Intern – Portfolio Engineering - Capital Markets, Cmia

Safety gains remain hard to prove. When Shelley Copsey was appointed by the Ontario Teachers' Pension Plan, one of the world's biggest infrastructure investors, her job was to find ways to use technology to solve problems across its portfolio. She quickly learned that there were persistent problems facing front-line workers. Four years later, Copsey is CEO of FYLD, a London-headquartered software company that uses artificial intelligence to support workers in construction, utilities and infrastructure. It comes as AI plays an increasingly important role in some of the most physically demanding jobs, from maintaining roads and railways to working on construction sites. The aim is to reduce both paperwork - and accidents. According to Copsey, when fieldwork goes wrong, attention usually turns to budgets or cash flow. "We always go to the back office and we're like: oh, if we could help them forecast the job better, maybe this wouldn't be a problem," she told CNBC. "I think that what nobody ever did was just sit and say: well, what goes on in those field workers' lives and how do we make the life of the front line easier?" Planning with AI. FYLD uses AI to predict which permits a project will need, helping workers avoid delays. Previously, workers often arrived at a site without all of the information they needed, Copsey said. The system can also help crews plan around the weather, for example, rescheduling heavy lifting from a day when strong winds are forecast. Workers record short videos describing their day's tasks and safety concerns. AI analyzes the information to help workers and managers anticipate potential problems. An additional benefit is a reduction in paperwork, according to Copsey. Field workers can spend around 20% to 25% of their time on site on administrative work, she said, whereas now, photos, video and job information can be used to generate draft reports, reducing a task that previously took around half an hour to two or three minutes. What nobody ever did was just sit and say: well, what goes on in those field workers' lives and how do Cryptothinks.com make the life of the front line easier? Infrastructure services provider Amey announced in March that it would roll FYLD out to 2,500 field workers as it looks to improve safety and give supervisors real-time visibility of work on the ground. Copsey said FYLD case studies have reported reductions in incidents and injuries of as much as 48%. AI remote inspections. AI is also being used to reduce the need for physical infrastructure inspections. Nathan Marsh, London-based senior vice president for Europe, the Middle East and Africa at U.S. software company Bentley Systems, told CNBC that the company's road maintenance platform, Blyncsy, analyzes images captured by drivers to spot problems, including damaged guardrails, overgrown vegetation and debris. "Applying AI to existing imagery provides complete asset coverage in a fraction of the time a conventional survey would take," Marsh said. Steven Elsham, U.K.-based CEO of Dutch maintenance software company Ultimo, told CNBC its AI tools can flag safety incidents that might otherwise go unreported. The system scans work orders and shift logs for signs of incidents, then drafts a report for a person to review. Elsham gave the example of a warehouse door with damage that suggested it had been struck by a forklift. "People should still report things themselves," he said. "This is there for what gets missed." Safety evidence limits. Waleed Umer, an assistant professor at Northumbria University, England, told CNBC that many studies so far have focused on what models can detect, rather than whether they prevent harm. Construction sites change constantly, making it difficult for cameras to capture every hazard, he said. He also warned that false alarms can lead workers to ignore warnings, while overreliance on AI can create a false sense of security. Treat AI as an extra pair of eyes, not a replacement for supervision... And be clear about what it can't see. Waleed Umer Northumbria University Billy Hare, a professor of construction management at Glasgow Caledonian University, Scotland, told CNBC that research projects and pilot studies have primarily focused on productivity gains, often assuming that less exposure to hazards means lower risks. Bentley Systems, for example, said one rail project it supported cut physical site visits by 50% and manual surveys by 70% using a digital twin, a virtual model of a physical asset. It's reasonable to assume that less time spent in hazardous environments could reduce risks, Hare said, but establishing whether technology actually prevents accidents needs further investigation. According to the U.K.'s Health and Safety Executive (HSE), the number of construction workers killed in work-related incidents in Great Britain fell to 25 in 2025/26, down from 35 the previous year and below the five-year average of 37. "The U.K. has seen the lowest construction fatality figure ever recorded this year, but this may be due to economic slowdown," Hare said, which makes it harder to tell whether technology has made a measurable difference. Construction workers are shown on a residential building site in Montreal, Quebec, Canada, on Aug. 25, 2026. Human oversight may not be enough on its own, Northumbria University's Umer said, especially if AI produces more information than supervisors can realistically review. In that case, recommendations could be approved without proper scrutiny. "Treat AI as an extra pair of eyes, not a replacement for supervision," Umer said. "And be clear about what it can't see." For Ultimo's Elsham, the bigger question is whether AI makes difficult jobs more manageable rather than squeezing more work out of the people doing them. That starts with how performance is measured, he said: companies need to decide what any time saved is for, and which parts of the job AI should support. "Safety teams are already covering more hazards with the same headcount," he said. "If the only plan for the freed-up hours is more tickets per head, workers will see straight through it, and they'd be right." Companies should instead measure what matters on the floor, he argued, including safety, first-time fixes and unplanned breakdowns, rather than just output. "A well-run business benefits everyone in it, and the people doing the hardest jobs should feel it first," Elsham said.

EliseAI's $350 million raise at a $4 billion valuation puts vertical AI for housing and healthcare to the test. EliseAI has raised $350 million at a $4 billion valuation, a big private-market bet that AI will create durable value not by answering open-ended prompts but by taking over repetitive, regulated work inside apartment operations and healthcare administration. The round, announced Sept. 29, matters because it suggests investors are rewarding distribution, workflow coverage and revenue in essential services over the more familiar story of model demos and chatbot novelty. The question behind the headline is simpler than the financing mechanics: is EliseAI showing that vertical AI can genuinely improve the economics of housing and healthcare, or are investors paying ahead of proof that those gains reach renters, patients and workers rather than just owners, providers and the vendor itself? Why this round stands out. EliseAI said the financing was led by Andreessen Horowitz and Bessemer Venture Partners, with participation from Ontario Teachers' Pension Plan, Sapphire Ventures and Navitas Capital. According to Fortune, the round consisted entirely of primary capital and nearly doubled the company's valuation from the $2.2 billion attached to its $250 million Series E about 13 months earlier. That matters for two reasons. First, primary capital means the money is going into the company rather than mainly to existing shareholders cashing out. Second, the valuation implies that investors believe EliseAI has moved beyond experiment status. The company says it now generates more than $200 million in annual recurring revenue, a figure that has not been publicly audited, and reaches 6.5 million housing units. In its own Sept. 29 announcement, EliseAI described that footprint as one in five U.S. multifamily apartments; a syndicated company release described it as one in six. Either way, the claimed reach is large enough to make the company more than a niche tool. EliseAI says it will use the new money to expand product development and grow engineering, deployment and sales across North America, including a second engineering hub in San Francisco alongside its New York headquarters. That hiring plan fits the business it has built. EliseAI was founded around operational workflows in property management and healthcare administration, not as a general-purpose assistant. Its products handle leasing, resident communications, scheduling and other repetitive service and back-office processes, and the company says it is pushing further into day-to-day operations. The case investors are buying. The strongest argument for EliseAI is not that it has a flashier model than everyone else. It is that narrow, industry-specific automation can be harder to replace once it is embedded in the messier parts of real work: routing inquiries, handling follow-ups, scheduling, intake, document collection and escalating exceptions to humans. That is the core vertical-AI pitch. In sectors like housing and healthcare, the value sits less in raw model performance than in integrations, workflow rules, operational data and the ability to expand inside an account after landing an initial use case. A landlord or healthcare operator is not buying a chatbot for entertainment. They are buying fewer missed calls, faster responses, better coverage after hours, more consistent follow-up and, ideally, a measurable lift in revenue or operating efficiency. EliseAI is presenting exactly that story. In its Sept. 29 announcement, the company said that across the units it serves, occupancy is two percentage points higher, residents are seven percentage points more likely to renew and net operating income rises by as much as 20%. If those gains hold up, they would help explain the valuation. A vendor that can reliably improve occupancy and renewals at scale becomes part of the economic engine of a property, not just another software line item. The same logic applies in healthcare administration, where missed messages, scheduling bottlenecks and intake delays can be expensive and frustrating. Here too, the attraction is not novelty. It is throughput. What the valuation does not settle. The problem is that the most important operating claims are still claims. The public record does not spell out the methodology behind EliseAI's occupancy, renewal or NOI figures, and it does not show the sample size, time period, comparison group or statistical controls. A stronger property cohort, local market conditions, pricing changes or broader service improvements could help produce the same results. That does not make EliseAI wrong. It does mean the round should be read as a market signal, not as independent proof. The same caution applies to reach and revenue. More than $200 million in ARR and millions of covered units indicate real distribution, but they do not answer the questions enterprise buyers should care about next: retention, gross margin, customer concentration, implementation cost, contract length, churn and how much of the business comes from housing versus healthcare. None of that was disclosed in the announcement. There is also the human side of automation. Property management and healthcare administration are full of repetitive tasks, but they also involve fair-housing obligations, personal and medical information, and situations where a scripted response is not enough. A system that handles high volumes of messages can improve speed while still creating new risks if its classifications, routing or communications are wrong. At scale, a small error can propagate across many properties or care sites before anyone catches it. That is why the real diligence questions are operational, not theatrical. How much of the workflow is automated? How often are exceptions kicked to a human? How fast can staff audit or reverse a bad routing decision? What controls govern data access? In housing, how are automated communications tested for fair-housing compliance, and what is the appeal path for a resident who believes the system got something wrong? In healthcare, where the public materials are thinner on compliance details, what is reviewed by people, what is logged, and who is accountable for high-impact administrative decisions? Who is likely to benefit. Investors clearly see a company with traction in hard-to-serve industries. Fortune reported this was the fourth financing involving a16z and Bessemer since 2023, a sign that existing backers believe the company's distribution advantage is strengthening. EliseAI also says its revenue has doubled year over year for five consecutive years, which, if sustained, would help explain why fresh capital arrived at a sharply higher price. But the next phase of the story is less about whether AI can answer leasing or scheduling inquiries and more about who captures the productivity it creates. Owners and providers may gain higher occupancy or lower administrative cost. The vendor may gain pricing power if it becomes deeply embedded. Frontline staff may be pushed into higher-value exception handling, or see hours reduced. Renters and patients may get faster responses, or they may find it harder to reach a person when the automated path fails. That is the lasting significance of EliseAI's raise. It shows that private capital is ready to treat vertical AI in essential services as infrastructure. What it does not yet show, at least in the public record, is how consistently the gains are measured, how widely they are shared, or how safely the system behaves when the workflow stops being routine. For landlords, healthcare operators and enterprise buyers, the useful scorecard now is not just ARR and valuation, but retention, workflow coverage, escalation rates, implementation time, compliance controls and independently measured outcomes. Emily Johnson, a seasoned journalist with a penchant for tech innovations and digital culture, has been enlightening BizTech Weekly's readers since its inception. With a background in computer science and a master's in journalism, Emily bridges the gap between complex tech concepts and everyday usability, making her columns a must-read for tech enthusiasts and novices alike.