AI and automation consulting

Where to begin

Start with recurring work, clear inputs, and a person responsible for checking the result. Examples include:

• Recurring reports. Gather information from approved sources, prepare a draft, and flag missing or conflicting data for review.
• Document intake. Extract information from incoming documents and route incomplete or unusual cases to the right person.
• Internal information. Help staff find relevant procedures and reference material, with sources they can check.
• Workflow handoffs. Connect intake, review, and system updates where staff currently copy information between tools.

Choose tools that fit the work

We compare software you already have, available AI products, and custom development. The assessment covers the task, available data, integration requirements, operating costs, and who will support the result.

Readiness workshops bring the people responsible for the process into that discussion. We document how they handle exceptions, which decisions need judgment, and what information is missing.

Build, test, and prepare the team

Start with a defined workflow and agree on how it will be evaluated. We configure tools, connect systems, and test representative cases with the people who will use the results.

We also address data access, human review, staff guidance, documentation, and responsibility for maintenance. The pilot gives your team evidence for deciding whether to continue, change the approach, or stop.

Scope and deliverables

We provide an initial assessment, tool evaluation, pilot implementation, and ongoing part-time support. Deliverables include a workflow map, tool comparison, working prototype, evaluation findings, and operating guidance.

We agree on the scope, responsibilities, and deliverables before work begins.

Research informed by the work

Our work on automation brought us repeatedly to the problem of data leakage. We developed TN-Proto to address how sensitive information is protected and shared between applications. That research informs how we approach data handling in AI workflows.