During my time as Head of Professional Services for a private Genomics based company I spearheaded an initiative to utilise an open source offline LLM for analysing our Jira, Confluence and Salesforce docs for quick retrieval of process and technical information.

The purpose was twofold,

a) To create a common ‘search’ tool in order to locate a source of truth as to what was configured and where, and

b) To be able to generate process documents very quickly in whole or in part through the use of generative AI.

As these were sensitive company documents, use of a service like ChatGPT was restricted, which is why we opted to trial PrivateGPT with Llama-2-7b amongst other models.

We found RAG to be less than perfect, but it did effectively allow us to search out the right documents. Text generation was better in that we managed to produce good content as an initial template for a process guides, as well as feeding a full process document to generate a RASCI chart. We ran the pilot service on AWS EC2 infrastruture.

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This script I wrote essentially takes a python version or an ansible version and will loop through checking out each branch then printout which versions of Kubespray are supported.

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I was asked to provide a health check with insights into the clients Discovery operations. I created a custom application called Pithos – a python-based application which collected data from CMDB and Discovery. This data was then imported into Power BI and we were able to present the following back to the client:

• High disk utilisation
• Presence of core dumps
• Segregated architecture, but no network segregation
• Sensitive data such as plaintext usernames and passwords stored in unstructured data fields
• Scan schedules impacting performance

This was followed up with a roadmap of improvements for the client with the option of being delivered by my company.

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I worked with presales to deliver a $55k project with insights into the clients Discovery operations created with Pithos – a custom built python-based application which collected data from CMDB and Discovery. This data was then imported into Power BI and we were able to present the following back to the client:

• Sensitive data filters not capturing plaintext username/password
• Under-specced Outpost sizes causing performance issues
• Active user accounts from departed users not removed
• Various data quality issues
• Configuration discrepencies between prod and dev environments

This was followed up with a roadmap of improvements for the client which included development of a runbook, and 10 consultancy days of remediation.

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I developed this with a mixture of Bash and Python, exporting the results to JSON and then converting to CSV. All code was handed to the client.

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