Client
A UK county fire and rescue service, covering operational response, home fire safety and premises risk assessment across a mixed urban and rural county
Sector
Public Safety & Emergency Services
Engagement
Integration of building risk assessment, incident history and home fire safety visit data into a single operational platform, with real-time access for control room and attending crews - phased delivery over two quarters.
What the client needed
The service's risk-based inspection programme generated detailed building risk assessments for higher-risk premises - care homes, houses in multiple occupation, high-rise residential blocks - but that information lived in a records system built for compliance reporting, not for operational use in the moments before a crew arrived at an incident. A control room operator taking an emergency call had no fast way to check whether the address had a known risk factor on file - a compromised means of escape, a previous enforcement notice, a building with cladding still awaiting remediation - short of a manual search that few incidents allowed time for. Separately, home fire safety visits, aimed at the households statistically most likely to experience a serious fire, were prioritised from a static list rebuilt periodically from historical data, with no way to fold in more recent signals like a nearby incident or a change in a household's circumstances. By the time we were engaged, an internal review had identified that attending crews were arriving at higher-risk premises without the risk information the service already held on file, and that home fire safety visits were not consistently reaching the households most likely to need one first.
How we worked
- Built a single premises risk record that pulls building risk assessments, enforcement history and prior incident data into one profile per address, replacing three separate systems that previously had to be checked individually.
- Gave control room staff a live risk flag at the point a call is taken, surfacing known building risk factors to mobilising crews before they leave the station rather than after they arrive.
- Introduced a dynamic home fire safety prioritisation model that reweights the visit list using recent local incident data alongside the existing demographic risk indicators, rather than relying solely on a periodically rebuilt static list.
- Worked with watch managers to design a mobile-friendly premises risk view crews could check from the appliance en route, built to work over the patchy mobile coverage found across the service's rural areas.
- Rolled out station by station, starting with the two stations covering the highest concentration of higher-risk premises, so the risk-flagging workflow was proven under real incident conditions before wider connection.
- Trained control room operators and watch managers together on the shared risk view, so a flag raised in the control room and a flag checked on the appliance were reading from the same record.
Measured results
All figures verified with the client. Specific station, premises and household detail withheld in line with our standard confidentiality terms.
- Time to surface known premises risk information to a control room operator taking an emergency call fell from a manual search measured in minutes to a live flag returned in seconds.
- The service's own data shows a marked increase in the proportion of incidents at known higher-risk premises where the attending crew received a pre-arrival risk brief, up from an inconsistent minority before rollout.
- Home fire safety visits to households flagged by the dynamic prioritisation model reached a materially higher share of the highest-risk addresses within the same visiting capacity as before, without increasing staff time committed to the programme.
- Watch managers report crews arriving with a clearer picture of a building's layout and known hazards specifically at premises the service had previously assessed but where that assessment wasn't reliably reaching the incident ground.
- The service's prevention team now uses the platform's incident-linked prioritisation directly in visit scheduling, replacing a static list that had previously been refreshed only a few times a year.
"We held the risk information. That was never the gap. The gap was getting it from a compliance record into a control room operator's hands in the thirty seconds they have to brief a crew on the way out of the door - and that's exactly the part this closed."
Working on something similar?
If this engagement looks like the kind of problem you are facing, we would be glad to compare notes by email.
Why we didn't build a new risk assessment system
The service already had a well-run risk-based inspection programme producing genuinely good assessments - the problem was never the quality of the data, it was where that data lived and how hard it was to reach at the moment it mattered. A rebuild of the assessment process itself would have addressed a problem the service didn't actually have, while leaving the real gap - getting an existing assessment in front of a control room operator or an attending crew in time to matter - completely untouched. We built the operational layer on top of the existing risk assessment system rather than replacing it, which is also why the highest-risk stations were live within a single quarter rather than a full system replacement cycle.
A static prioritisation list looks fine until you ask what it's missing
The home fire safety visit list the service was using before this programme was a reasonable model built on solid demographic risk indicators, and it would have been easy to leave it alone rather than add complexity. What it structurally couldn't do was respond to anything that happened after it was last rebuilt - a fire two streets away, a change in a household's circumstances flagged by another agency, a run of incidents in a specific type of housing. Folding recent incident data into the prioritisation model was a moderate technical change with an outsized effect on which households actually got a visit first, and it's the part of this engagement we'd point to first when a client asks what "well-run but static" risk models are usually missing.
Rural mobile coverage decided more of the design than the risk model did
A premises risk view that only works reliably at the station or over a strong 4G connection is of limited use to a crew already en route through the parts of this county where coverage drops out. We spent more of the technical effort on making the mobile view work reliably over patchy rural coverage - caching the relevant premises data locally as soon as a call was assigned, rather than depending on a live connection at the point of arrival - than we did on the underlying risk model itself. It's an unglamorous piece of engineering next to the prioritisation work, and it's also the piece that determined whether crews outside the largest towns got any benefit from this programme at all.
Lessons learned
The first lesson was that risk information a service already holds is often more valuable, sooner, than any new risk information it could go and collect - the constraint was distribution, not data.
The second lesson was that a static prioritisation model built on sound historical indicators still degrades the moment something changes on the ground that the model has no way to see; folding in recent operational signal is usually a smaller change than it looks and a larger one than it sounds.
The third lesson was that for any service covering a genuinely rural area, designing for the worst mobile connection on the patch, not the best, is what determines whether an operational tool gets used consistently or only near the stations with good signal.
If your organisation holds risk or operational data that isn't reliably reaching the people who need it in the moment it matters, we would be glad to discuss what a platform like this might look like for you. Email sales@halfteck.com.