| _version_ | 1866901718148579328 |
|---|---|
| author | Gillespie, Stuart Ariskina, Kira Elezaj, Ogerta Araujo Alvarez, Alexandra |
| author_facet | Gillespie, Stuart Ariskina, Kira Elezaj, Ogerta Araujo Alvarez, Alexandra |
| contents | <div> <p><span lang="EN-GB">This case study is published as part of the<a href="https://iuk-business-connect.org.uk/programme/bridgeai/"> Innovate UK BridgeAI programme</a>, under the Independent Scientific Advisor (ISA) offer delivered by The Alan Turing Institute. The ISA initiative provides transformative, evidence-based support to SMEs across BridgeAI’s priority sectors, empowering them to harness AI for strategic growth and practical impact.</span> </p> </div> <div> <p><span lang="EN-GB">We gratefully acknowledge the contributions of <strong>Kira Ariskina</strong>, Founding Director of <strong>ViableSite</strong>, and <strong>Dr Ogerta Elezaj</strong>, Independent Scientific Advisor for BridgeAI at <strong>The Alan Turing Institute</strong>, whose insights and engagement were invaluable to the development of this case study.</span> </p> </div> <div> <p><span lang="EN-GB">We would also like to express our appreciation to <strong>Alexandra Araujo Alvarez,</strong> Senior Research Community Manager for BridgeAI; <strong>Dominica D'Arcangelo</strong>, Programme Manager; and <strong>Kathryn Hockman</strong>, Project Coordinator, for their leadership and support throughout this work. We further acknowledge Stuart Gillespie for his role as technical writer for this and other case studies within the ISA offer.</span> </p> </div> <div> <p><span lang="EN-GB">This work is led by <strong>Dr Vera Matser,</strong> Head of Strategic Capabilities and Principal Investigator for BridgeAI at The Alan Turing Institute.</span> </p> </div> <div> <p><span lang="EN-GB">For any comments, questions, or collaboration opportunities with BridgeAI, please email: </span><a href="mailto:bridgeAI@turing.ac.uk" target="_blank" rel="noreferrer noopener"><span lang="EN-GB">bridgeAI@turing.ac.uk</span></a><span lang="EN-GB">.</span> <br><br></p> <p><strong>Abstract</strong></p> <p>This case study explores how UK-based SME <a href="https://viablesite.co.uk"><strong>ViableSite</strong></a> is leveraging artificial intelligence to unlock the development potential of small residential sites. Across the UK, hundreds of thousands of small plots remain underutilised due to the high cost, complexity, and time required to assess their viability, particularly for SMEs lacking access to extensive technical expertise.</p> <p><strong>ViableSite</strong> addresses this challenge by developing an AI-enabled platform that integrates diverse data sources, including planning policies, geospatial data, environmental constraints, utilities information, and market data to automate site feasibility assessments. Through participation in the Innovate UK BridgeAI programme, the company received targeted support from an Independent Scientific Advisor at The Alan Turing Institute to define its AI strategy, prioritise data readiness, and design a scalable system architecture.</p> <p>The collaboration focused on consolidating fragmented datasets into a unified repository and establishing the foundations for predictive modelling. This enabled <strong>ViableSite</strong> to progress rapidly from an early-stage concept to the development of a minimum viable product, featuring capabilities such as risk visualisation, cost prediction, and AI-assisted decision-making with human oversight.</p> <p>The case highlights the critical role of high-quality data, structured advisory support, and interdisciplinary collaboration in early-stage AI innovation. It offers practical insights for SMEs seeking to adopt AI in complex, data-intensive sectors such as construction and urban development, demonstrating how AI can reduce risk, improve decision-making, and accelerate access to new development opportunities.</p> </div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18481884 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | ViableSite: unlocking the potential of small residential sites through AI automation Gillespie, Stuart Ariskina, Kira Elezaj, Ogerta Araujo Alvarez, Alexandra <div> <p><span lang="EN-GB">This case study is published as part of the<a href="https://iuk-business-connect.org.uk/programme/bridgeai/"> Innovate UK BridgeAI programme</a>, under the Independent Scientific Advisor (ISA) offer delivered by The Alan Turing Institute. The ISA initiative provides transformative, evidence-based support to SMEs across BridgeAI’s priority sectors, empowering them to harness AI for strategic growth and practical impact.</span> </p> </div> <div> <p><span lang="EN-GB">We gratefully acknowledge the contributions of <strong>Kira Ariskina</strong>, Founding Director of <strong>ViableSite</strong>, and <strong>Dr Ogerta Elezaj</strong>, Independent Scientific Advisor for BridgeAI at <strong>The Alan Turing Institute</strong>, whose insights and engagement were invaluable to the development of this case study.</span> </p> </div> <div> <p><span lang="EN-GB">We would also like to express our appreciation to <strong>Alexandra Araujo Alvarez,</strong> Senior Research Community Manager for BridgeAI; <strong>Dominica D'Arcangelo</strong>, Programme Manager; and <strong>Kathryn Hockman</strong>, Project Coordinator, for their leadership and support throughout this work. We further acknowledge Stuart Gillespie for his role as technical writer for this and other case studies within the ISA offer.</span> </p> </div> <div> <p><span lang="EN-GB">This work is led by <strong>Dr Vera Matser,</strong> Head of Strategic Capabilities and Principal Investigator for BridgeAI at The Alan Turing Institute.</span> </p> </div> <div> <p><span lang="EN-GB">For any comments, questions, or collaboration opportunities with BridgeAI, please email: </span><a href="mailto:bridgeAI@turing.ac.uk" target="_blank" rel="noreferrer noopener"><span lang="EN-GB">bridgeAI@turing.ac.uk</span></a><span lang="EN-GB">.</span> <br><br></p> <p><strong>Abstract</strong></p> <p>This case study explores how UK-based SME <a href="https://viablesite.co.uk"><strong>ViableSite</strong></a> is leveraging artificial intelligence to unlock the development potential of small residential sites. Across the UK, hundreds of thousands of small plots remain underutilised due to the high cost, complexity, and time required to assess their viability, particularly for SMEs lacking access to extensive technical expertise.</p> <p><strong>ViableSite</strong> addresses this challenge by developing an AI-enabled platform that integrates diverse data sources, including planning policies, geospatial data, environmental constraints, utilities information, and market data to automate site feasibility assessments. Through participation in the Innovate UK BridgeAI programme, the company received targeted support from an Independent Scientific Advisor at The Alan Turing Institute to define its AI strategy, prioritise data readiness, and design a scalable system architecture.</p> <p>The collaboration focused on consolidating fragmented datasets into a unified repository and establishing the foundations for predictive modelling. This enabled <strong>ViableSite</strong> to progress rapidly from an early-stage concept to the development of a minimum viable product, featuring capabilities such as risk visualisation, cost prediction, and AI-assisted decision-making with human oversight.</p> <p>The case highlights the critical role of high-quality data, structured advisory support, and interdisciplinary collaboration in early-stage AI innovation. It offers practical insights for SMEs seeking to adopt AI in complex, data-intensive sectors such as construction and urban development, demonstrating how AI can reduce risk, improve decision-making, and accelerate access to new development opportunities.</p> </div> |
| title | ViableSite: unlocking the potential of small residential sites through AI automation |
| url | https://doi.org/10.5281/zenodo.18481884 |