Data science services that turn your data into decisions

Data pipelines and warehouses, dashboards, scoring and forecasting models, anomaly detection, and LLM-based extraction from text. We built the multi-shop analytics layer for AutoCar Intelligence and the 7-criteria scoring engine behind StaffUp.

No. Most clients start with data in a CRM, Shopify, an app database, and a few spreadsheets. We build the pipeline into one Postgres warehouse as the first step, usually on Supabase.

Less than you think for scoring and anomaly detection, more for forecasting. In Discovery we look at what you have and tell you in writing which questions the data can answer now and which need more history.

Both, picked per problem. Forecasting and scoring usually use classical models because they are cheaper, faster, and easier to explain. LLMs do the text work: pulling fields out of emails, transcripts, and documents.

Every model ships with monitoring on its inputs and outputs, a holdout set it is scored against, and a retraining schedule. If accuracy drops, you get an alert, not a surprise.

Postgres and Supabase, Python with pandas and scikit-learn, Recharts or your existing BI tool for dashboards, n8n for scheduled jobs, and Claude or OpenAI for text extraction.

A 1 to 2 week Discovery with a fixed fee and a written quote. Then a fixed-scope build for the first pipeline, dashboard, or model. Ongoing modeling and monitoring runs as a monthly retainer.