Grading pipelines, review queues and photo-to-data features, with a person checking the model's work where it matters.
First callWeek 1
Milestone pricedWeeks 1-2
You're worried the AI bill grows faster than the revenue.
We estimate running costs before building, then cap them in the product: spending limits per user, the model chosen on the server, and caching where it's safe.
Working buildsWeeks 2-7
Your evaluators can't keep up, and you can't hand grading to a model unchecked.
AI answer evaluation for ed-tech and test prep: an LLM drafts the grade on descriptive answers, a person reviews it where it matters, and roles keep evaluators, reviewers and freelancers apart.
You have hours of recorded classes and nobody to turn them into notes.
Lecture video to transcript to notes, automated over a message queue and surfaced in your admin CMS, so content teams review output instead of typing it.
Your users won't type it in, but they will snap a photo or write a sentence.
AI features that read a photo or a line of text and fill in structured data, like AI meal logging in a calorie tracking app, run through a server-side proxy with per-user quotas.
Tested, then liveWeek 8
You've seen what happens when an AI feature ships untested.
Before launch we try to break it: prompt injection, forged tokens, key confusion and cost attacks, in a test suite you keep. See QA and testing for the full audit.
We usually start with OpenAI's models and compare others for your case, testing the options before picking one.
Not unchecked. We ship grading with a person reviewing the model's work where it matters, which is how our test-prep work runs in production.
Spending limits per user, the model chosen on your server rather than in the app, and an estimate of running costs before any build.
Yes. We add AI to products that already exist, starting with a scoped look at your code and data.
15 or 30 minutes with the engineers who would write your code. If we're not the right fit, you hear it on that call.