I was technical lead on a project worth $200k to implement the Helix Discovery SaaS solution. Since scanning hybrid infrastructure requires on-prem components there was still substantial Solution Design work required.

At this time the SaaS product was less than 18 months old and there were gaps in features and functionality in how it worked compared to the on-prem version.

This led to challenges with unkown behaviour and bugs in the collector/outpost components which had to be resolved whilst maintaining client confidence in the solution they had bought. All bugs were logged and tracked with the vendor, but thankfully sufficiently minor that they did not delay implementation of the solution.

The customer was also given value added data quality reporting allowing them to identify various gaps and misconfiguration across their estate.

AI Led Operational Improvements

I worked in presales to present insights into their ITSM operations gathered from Talos back to the client. My involvement was in data cleansing and preparation for injesting into Talos for supervised and unsupervised learning. Once Talos had created the reports, I would then import these to PowerBI to develop the insights. With AI we were able to identify:

• Issue candidates for automation e.g. “password reset”
• Categorical data located in unstructured notes fields
• Under-utilisation of knowledge and overuse of top 5 knowledge articles suggesting need to break down into multiple topics
• Poor knowledge tagging to incident correlation, impacting the performance of the chatbot
• High MTTR for self-service leading to higher phone-resolved issues with shorter MTTR
• Discovery of sensitive (GDPR) information in unstructured text fields

This was followed up with an offer to the client to deliver the recommendations such as service desk training, restructuring categories, data cleansing, and knowledgebase improvements.

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