Strengthening national police coordination through machine learning innovation
Informed Solutions has worked with the National Police Coordination Centre (NPoCC) to enhance its national Mercury mutual aid platform: the data-led solution that enables the fast, coordinated deployment of officers and resources across the UK during planned and unplanned events and emergencies.
Through a series of exciting innovations in data analytics, machine learning, and knowledge management, NPoCC has enhanced its capability to enable it to better understand and respond to national capacity, capability, and wellbeing data in real time.
These innovations are helping NPoCC deliver its national resilience mission through enhanced insight, as well as improved speed and confidence in decision making.
NPoCC plays a critical role in coordinating policing capability between police forces, ensuring the right resources, with the right skills, and specialised assets are deployed effectively when national-scale events or emergencies occur.
Mercury already held rich data about policing capacity, capability, and mutual aid. However, this information was stored in a variety of formats and systems, making it difficult to access analysis and insights quickly.
Planners and decision-makers needed:
The goal was explore how Mercury could be enhanced by embedding intelligent machine-learning and data-driven capabilities that could be used daily, not just for reporting, but to actively support operational decisions.
Working closely with NPoCC, Informed Solutions brought ISO-42001 certified AI Management System expertise, as well as its trusted AI Readiness and InformedTRANSFORM™ methodologies to deliver three innovative, high-impact capabilities that met these challenges head-on.
Working closely with operational users, Informed Solutions has integrated a new real-time Reporting and Data Analytics capability built on open-source technology.
Three initial high-priority interactive visual dashboards have been developed, focusing on:
These dashboards deliver live analysis, providing a trusted, single view of national policing capacity and capability, enabling teams to effectively coordinate and forward-plan for events using accurate, current data.
The work is enabling a move from retrospective reporting to an approach where data is captured, understood, and acted on in real time and in one place.
Informed developed a planning recommendation capability, using intelligent models to help police identify which police regions to approach first for mutual aid when planning events or emergency responses.
Using machine learning and statistical modelling, the tool considers:
These factors are weighted to generate a ranked list of suggested regions, along with a confidence score to support human-in-the-loop decision making. The model is transparent and adjustable, enabling expert decision-makers to see why certain recommendations are made and adapt the criteria as needed.
This means mutual aid requests and coordination can be executed faster, based on consistent evidence rather than time-consuming, manual, ad-hoc analysis and estimation. Over time, data on how recommendations are used and fulfilled helps the system learn and improve, further improving planning capability.
NPoCC collects written feedback from officers after deployments. These reports capture invaluable information about welfare, organisation, and operational challenges. Until now, this data was difficult to explore and analyse at scale, and difficult to learn from.
Using its AI Readiness Assessment method, Informed Solutions undertook a Knowledge Management proof-of-value that applies natural language processing (NLP) to extract meaning and trends from thousands of survey responses.
The system performs:
This capability enables NPoCC to identify emerging issues and share lessons learned across policing, ensuring officer experience informs future planning, policy, and welfare decisions.
Delivered as an integrated suite of user-centric capabilities, these innovations in advanced reporting, recommendations, and knowledge management reinforce one another:
Together they create a data-driven national coordination ecosystem, positioning Mercury as a first-of-its-kind advanced operational coordination, decision and analysis platform.
The new capabilities mean NPoCC can now:
Planners, analysts, and leadership teams can access the same live view, ensuring consistent understanding of national capacity and demand.
The ML-powered feedback analysis also means wellbeing and logistics issues can be detected and acted upon sooner, improving officer experience and, sustainability and resilience of mutual aid operations.
The next phase of work will focus on further adoption and scaling:
National resilience relies on the ability to understand, decide, and act quickly at scale. By combining modern analytics, responsible machine learning, and explainable AI, Informed Solutions and NPoCC have built capabilities that enhance trust, speed, and operational readiness.
The innovations are already changing how national coordination is done, helping police plan smarter, mobilise faster, and ensure resilience across public safety.
Mark Williams, Assistant Chief Constable for the National Police Coordination Centre (NPoCC) said: “This work represents a significant step forward in how we coordinate policing capability at a national level. Through close partnership with Informed Solutions, we are unlocking the value of our data and gaining clearer insights into where machine learning can deliver practical operational benefits for policing.
“The work with Informed Solutions has enabled us to go beyond exploring technical feasibility, and has supported our data quality, governance, and our ability to roll out new features into live operations. This has given our teams greater confidence, speed and clarity when planning for and responding to national-scale events and emergencies.
“This is not machine learning for its own sake; it is judiciously and responsibly applied innovation to strengthen our service, helping UK policing respond more effectively when it matters most.”
Originally posted here
