Business Idea Competition

Project:

BetaSync addresses lipohypertrophy, a common complication in Type 1 diabetics caused by repeated insulin injections into the same tissue, which leads to poor insulin absorption and long-term health issues.

The team developed a smart mechanical injection aid that uses non-invasive sensing technology to identify healthy issue before delivering insulin. Once a suitable site is found, the device administers the dose with a controlled insertion force of 20–24 N, ensuring precise, safe, and consistent delivery.

Unlike conventional methods that rely on guesswork, BetaSync offers an intuitive solution that actively avoids damaged areas, reducing pain, improving insulin efficiency, and lowering the risk of complications. It uses short, disposable needles (4–5mm) and is designed to minimise medical waste, offering a more sustainable and patient-friendly approach. Their business model combines direct-to-consumer sales with B2B partnerships across hospitals and diabetes care providers, making daily diabetes management safer and smarter.

Project:

Biomedical researchers often face challenges in quickly finding relevant literature, analyzing complex datasets, and protecting sensitive research data. Many existing AI tools are general-purpose, require constant internet access, and may not be suitable for handling private or unpublished data. This creates inefficiencies and privacy risks in high-stakes research environments such as labs, hospitals, and biotech companies.

Lab AI would develop a locally deployed AI assistant based on the open-source DeepSeek model, specifically tailored for biomedical research. This assistant runs entirely on local machines, without requiring internet access, which ensures data privacy and security. It integrates PubMed for up-to-date literature retrieval and connects with local databases or folders containing the researcher's own data and documents. The AI can be trained or fine-tuned using the lab’s own materials, allowing it to provide customized, relevant responses based on both public and private data sources. Researchers can ask natural language questions, and the assistant can summarise papers, analyse experimental data, and assist with writing or problem-solving.

This solution improves research efficiency, accuracy, and confidentiality, offering a powerful tool that adapts to each user’s specific field. The business will earn revenue by offering custom local deployments and support services for labs, companies, and individuals who need personalized, secure AI solutions in biomedical research.

Project:

In response to Nigeria’s growing water pollution crisis, particularly in the Niger Delta where over 80% of wastewater is discharged untreated, the Wetlands Wastewater Treatment innovation introduces an affordable, nature-based solution: engineered constructed wetlands. This technology mimics natural filtration processes, using layers of vegetation and soil to break down contaminants like hydrocarbons through microbial activity. The outcome is a significant reduction in environmental pollution, public health risks, and degradation of agricultural land.

Targeting industries with high pollution footprints, including oil and gas, manufacturing, and construction, our solution offers a cost-effective alternative to conventional chemical-based wastewater treatment. At just £5,000 per system, it reduces hydrocarbon levels by up to 95%, outperforming traditional methods priced at over £35,000. These systems are low-maintenance, energy-efficient, and adaptable to local ecosystems, making them particularly suited to the communities and broader Niger Delta region.

The business model includes designing, installing, and maintaining customised wetland systems, with optional consulting services such as environmental impact assessments and compliance reporting. Future income streams will include skills training for locals and strategic partnerships with environmental bodies.

Beyond water treatment, the systems sequester carbon (15–30 metric tons of CO₂ annually), restore biodiversity, and support circular economy practices by repurposing filtered sediments as organic fertilizer. With community empowerment and ecological restoration at its core, this innovation not only addresses a pressing environmental issue but also transforms wastewater into an opportunity for sustainability, livelihoods, and resilience.

Project:

Online images are inaccessible to those with visual impairments. Major online platforms have alt-text on as few as 0.1% of their images and 98% of top websites fail basic accessibility standards. Since the most popular screen readers rely on alt-text to interpret images, this makes visual content potentially invisible to millions of users.

In-Sight.AI is a browser extension that describes images to those with visual impairments, working on any website regardless of integrated accessibility support. It gives detailed, context-rich image descriptions and allows users to ask follow-up questions. It also features hyper-realisted AI voices in many languages, tones and speeds, uses cutting-edge vision, speech and language models and has custom algorithms to allow for real-time learning and adaptation.