Where to apply ML for measurable ROI — without over-engineering
Why ML and AI consulting
ML and AI is everywhere — but most teams aren't sure where to start, how much it should cost, or which approach (managed pipeline vs hosted LLM API vs self-hosted open-source) fits their constraints. We've shipped ML and LLM systems for over a decade, and we'll tell you straight which use cases will return on investment for your business and which are hype.
After a consultation with us, you'll know which approach fits, what infrastructure you actually need, and what to budget — so you can decide build-vs-buy without guessing.
Our clients use ML & AI consulting to:
decide which approach fits — managed ML platforms, hosted LLM APIs, or self-hosted open-source models
evaluate ROI before committing to a full build
pick the right model for the use case (RAG, fine-tuning, agentic workflows, classical ML)
design data and retrieval pipelines — privacy, sovereignty, latency, cost
audit existing AI features for accuracy, hallucination rate, and prompt safety
add ML/AI features to existing web and mobile applications
perform advanced data analysis and turn it into business decisions
Contact us for an ML & AI consultation.
We also build the systems we recommend — see our Machine Learning & AI solutions page for the technology stacks and deployment layers we deliver.