"Add AI" is rarely the right brief. The right brief is usually "reduce churn", "personalize the catalog", "automate Tier 1 support", or "forecast demand". We start with the business problem and choose the model class — LLM, classical ML, or rules — that fits.
What we deliver
- LLM applications: RAG systems, agentic workflows, structured-output extraction, fine-tuning when prompting hits its ceiling.
- Recommendation engines: from cold-start collaborative filtering to two-tower neural retrieval, hosted on your infrastructure.
- Forecasting: demand, churn, fraud — Prophet, XGBoost, deep learning where the data justifies it.
- Computer vision: classification, detection, OCR, document understanding — on-device or cloud.
- MLOps: training pipelines, feature stores, model registry, online evaluation, drift monitoring.
Production discipline
Every model we ship has an evaluation harness, an SLO for accuracy and latency, fallback behavior when the model is unavailable, and a kill switch the operations team can pull. ML in production is software engineering, not data science.