Spotlight Interview: FairFleet

In our latest Spotlight Interview, we spoke with Tomas Evans , Sales Director at FairFleet. Tom discusses FairFleet’s journey from an insurance innovation spin-out to a global drone inspection platform serving real estate portfolios worldwide.  "For us, success means helping the real estate industry fundamentally shift its maintenance philosophy from reactive (i.e. scrambling to fix leaks after they damage a tenant's property), to proactive. We want to move beyond just identifying what is broken today and start predicting what will fail in six months, allowing asset and property managers to budget for repairs long before they become emergencies."

Q1. Can you walk us through FairFleet’s founding story, what inspired the team to build a platform around drone services and aerial data?

FairFleet launched in 2016 was originally spun out of an Allianz innovation accelerator. We designed a drone solution for the insurance industry to help assess claims in relation to damaged buildings but quickly realised the broader potential directly within the real estate sector. As drone pilots working on projects with property professionals, our Co-Founder’s realised there was a communication gap between the type of aerial data that real estate clients wanted, and how drone pilots tried to collect that data for them. They also realised there was a lack of scalability with drones generally, as pilots often still had to travel long distances to get to client sites. Over time, the idea evolved into a full service solution, combining the benefits of a globally distributed network of fully qualified and vetted pilots, with a robust AI computer vision system that can autonomously identify and grade the severity of visible building defects, and an easy-to-use reporting platform offering deeper interactivity with the survey results.

Q2. Traditional building inspections are often time-consuming, costly and disruptive. How does FairFleet change the way inspections are carried out across large property portfolios?

In simple terms, our solution solves the three biggest headaches for real estate owners and operators; speed, scale, and consistency.

In terms of speed and scale, our global network of drone pilots means we don’t need to send people long distances to get to client sites. They’re usually within 2 or 3 hours of each property so we can be on site very quickly. We can also inspect large portfolios simultaneously, even those that are spread across multiple countries. Once the data has been collected, the AI can analyse thousands of photos very quickly, saving surveyors days on manual image review and reporting.

In terms of consistency, AI computer vision analyses the 50,000th photo, or the 100^th building, with the exact same precision as the first one. Unlike humans, it doesn’t suffer fatigue or have bad days. It also doesn’t suffer from cultural or regulatory differences in terms of expectations, building regulations, or operational processes, meaning we can survey buildings in London, Paris, Tokyo, Kathmandu, Delhi, Mexico City, Dubai, literally anywhere in the world, with the exact same standard applied every time.

As great example that highlights all of this, we just surveyed the roofs for a retail banking client’s entire >2,000-strong branch network across 15 countries. The whole process would have taken over 3 years if they’d done it manually and been fraught with country-specific differences in data collection, but through our portfolio survey solution we provided a standardised risk profile of every branch in 3 months. This enabled their managing agent to develop a 10-year branch capex plan based on objective, standardised, visible data in 83% less time and with 64% less cost than projected.

Q3. What role does AI play in identifying defects, deterioration, or potential risks within real estate assets?

FairFleet’s AI does the heavy lifting in terms of identifying and grading the severity of defects, even some that might not be visible to the naked eye. Our detection models have been trained and repeatedly re-trained with visual data from over 53,000 drone surveys across every asset type. They have been tried, tested, and are now trusted in over 100 countries by some of the biggest real estate companies in the world.

Even so, we always have a human operator performing a quality review for every survey we conduct. This ensures we capture all the defects and categorise them in the right way. One of our biggest clients, who has used us for hundreds of surveys, compared our survey data with their own manual survey data, and found our solution picked up on average 18% more defects per building, so our AI + human approach delivers a significant uplift in performance while freeing up surveyors to focus on the more ‘human’ aspects of the job.

It's super important to stress this last point, as our solution has been built to augment the very important work done by surveyors, rather than replace it. Ultimately, a drone can’t talk with a tenant to gauge satisfaction or pull at a loose flashing to figure out the extent of any damage, so people still play the most crucial role in managing properties; our solution is simply a tool to enhance their productivity and the quality of the output.

Q4. With increasing regulatory and insurance scrutiny, how can drone-based inspections support compliance and reduce risk exposure for property owners?

Using drones massively reduces the risk profile of property inspections by keeping people firmly on the ground, supporting health and safety initiatives.

Our pace and scalability also helps clients reduce risk for tenants or members of the public. Every day or week that passes without an issue being resolved (e.g. a loose tile hanging from a gutter directly over a pavement) potentially exposes the property owner and/or operator to litigation should that issue cause injury to people or damage to neighbouring properties, so being prompt and accurate with public risk assessments is mission critical. The fact our AI also identifies up to 18% more issues than a manual inspection helps to nip smaller issues in the bud before they become litigation-worthy problems.

From an insurance perspective, insurers are increasingly demanding proof of maintenance to pay out claims. Our drone data provides a high-resolution, time-stamped, and geo-located digital twin of client assets. This serves as objective (and visible) proof that demonstrates clients have met their duty of care.

Finally, for real estate clients who give their surveyors drones, the drone operation itself carries regulatory risk (aviation laws, airspace permits, neighbouring properties). FairFleet absorbs this entire liability layer. We handle the flight planning, permits, insurance, and CAA compliance, meaning clients get the data without taking on the legal exposure of operating an aviation department in-house.

Q5. Looking ahead, what does success look like for FairFleet in the next few years and how do you see drone-powered data becoming a standard part of real estate asset management?

For us, success means helping the real estate industry fundamentally shift its maintenance philosophy from reactive (i.e. scrambling to fix leaks after they damage a tenant's property), to proactive. We want to move beyond just identifying what is broken today and start predicting what will fail in six months, allowing asset and property managers to budget for repairs long before they become emergencies.

The key to this is longitudinal analysis. By overlaying new survey data against previous years, our AI will be able to detect subtle rates of change that a human eye would miss, like a crack that has grown 5mm or a rusted area that has expanded by 10% since the last flight. This transforms maintenance from a guessing game into a precise science, tackling degradation before it ever becomes a failure.

We also recently launched our 3D inspection tool, which not only accurately indicates where defects are located on a property, but shows how accessible or inaccessible they are in respect to the broader property structure. This is giving clients a better way to plan maintenance and repairs, saving time and money while minimising disruption to tenants.

In the longer term, expect more autonomy in terms of how the data is collected, processed, and ultimately combined with other building data sets to deliver essential insights for multiple stakeholders at both the owner and operator levels, in fully interactive digital twins.