Modern tools.
Clear boundaries.

Useful assistance.
Human responsibility.

I use automation and AI where they help investigate problems, understand complex systems and prepare clearer documentation.

I remain responsible for the analysis, recommendations and work delivered. AI output is checked against source evidence; proposed changes go through the agreed review and testing process.

We agree the information, tools, processing location and oversight before AI is used with your material. If a proposed tool or information flow changes, we review that change with you first.

“Local” should name
a real boundary.

A model on my workstation and a model inside your infrastructure are different arrangements. The proposal identifies which one we mean.

Local inference means the selected model processes its input on that machine. Other parts of a workflow may still use external services, including embeddings, integrations, updates or telemetry.

We inspect those information flows, access controls and logs before making claims about where information stays. Storage, retention, deletion and operational ownership are agreed too.

Model suitability is evaluated using representative tasks. Local processing is a deployment choice, not a guarantee of confidentiality, accuracy or equivalent performance to every hosted model.

Give AI relevant context.
Make that context reviewable.

ContextDigger describes structured code areas and governed context bundles for use with coding assistants.

Where suitable, I can use it to organise the relevant code and documentation, define context boundaries and prepare bundles that your team can inspect before use.

That supports a repeatable investigation or development workflow. We still need to verify what the assistant can actually access, how its provider processes information and which actions its tools can take.

ContextDigger supports context organisation. It does not, by itself, establish a complete security boundary or replace provider controls, human review and testing.