What an AI programmer actually builds
"AI" covers a lot of unrelated work. These are the shapes real projects take, and a short call usually narrows it to one.
- Prediction from your records
- A neural network or gradient-boosted model trained on historical data to predict an outcome from measured inputs: pricing, material selection, demand, risk scoring, yield. This is the most common request and the one with the clearest payback.
- An assistant that knows your business
- A retrieval system (RAG) that answers strictly from your own documents with citations, and updates the moment those documents change. Usually the cheapest route to "an AI that actually knows our pricing and policies".
- Computer vision
- Models that read images: defect detection, counting, grading, quality inspection, document and receipt scanning.
- Filipino and Taglish text
- Sentiment analysis, classification, and extraction on Filipino and Taglish content, which most off-the-shelf tooling handles poorly because it was trained on English.
- Automation around the model
- The wiring that makes a model useful: connecting it to your website, email, CRM, or an automation layer like n8n, so it runs inside the workflow your team already opens every morning.
- The data pipeline underneath
- The unglamorous half of every project. Getting your data out of spreadsheets, PDFs, and legacy systems into a shape a model can learn from is usually most of the work.
