Artificial Intelligence For Ambulance Dispatch
WEEWOO
They help you on your worst day.
We help get them there faster.
46 sec faster
Avg. P1 response
10.2 min faster
90th % P4 response
203,000 km less
annual fleet travel
Compared to current dispatch practice in an independent simulation on one year of historic data.
AI optimised to
Consistent · Near-instant · Reliable
The Challenge
The stakes
are real
are real
In cardiac arrest and stroke, every minute of delayed treatment costs tissue that does not come back. Ambulance demand keeps rising and paramedic churn is high: only 28% are still on the road a decade after registration, compared to 73% of doctors.
Reducing response times is both hard and expensive.
Reducing response times is both hard and expensive.
10,120 lives
Est. cardiac patients saved in the U.S. p.a. / min faster
How Weewoo fits
The Weewoo
approach
approach
Weewoo integrates with existing Computer Aided Dispatch (CAD) systems. Dispatchers stay in command and accept, modify, or ignore every recommendation.
Weewoo is not a Large Language Model. It is a machine learning algorithm built for ambulance dispatch: it cannot hallucinate.
Weewoo is not a Large Language Model. It is a machine learning algorithm built for ambulance dispatch: it cannot hallucinate.
Weewoo changes which unit is sent. It does not affect triage and it does not affect routing.Does not affect triage or routing.
Ambulance Service
Our API
01Caller
02Call Centre
03Dispatch Centre
04Weewoo
05Crew & Vehicle
06Incident
Weewoo's Operational Impact
Engineered
for the field
for the field
Independently Validated
Via an independent simulation on real data.Via an independent simulation using a full year of historic emergency data. .
Less Time On The Road
Lower running costs, less vehicle wear, and less of a paramedic's shift spent driving.
Breaks And Shift Ends Protected
Dispatchers decide whether crews get home on time.Dispatchers decide whether a crew gets a break or home on time. Weewoo improves the rate of both, reducing paramedic churn.
20-unit fleet KPI improvements
Annual fleet travel
−203,000 km
Paramedic driving hours
−5,000 hrs
Shifts running 2 hrs+ over
−22.6%
Missed meal breaks
−25.2%
Breaks taken in window
+3.4%
From one year of data of a twenty-unit fleet serving ~550,000 people, measured against Weewoo's benchmark approximating the service's own dispatch procedure.
Weewoo's Patient Impact
Real change
real lives
real lives
Weewoo reaches patients sooner across every priority class. It improves the slowest responses most, which is where patient harm sits.
~80 lives
Est. cardiac lives saved a year, New Zealand
~87 years
Est. stroke brain aging avoided a year, New Zealand
Response time KPI improvements
Priority
Average
90th percentile
P1 (n=726)
46 sec
51 sec
P2 (n=22,356)
14 sec
51 sec
P3 (n=27,130)
3.9 min
12.6 min
P4 (n=5,805)
4.5 min
10.2 min
The 90th percentile measures a service's slowest responses and is the figure most services are formally held to.
Weewoo's Values
Built with patients
front of mind
front of mind
01
Reliable
Weewoo cuts response times across every priority class, not only the most urgent.
02
Intelligent
Weewoo works within your existing standard operating procedure when it chooses a response.
03
Transparent
Every recommendation is auditable and explainable. Dispatchers stay in command, and can always override.
04
Resilient
A system that fails when it is needed most is worse than no system. Built for observability and failover.
The Weewoo Team
People who've
been there
been there
Weewoo
Research
JM
Jordan
MacLachlan
MacLachlan
CEO & Co-Founder
An ambulance patient of nearly 15 years that wants to give back. PhD in Machine Learning for Emergency Medical Dispatch, 2026.
SM
Scott
MacLachlan
MacLachlan
COO & Co-Founder
Ex-CFO of several Kiwi startups. One of four that started Kiwibank.
NC
Neil
Clayton
Clayton
Head of Engineering & Co-Founder
Three decades of shipping systems that do not fail — the kind of experience only hard lessons give you.
RH
Rob
Higgs
Higgs
Head of Applied Research & Co-Founder
Software engineer in machine vision and compression. Central to achieving independent validation.
JR
Joe
Robertshaw
Robertshaw
Research Engineer
Former Honours student and Research Assistant. The first person to join outside the founders.
AT
Alan
Teesdale
Teesdale
Research Engineer
Masters student and former Research Assistant, with strong PhD prospects.
You?
Independent
Chair
Chair
Currently Open
We're looking for an independent chair with international SaaS and healthcare experience. Get in touch.
You?
Engineers &
Scientists
Scientists
Currently Open
If you think you'd be a great fit for our team, we'd like to hear from you.
YM
Yi
Mei
Mei
Professor
Primary PhD Supervisor, Lead Researcher. AI/ML for Optimisation and Decision Making.
FZ
Fangfang
Zhang
Zhang
Senior Lecturer
Secondary PhD Supervisor. AI/ML for non-stationary combinatorial optimisation problems.
MZ
Mengjie
Zhang
Zhang
Professor
Tertiary PhD Supervisor. Director of the Centre for Data Science and Artificial Intelligence.
You?
Researchers &
Scientists
Scientists
Currently Open
Working on optimisation, scheduling, or dispatch? We'd like to hear from you.
Weewoo's Supporters
Backed by those
who get it
who get it
Research & Industry Partner
Ambulance Service
Ambulance Service
Research Partner
Research Partner
REDACTED
Industry Partner
REDACTED
Industry Partner
REDACTED
Ambulance Service
Milestones
What we've done,
and what's next
and what's next
2027
NextDeploy
- Six-month parallel run (Q1/2).
- Go-live mid-2027, following clinical sign-off.
- Weewoo dispatch recommendations live in New Zealand.
- Onboard a second ambulance service.
2026
NowBuild & Test
- Phase One investment received (Q2).
- Letter of Intent signed with our integration partner (Q2).
- Steering committee established with our health and ambulance partners (Q2).
- PhD completed (Q3).
- Integrating with CAD (Q3/4).
2025
CompleteValidate
- Company Founded.
- Completed API with third-party simulation engine.
- Social and operational benefits higher than expected.
- Developed several industry partnerships.
2024
CompleteDevelop
- Presented results to an Australian service and the WFA Board.
- Starting to understand social and operational opportunity.
- Third-party validation requested by Te Whatu Ora - Health New Zealand.
2023
CompleteResearch
- Research Team receives NZ$1m research grant.
- Intellectual Property secured.
- 10x efficiency improvement.
- First experiments on historic data.
2022
CompletePartner
- First experiments on randomly generated data.
- Partnership with Wellington Free Ambulance (WFA).
- 10x efficiency improvement.
2021
CompleteStart
- Research Team Founded.
- Jordan started his PhD at Te Herenga Waka - Victoria University of Wellington in Machine Learning for Emergency Medical Dispatch.
Get in Touch
Let's talk about
saving lives
saving lives
If you would like to learn more about Weewoo, please get in touch!
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