How Linarc’s Shanthi Rajan Is Using AI to Transform Construction Projects

Today we're meeting Shanthi Rajan, Founder and CEO at Linarc.

The company specialises in collaborative, cloud-based management platforms built for the construction industry.

Over to you Shanthi - my questions are in bold:

Who are you, and what's your background?

I'm Shanthi Rajan, the Founder and CEO of Linarc, a collaborative, cloud-based management platform built for the construction industry. My journey didn't begin in construction, and that's part of what makes this path so meaningful. I started my career in enterprise software, working across large-scale systems and product development. I've always been fascinated by complex ecosystems, how things come together, how people collaborate, how we build not just products, but trust and momentum.

That fascination eventually brought me to construction. A field that's foundational to society, but one that historically hasn't had access to the modern tools it truly needs. What drew me in was the human side of construction and collaboration: the coordination, the pressure, and the real cost of miscommunication and delays. I founded Linarc to bridge that gap with a platform that empowers construction teams, improves transparency, and brings modern tools to the field.

As a woman founder in tech and construction, I'm passionate about building inclusive teams, mentoring future leaders, and driving innovation that truly serves people. I believe that with the right technology and the right mindset, we can transform how we build, collaborate, and grow.

What is your job title, and what are your general responsibilities?

I'm the Founder and CEO of Linarc. My role is multifaceted, part innovator, part strategist, and always a collaborator. I guide our company's direction, oversee our product evolution, and ensure we're creating real value for the people who rely on us, construction professionals out in the field, project managers juggling timelines, and executive teams responsible for the bottom line.

At Linarc, I work closely with internal teams across engineering, design, sales, and customer success to ensure our platform is intuitive, powerful, and built with the end user in mind. I also spend a lot of time with our customers, listening, learning, and staying grounded in the realities they face every day. My job is to keep our mission clear, and our solutions aligned with what the industry truly needs: simplicity, transparency, and efficiency. More than a title, being a founder means showing up with clarity, resilience, and support for the people who are building our future.

Can you give us an overview of how you're using AI today?

At Linarc, AI is not just a buzzword; it's a focus area we're actively investing in as we expand the capabilities of our construction management platform. We see AI as a powerful enabler to improve decision-making, reduce rework, and drive efficiency across complex construction workflows.

We're currently developing AI-powered features that aim to deliver real-time insights and predictive analytics to project teams. Among the areas we're focusing on: Predictive Scheduling: We're building tools to analyse historical patterns and current progress data to help anticipate delays, optimise sequencing, and improve schedule reliability. Budget Forecasting: Our roadmap includes techniques designed to flag cost anomalies, predict budget overruns, and account for variables like labour trends or material pricing shifts, before they impact the bottom line. Resource Optimisation: We're working on intelligent allocation engines that will help balance crew workloads, reduce idle time, and improve asset utilisation on-site.

What sets our strategy apart is the goal of connecting these insights across schedules, budgets, and workforce plans, so when a change happens in one area, the impact is reflected system wide. This level of visibility will empower construction leaders to make smarter, faster, and more confident decisions.

Tell us about your investment in AI? What's your approach?

Our approach to AI at Linarc is both deliberate and integrated. We've embedded AI and data science expertise within our product and engineering teams to ensure AI is woven into the platform, not added as an afterthought. We also know the potential cost escalation as AI becomes more dominant. We approach AI integration with intent, leveraging it to enhance value whilst keeping platform investment optimal and sustainable for our clients.

We are developing proprietary models that reflect the nuances of construction workflows, e.g., predictive scheduling and risk detection, whilst also leveraging proven third-party technologies where appropriate. This gives us the agility to innovate quickly whilst ensuring robustness in delivery. AI in construction is evolving from concept to execution, and its impact will be transformative. At Linarc, we're committed to building AI capabilities that bring measurable value, turning project data into actionable insights and enabling teams to stay ahead of challenges rather than react to them.

What prompted you to explore AI solutions? What specific problems were you trying to solve?

We started exploring AI to tackle common issues in construction like delays, cost overruns, and last-minute firefighting. Even though projects generate a lot of data, most of it isn't used effectively. We wanted to turn that data into useful insights. Our aim is to help teams spot problems early, improve resource use, and get better visibility into schedule and budget risks. AI gives us a way to move from static reports to smarter, more proactive project management.

Who are the primary users of your AI systems, and what's your measurement of success? Have you encountered any unexpected use cases or benefits?

Our primary users are general contractors and subcontractors, typically companies with annual revenues between $15 million and $250 million. These are hardworking, fast-moving teams that don't have time for siloed communication and complicated systems. They need tools that are intuitive, insightful, and make their job sites more efficient and safer.

Our AI-assisted features help plan more accurately, manage budget allocations effectively, reduce rework, and prevent over-allocation of crews and equipment. We measure success through many small but meaningful outcomes: spotting a potential delay before it happens, identifying an unexpected cost spike, balancing resources across multiple jobs, or simply saving hours of manual data review. These incremental gains add up to smoother, more confident project delivery.

It's still early in our AI journey, so whilst we're seeing promising results, we're careful not to overstate. We continue to observe how users interact with these tools and expect more unexpected use cases and insights to emerge over time.

What has been your biggest learning or pivot moment in your AI journey?

A key realisation for us has been that construction is not a monolith. Civil infrastructure, residential, multi-family, retail, hospitality, hospitals, and educational projects all operate with different workflows, priorities, and documentation standards. This diversity makes it difficult to apply one-size-fits-all AI solutions.

That's why we've focused our efforts on building AI capabilities that can adapt to different project types—whether that means interpreting plan sheets, understanding image-based field data, or extracting key details from submittals and spec books. AI has the potential to bridge these variations by reading and learning from the visual and text-heavy inputs that are core to construction. We aim to make this intelligence practical and usable, regardless of project type.

How do you address ethical considerations and responsible AI use in your organisation?

At Linarc, our AI efforts are focused strictly on improving construction project execution—what happens in the field and the office. This means we do not collect or work with personal or sensitive employee data. Our models operate on project data such as schedules, budgets, plan sheets, and field documentation. Because of this, many of the broader ethical concerns around AI, such as bias in hiring or diversity tracking, are outside the scope of our current work.

That said, we remain committed to responsible AI practices. We ensure transparency in how our models work, limit the scope of automation to support, not replace, human decision-making, and continuously monitor accuracy to avoid unintended consequences in critical project workflows.

What skills or capabilities are you currently building in your team to prepare for the next phase of AI development?

At Linarc, we're building a team that blends technical expertise, construction know-how, and cross-functional collaboration. Our platform engineers are focused on advanced AI/ML, cloud architecture, and data analytics, all grounded in a deep understanding of how construction projects actually run. Their goal isn't just to build scalable systems; it's to create tools that feel intuitive for both field teams and office staff.

Our operations and support teams are building data fluency and learning to work with AI-powered tools like ChatGPT to automate workflows, draft reports, and extract insights from complex project data. This is helping shift the mindset from manual reporting to smarter, faster decision-making.

Above all, we nurture a culture of continuous learning. We believe AI should amplify human capability, and we're developing our teams to align with that vision, equipping them to lead, adapt, and apply technology in ways that drive real-world impact on the jobsite and beyond.

If you had a magic wand, what one thing would you change about current AI technology, regulation or adoption patterns?

If I had a magic wand, I'd accelerate the development of AI that truly understands structured, logical, and domain-specific data, not just language or images. Just like other technologies that reached an inflection point, AI is now a tsunami. But much of the rapid evolution so far has been in large language models and image processing.

What's still maturing is algorithmic intelligence—the ability to analyse critical project data, reason through complex dependencies, and extrapolate outcomes with context and accuracy. For industries like construction, that's where the real value lies. We don't just need AI that can talk, we need AI that can think in structured terms, interpret data flows, and support high-stakes decisions. That's the frontier we're most excited to see evolve—and contribute to.

What is your advice for other senior leaders evaluating their approach to using and implementing AI? What's one thing you wish you had known before starting your AI journey?

My advice to other senior leaders is to be cautious about chasing AI hype without anchoring it in real industry pain points. In construction, large language models can feel like a chimera—impressive on the surface, but with limited practical runway when it comes to solving core challenges. We've seen this before. Excel, whilst flexible, became deeply embedded across the industry—but instead of solving problems, its overuse often made things worse by creating disconnected workflows, hidden errors, and a lack of shared visibility.

One thing I wish I had known earlier is how many of construction's real challenges don't fit into current AI models or frameworks. The data is fragmented, the decisions are highly contextual, and the workflows are non-linear. That's why success with AI comes from building purpose-driven solutions, not retrofitting generic tools. The goal isn't to sprinkle AI across everything—it's to apply it where it can actually drive clarity, coordination, and outcomes.

What AI tools or platforms do you personally use beyond your professional use cases?

Beyond my work at Linarc, I'm genuinely curious about how AI can improve everyday productivity, creativity, and communication. I regularly use ChatGPT for everything from idea generation to writing drafts. It's like having a thinking partner that helps me move faster without losing quality or tone.

I also use Copilot, Claude, Perplexity, and Gemini, each offering unique strengths in retrieving, processing, or presenting information. It's been fascinating to see how these tools complement each other depending on the task, whether I'm researching, summarising, or just exploring new perspectives.

Grammarly is another favourite. Its AI-driven feedback on clarity, tone, and grammar makes a real difference, especially when communicating with varied audiences. It's one of those tools I wish existed back in school.

What excites me most is how accessible and intuitive these tools have become. They're not just for specialists; they empower anyone to work smarter, think more clearly, and explore ideas faster. And that's the kind of shift that truly changes how we work and learn.

What's the most impressive new AI product or service you've seen recently?

One of the most exciting developments in construction technology is the rise of autonomous equipment on job sites. From AI-powered bulldozers and excavators by companies like Built Robotics to robotic surveyors like Boston Dynamics' Spot, these machines are reshaping how work gets done in the field. Autonomous haul trucks, drones for site monitoring, and even bricklaying robots now support crews by handling repetitive or high-risk tasks quickly and precisely. As these systems mature, they promise efficiency and safer, more data-driven project execution.

Finally, let's talk about predictions. What trends do you think are going to define the next 12-18 months in the AI technology sector, particularly for your industry?

In the next 12–18 months, I believe three major AI trends will shape the construction industry. First, the rise of analytical and logic-based AI models, tools that can reason over structured project data, like schedules, cost breakdowns, and resource allocations. These models will go beyond generative outputs to help project teams make proactive, data-driven decisions.

Second, we'll see broader adoption of advanced image and video analysis tools. From automated site inspections to progress verification, AI will increasingly interpret visual data—photos, drone footage, and 360° captures—to detect issues, verify work, and monitor safety conditions in real time.

Third, AI in job site safety is poised for rapid growth. Whether through wearable sensors, camera-based risk detection, or predictive incident modelling, safety tech powered by AI will shift from reactive to preventive, helping teams identify hazards before they turn into incidents.

Together, these trends will push AI in construction from experimentation to embedded value, delivering real, measurable outcomes in productivity, cost control, and jobsite safety. AI is evolving at a remarkable pace, and its ability to transform how construction projects are planned, executed, and monitored is only accelerating.

Thank you Shanthi. Connect with Shanthi on LinkedIn and read more about Linarc at their website.