I build practical AI systems where models, software, data, and product requirements have to work together.
Based in Kampala, Uganda; available for remote collaboration with clients internationally.
An AI feature is useful only when it improves a defined task and behaves acceptably when the model is uncertain, unavailable, slow, or wrong. The engineering starts with the workflow, the data it can legitimately use, the cost of an incorrect result, and how a person will review or act on the output.
Identify whether the task needs generation, retrieval, classification, extraction, computer vision, or ordinary deterministic software. Compare hosted APIs and deployable models against accuracy, privacy, latency, cost, and maintenance constraints.
Define representative test cases, expected outputs, refusal and fallback behaviour, input validation, access controls, and human-review points. Do not treat fluent output as proof of correctness.
Connect the model to application data and user workflows with bounded permissions, logging, monitoring, rate limits, and cost controls. Keep critical business rules outside the model where they can be tested directly.
AI is a poor fit when a simple rule or search query solves the problem more reliably and cheaply. It is worth evaluating when language, images, ambiguous information, or large volumes of unstructured data make conventional automation impractical. The first step is to test the use case against representative examples before committing to a production design.
Can AI decisions be fully automated? That depends on the consequence of an error and the evidence from evaluation. Sensitive or high-impact decisions should retain appropriate human review and accountability.
Go deeper into the engineering practices and production trade-offs behind this service.
If you have a product, workflow, system, or technical problem that needs to become real software, start with the requirements and constraints. I can help turn them into an implementation plan and a production-focused build.