement decisions can be audited later.
A side-by-side view of common DNO analysis tools — license, skills, timeline, and data history requirements.
| Characteristic | AccessMod | OptiDx | BI (Power BI / Tableau) | AI DNO |
|---|---|---|---|---|
| License fee | Open source | Open access | License required | Subscription (hosted platform) |
| Skillset required | GIS analyst | Supply chain analyst | Data analyst | Programme manager (self-serve UI) |
| Typical timeline | 2–3 months | 3–6 months | Depends on analysis | Minutes to hours per scenario |
| DNO history | Encouraged | Required | Depends on analysis | Not required |
| Further resources | UNIGE AccessMod docs | OptiDx.org guides & SOPs | Vendor training & dashboards | In-app guide + AI agent support |
Characteristics for AccessMod, OptiDx, and BI (Power BI / Tableau) align with the Stop TB Partnership DNO Implementation Guide (Annexure C). AI DNO reflects the Froncort platform.
AI-DNO supports solo planners on Pro or Ultra, and Enterprise teams — same optimization engine, the right workspace model.
Design and compare diagnostic network scenarios on your own—without organization setup.
Higher usage caps and a larger network footprint for advanced solo diagnostic planning.
Collaborate across your organization with shared projects, members, and invites.
Pro includes 20 scenario optimizations per month. Need more? Purchase add-on packs anytime from your billing dashboard.
Create your AI-DNO workspace, or reach out to explore enterprise collaboration.
How scenario modeling helps you redesign diagnostic networks before you move devices.