Intelligent Systems That Learn
Applied AI and machine learning for real products: prediction, classification, automation, and retrieval. Data pipelines, model training, evaluation, and deployment—built to run reliably in production.
AI work is only valuable when it connects to business workflows: reducing manual effort, improving decision-making, or enabling new product features. This service is about building practical systems—models and pipelines that are measurable and maintainable.
The process starts with defining a target metric and a baseline. From there, we iterate on data quality, features, evaluation, and deployment so results are grounded in reality, not demos.
Tailored machine learning models for your use case: classification, regression, ranking, and recommendation. Trained on your data with a clear evaluation plan so you can measure whether it actually improves outcomes.
Neural networks for complex pattern recognition, including computer vision and NLP. TensorFlow and PyTorch workflows with attention to dataset curation, labeling strategy, and performance constraints when deployed.
Automation and agent-style systems when they make sense: decision support, workflow automation, and constrained agents with clear guardrails. The emphasis is on safety, observability, and predictable behavior—especially when systems can take actions.
ETL pipelines that keep data clean and consistent for training and inference. Versioned datasets, repeatable transformations, and monitoring to catch drift. Great models are usually the result of great data and disciplined evaluation.
Packaging models behind APIs, batch jobs, or embedded inference—plus monitoring for quality and drift. The goal is to move from “it works on my laptop” to a system that runs reliably and can be improved over time.
We start with a baseline. If a simple rule-based approach solves most of the problem, it may be the best option. If the problem needs ranking, prediction, classification, or information retrieval at scale, ML can be a fit. The goal is measurable improvement, not “AI for AI’s sake”.
Not always. Many useful systems start with modest data plus strong evaluation and iteration. The more important question is whether you can define “correct” outcomes and whether your data reflects real usage. Data quality, labeling, and feedback loops often matter more than raw volume.
Yes. Production ML is mostly engineering: packaging, monitoring, drift detection, and safe rollouts. Models often live behind APIs or batch jobs, with clear observability so you can see performance over time and improve it deliberately.
Guardrails are part of the design. That includes constrained outputs, human-in-the-loop approval when actions are taken, and logging so behavior is auditable. For agent-style automation, predictable behavior matters more than flashy demos.
Let's discuss your project and see how I can help.