Associative Trails

We develop bespoke web and mobile applications

Associative Trails is a boutique internet consultancy based on the high-speed rail link in East Kent, UK. We take pride in crafting web and mobile applications that save time and make money for our clients.

Our small size makes us great value for money. Please get in touch to arrange a free, no obligation consultation to discuss your requirements.

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Services

  • Databases
    SQL Server, MySQL, PostreSQL
  • API integration
    Hubspot, Creditsafe, YouTube, Vimeo, Egnyte, OpenAsset, Slack, Twitter
  • Quality front-end coding
    HTML, CSS, Javascript, responsive design, Outlook add-ins, D3 data visualisation
  • Mobile applications
    HTML5, Apple iOS (iPhone and iPad), Android
  • Hosting and deployment
    Linux, Windows, AWS EC2/S3/Cloudfront, Apache, IIS, Nginx, AWS Elastic Beanstalk, AWS Lambda, Docker, Jekyll

Selected projects

Sling
Wonderfully easy workflows

A flexible and innovative workflow building tool enabling companies to set up and administer approval workflows for business development, procurement, and all sorts of other key processes.

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Eckersley O'Callaghan Intranet
Invaluable technical information

A self-populating technical library that grew into a comprehensive company-wide information portal for one of the UK's most accomplished engineering specialists.

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Old Arupians Community Website (2019)
Freshening community fellowship

A thorough refresh of one of our most venerable and successful projects. Now fully responsive and even easier to use.

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Heatherwick Studio Intranet
Digging that studio scene

A bespoke company intranet for one of the UK's top design studios.

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Selected clients

  • Arup
  • Ashridge Business School
  • Eckersley O'Callaghan
  • Ellipse
  • Heatherwick Studio
  • London Business School
  • Safetime
  • Scott Brownrigg
  • Sheppard Robson

Elsewhere

Recent blog posts

Deep Learning Architect - Classification for Architectural Design through the Eye of Artificial Intelligence (2018)
Notes on a 2018 paper by Yuji Yoshimura, Bill Cai, Zhoutong Wang, and Carlo Ratti

Using a convolutional neural network to recognise top-level architects' individual styles. Almost 70% or architects can be distinguished with 80% accuracy. 20,000 sample images by 34 past Prisker Prize winners.

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Architectural Drawings Recognition and Generation through Machine Learning (2018)
Notes on a 2018 paper by Weixin Huang and Hao Zheng

A generative adversarial network to superimpose coloured rectangles on architectural floor plans based on room type (Bedroom, kitchen, etc.). Worked really well for some rooms - it even picked up a few mistakes in the human labelling.

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OPTIMISING IMAGE CLASSIFICATION - Implementation of Convolutional Neural Network Algorithms to Distinguish Between Plans and Sections within the Architectural, Engineering and Construction (AEC) Industry (2019)
Notes on a 2019 paper by Alessandra Fabbri, M. Hank Haeusler, and Yannis Zavoleas

An attempt to build a convolutional neural network to classify 1000 images into 3 categories. Accuracy peaked at 82%, no hyperparameter tuning was performed.

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