Case Study - Knowledge Base Development for ID-Workshop Ltd. (April 2015 – February 2017)

Summary	Development of a knowledge base solution for the automotive aftermarket
Key Outcomes	Rapid development of a working first version. Establishment of a strategy to move progressively from expert system to advanced AI
Key Challenges	Complex interactions with multiple content sources. Intermittent funding.
Key Technologies	Microsoft stack (ASP.Net, SQL/Server), HTML5/CSS/SVG, Python + machine learning libraries (e.g. pandas, sklearn), Hadoop/spark

The ID Workshop solution provides comprehensive vehicle diagnostics, but provides little or no guidance on how to efficiently identify and rectify the underlying causes of a problem. I developed a companion system which bridges that gap.

The initial version focused on synthesising information from a number of external sources to help mechanics identify parts recommendations which can then become queries in a parts catalogue. This was progressively extended to deliver curated expert guidance on testing and diagnosis, and to allow the mechanic to explore a complex set of faults and possible solutions graphically, via a pure HTML interface which runs on any device or browser.

I developed an agreed strategy whereby this can progressively evolve with machine learning capabilities, both supervised (e.g. known successful solutions to similar symptoms) and unsupervised (e.g. preventative maintenance for common problems as a vehicle ages). I undertook some early work on candidate algorithms, but these will necessarily evolve in parallel with the exploitation of ID Workshop and as its data set grows.

File not found.