Send me a mess. We'll find the right answer together — not a bare-minimum bandaid. Get back a solution that runs on its own and evolves with you.
AI building is everywhere right now. But the hard part usually is not model selection or even building with AI tools. The hard part is deciding what should be automated, what should stay human, what data can be trusted, what happens when the system is wrong, and how the work actually survives contact with real teams. Sometimes the answer is software. Sometimes it is a dashboard. Sometimes it is a better workflow, a better rubric, or a document people actually use. The model is one tool. The system around it is the work.
AI where it helps. Operator judgment where it matters. Boring software where the answer can't be wrong.
I help teams turn messy, human-heavy workflows into reliable systems: map the actual process, choose what to automate, connect the source-of-truth data, and build the review loops, dashboards, and tools that make it work in practice. Where the work usually breaks out:
Classifying messy inbound. Drafting vendor replies. Interpreting an ambiguous memo. Searching a policy doc. Summarizing legal news. Proposing a mapping. Flagging the thing that looks off. These are the tasks where a model earns its keep — anywhere language and ambiguity are the bottleneck.
Decide what to automate, what to keep human, what needs source-of-truth data, and where review belongs before the system reaches a customer, a partner, or a decision-maker.
Facts. State. Permissions. Proof. Payment status comes from Stripe — not from a model that's pretty sure. Customer state comes from Salesforce. Access comes from your permissions system. The output ties back to source data. Use code for the parts that can't be fluently wrong.
If the process is important, repetitive, and currently held together by people, spreadsheets, bubblegum & duct tape, and judgment passed down by tenure, I will help map it, automate the right parts, and ship it into production.