Service 03
AI with a clear job and checkable results.
I build AI features for new and existing applications, from document handling and search over your records to support and learning workflows. For businesses in Claremont and the Pomona Valley, each build starts with a specific task, a way to evaluate the results, an estimate of running costs, and a review step where an error has consequences.
- Scoped around
- One job
- Cost modeled
- Before build
- Based in
- Claremont
01 — What you get
What ships.
A task with a clear success measure
We define what a useful result looks like and how to test it. That might mean time saved, response speed, or the issues a tutor expects an analysis to find. The measure belongs to the task, and we agree it before building.
Output a person can check
Answers can be grounded in your documents, records, and history, with the source attached. We decide what needs a person's approval, what the software can verify, and what should stop when there is too little evidence.
Cost and failure modeled up front
We estimate per-request costs before building, then scope spend limits, a fallback for slow or failed requests, and caching where repeated work makes it useful. The design accounts for what happens when the provider cannot return a usable result.
02 — How it runs
Test the approach before integrating it.
Step 1
Find the job worth doing
We look at the work you want to improve or the capability you want to build, and pick a task with a clear success measure. This is also where we compare AI with a simpler approach, before committing to implementation.
Step 2
Build a narrow version
The smallest thing that does the job on your real data, evaluated against examples where you already know the right answer. Cheap to change while the approach is still in question.
Step 3
Handle failures and integrate
Once the approach meets the agreed checks, I build the error handling, usage limits, monitoring, and review workflow into the application. The result reaches the person or process that needs it.
03 — Proof
A tutor found eight issues. The system found one.
Eeva is a pre-launch English-refinement app for advanced Korean speakers. That gap appeared in one sample during a tutor audit of three learners. I split finding possible issues from verifying them, so each stage can be measured and tuned on its own.
Read the case study04 — Questions
Before you ask.
Is this just adding a chatbot to my site?
A chatbot is one possible interface. The starting point is the task: reading incoming documents, drafting a first response, routing a request, or searching your records in plain language. Once we know who needs the result and how they will check it, we can decide where it belongs in the workflow.
What about our data — does it go somewhere we cannot see?
That is a decision we make explicitly, not a default you inherit. Which data leaves your systems, which provider receives it, what their retention terms are, and what stays local are all part of scoping. If the honest answer is that the data cannot leave, the design changes to match.
How do you know the output is actually right?
I compare the output against examples where we know the expected result, then design the review around the cost of a mistake. A person can approve a draft, check the attached source, or review a decision before it changes a record. Constrained formats help software validate an output's structure; they do not prove that its meaning is correct.
What if AI turns out to be the wrong tool for this?
Then I recommend the simpler approach. A rule can route a request, a search index can retrieve a record, and a better form can collect information that would otherwise need interpreting. We compare the work each approach saves, its running cost, and how it can fail before choosing what to build.
What should the system help someone do?
Describe the task, who needs the result, and what a mistake would cost them. It might be work that consumes an afternoon each week or a capability you want to build into a product. That gives us a starting point for deciding whether AI can help and how to judge the result.