SolutionAI decision support for a city
เมืองพัทยา · ระบบสนับสนุนการตัดสินใจด้วย AI
A concept for an AI command and decision-support layer across the municipal hospital, city operations and the environment. It reads the city's data, spots what needs attention and drafts the next step for an official to approve.
Sample output from the command centre below. The map is a schematic and every alert is synthetic.
The city
Pattaya runs its own hospital, roads, drainage, waste collection, beaches and back office. Each service keeps its own records. The AI opportunity is to read across them, so officials see one picture and act earlier.
Self-governing under the Pattaya City Administration Act B.E. 2542 (1999). Bangkok is the only other city in Thailand with this status.
City limits run from Naklua through Pattaya Bay to Jomtien, and include Koh Larn offshore.
Hospital beds, bins and roads are planned for residents but also used by large numbers of visitors, and demand shifts with the season.
Heavy rain from roughly May to October brings flash flooding on low-lying roads and extra load on drainage crews.
General public information. Figures and baselines will come from Pattaya City departments during discovery.
What is the current issue?
The issues listed under each area are working hypotheses to test with the departments that own them, not findings.
AI opportunity. Red-flag checks at arrival, a consult note the doctor edits and signs, and AI follow-up of patients at home with alerts to the right person.
See the hospital copilot screenAI opportunity. One live map of the city with what-if scenarios for flooding, traffic congestion and crowd safety.
See the twin scenarioAI opportunity. Photo reports matched to a dog registry, hotspot maps and planned CNVR rounds.
See the stray dog screenAI opportunity. A Thai-language document assistant, request triage and routing, and reports that draft themselves.
See the document assistantAI opportunity. Waste forecasts with re-planned routes, water-quality alerts and an AI-drafted monthly sustainability report.
See the Go Green screenHow can AI help?
Each workflow starts from something that already happens in the city: a report, a forecast, a memo. Models do the numbers, an AI agent joins the pieces and drafts the action, and a named official approves it.
A resident report becomes a matched, prioritised case and a planned CNVR visit.
A resident sends a photo and location to the city account, in Thai or English.
The model finds each dog, checks ear notches and collars, and matches it to the registry.
Duplicate reports are merged; bite history and nearby schools set the priority.
The officer reviews the case card and approves a CNVR or vaccination visit.
The team gets its route, the resident gets updates, and coverage maps refresh.
Tools the AI agent calls
Illustrative function names for a pilot design.
Today
Calls and messages are logged by hand. Nobody can tell which dogs are already sterilised, so teams revisit the same dogs.
With AI
Every report is geotagged and matched. Teams go where unsterilised dogs and bite risk actually are.
The intelligence layer
The use cases share one data layer and one set of AI services, so each new use case reuses what is already built. Data flows up; approvals and audit wrap every step.
Existing systems, connected not replaced
Clean, joined, protected
Models compute, the agent reasons and drafts
Each role sees its own view
What would the user see?
The opening screen is a briefing, not an empty dashboard: what changed overnight, what is at risk and what to do next. Ask a question in Thai or English, and the answer comes back as tables, maps and drafted actions.
Pattaya CityCommand centrePrototypeThe assistant runs scripted logic over synthetic city data, not a live language model. It answers three sample questions; a pilot would answer from real city data.
Hover or tab to a pin for its details.
Hover the chart, or focus it and use the arrow keys, to read each hour.
A daily briefing, city-wide risks and the decisions waiting on them.
Their queue, deadlines at risk and drafted actions to approve.
Arrival and bed outlook, staffing gaps and multilingual intake.
Assigned cases with location, photos and the route for the day.
Report an issue on LINE in their own language and follow its status.
Key screens
Each screen shows the AI output a department would act on, with its own working controls. Every name, number and record here is synthetic and only illustrates the idea.
Use the left menu to move between pages, approve the follow-up plan, then mark the alert as handled.
Hospital copilotDemo provincial hospital (fictional)Tue 13 Oct 2026 · Nurse Anna
Counting today's work…4 steps
The nurse's side of a hospital copilot: red-flag checks at arrival, AI-drafted follow-up questions the nurse approves, fixed rules that raise alerts, and an audit log of every AI and human action. All patients are synthetic and the answers are pre-written.
Pick a dog in the vision match to find it in the photo, then approve the visit and watch the resident's chat update.
Next update is sent once an officer approves the visit.
Add to Saturday CNVR round, Team B: 1 capture for sterilisation, 1 re-vaccination.
No visit planned for this case yet.
Residents report in Thai or English with a photo. Vision matches each dog to the registry, the agent merges duplicates and sets priority, and the officer approves the CNVR plan.
Switch memos to see the routing rule change. Point at an extracted field to find it in the scan, then edit or approve the reply.
ด้วยประชาชนในพื้นที่ซอยพัทยากลาง 122 แจ้งว่าท่อระบายน้ำอุดตัน ทำให้มีน้ำท่วมขังเมื่อฝนตกหนัก3 จึงเรียนมาเพื่อโปรดพิจารณาดำเนินการ
English gloss: Residents of Pattaya Klang Soi 12 report a blocked drain that causes standing water after heavy rain. Submitted for your consideration and action.
Rule matched: the subject mentions ท่อระบายน้ำ (drain).
เรื่อง รับทราบและดำเนินการตรวจสอบท่อระบายน้ำ ซอยพัทยากลาง 12 สำนักการช่างได้รับเรื่องแล้ว และจะส่งเจ้าหน้าที่ตรวจสอบภายใน 3 วันทำการ
“Public Works has received the request and will send staff to inspect within 3 working days.”
The assistant reads Thai official documents, pulls out the fields that matter, suggests where the request should go and drafts the reply in the standard memo format. The division head signs.
Drag hotel occupancy to see tomorrow's waste, the overflow risk and the re-planned routes move together.
3 trucks on re-planned routes, 148 km in total, Koh Larn ferry slot 14:00.
Outlined bars are over what today's fixed routes collect. Hatched: island, served by ferry.
3 streetlight circuits draw more than expected at night: Beach Rd, Thepprasit Rd, Naklua Rd.
September: 12,380 t collected, 1,140 km of truck distance saved by re-planning, and 46 of 48 beach water samples within standard.
A waste forecast per zone feeds a route optimiser, which plans the trucks and the Koh Larn ferry slot. Water and energy readings are watched for anomalies, and each month the assistant drafts the sustainability report for an official to review.
Switch scenarios and drag the slider. Roads, depths and actions recalculate as you move it.
The thresholds are made up; a real twin would run on Pattaya's drainage network, traffic counts and event plans.
What comes next?
This page is the concept and demo preview. Pilot scope, data, budget and timeline are agreed only after Pattaya City has seen the demo.
Focus areas identified.
This page, with working screens on synthetic data.
One or two workflows on sample city data with a live AI assistant.
Update the concept as more information arrives.
Validate the problem, data and needs with the city.
Scope, data, integrations, success criteria, budget, timeline.
Show the idea working on synthetic data, so officials can react to something concrete.
Output: a stakeholder-ready demo.
One or two use cases on real data with one or two departments, running alongside today's process.
Output: a pilot running alongside today's process.
A city platform: more use cases on one shared data and AI layer, hosted in Thailand.
Output: every use case on one platform.
| Use case | Data readiness (assumed) | Delivery risk | Visible to residents | Suggestion |
|---|---|---|---|---|
| Stray dog management | Medium | Low | High | Pilot candidate |
| Internal operations | Not scored | Low | Medium | Pilot candidate |
| Go Green, waste routes | Medium | Low | Medium | Second wave |
| Municipal hospital | Needs system access | Higher, clinical | High | Second wave |
| City digital twin | Needs GIS and drainage | Medium | Medium | Production phase |
A first read, for discussion with Pattaya City. It will change once real data is available.
Built for a city government
A public body will ask who decided, on what evidence and where the data lives. The design answers that from day one, and the list on the right is what we need from Pattaya City before a pilot.
Dispatching crews, spending money or messaging the public always waits for a named official's approval.
Numbers link back to the record, sensor or document they came from. Forecasts show their range, not just one figure.
Personal data is minimised and access is role-based, in line with Thailand's Personal Data Protection Act B.E. 2562.
Who asked what, which data was used, what the AI suggested and who approved it, all kept on record.
Reads and drafts Thai official documents, and serves visitors in English, Russian, Chinese and more.
Runs on city servers or a Thai cloud. The AI model sits behind one adapter, so it can be swapped for an in-country model.
The data and decisions that shape the pilot.
Review the screens with your team.Tell us which workflow to make real first.
Every figure, name and record in the screens is synthetic and illustrative. This is a concept, not an official Pattaya City system.