§ Machine learning · big data analytics

The few points
that matter.

Vapnik is a machine-learning and data-engineering studio. We build the pipelines, models, and analytics that turn your company’s data into decisions — and we keep them running in production.

Start a project Read the method ↓
Fig. 1 — Maximum-margin separation. Of the 60 points plotted, 3 (ringed) determine the boundary. Hover any point ·

§ Services

What we build

Four capabilities, one standard: if it can’t be measured, it doesn’t ship.

Fig. 2 — ingest → transform → serve

Data platforms

Warehouses, lakes, and pipelines that make your data dependable — batch and streaming, modeled, tested, and documented.

Spark · Kafka · dbt · Airflow · Postgres · BigQuery

Fig. 3 — validation, not vibes

Machine learning systems

Forecasting, ranking, anomaly detection, computer vision — trained on your data, evaluated honestly, deployed with monitoring.

PyTorch · scikit-learn · XGBoost · MLflow

Fig. 4 — grounded generation

LLM & applied AI

Retrieval, agents, and document intelligence built on frontier models — with evaluation harnesses so you know they work before your customers do.

Claude · RAG · agents · evals · fine-tuning

Fig. 5 — effect, with error bars

Analytics & decision science

Experiment design, causal inference, and dashboards that answer the question you actually asked.

A/B testing · causal inference · Metabase · Looker

§ Method

How an engagement runs

  1. 01

    Frame

    Name the decision, the metric, and what “better” is worth. No modeling until the question is priced.

  2. 02

    Data

    Audit what exists, then build the pipeline that makes it reliable. Most projects are won or lost here.

  3. 03

    Model

    The simplest model that generalizes. We add capacity only when the evidence demands it — never before.

  4. 04

    Ship & monitor

    Deploy, instrument for drift and quality, and hand over documentation and runbooks your team can operate.

Every engagement starts with a fixed-scope discovery: one to two weeks, a written findings memo, and a build plan you can take anywhere — including away from us. No lock-in.

§ The name

Why “Vapnik”

“Nothing is more practical than a good theory.”

— Vladimir Vapnik, The Nature of Statistical Learning Theory

Vladimir Vapnik co-created statistical learning theory — the mathematics of why models generalize instead of memorize. The name is a promise about our bias: theory in service of software that ships. Simple models before complex ones. Evidence before architecture. Measurement before scale.

Vapnik is run by Pascal Matta, a machine-learning engineer in Seattle — one senior engineer on your problem, not a bench of juniors behind a partner.

§ Contact

Start with the decision you’re trying to make

Tell us what you’re trying to predict, automate, or understand. We’ll reply within two business days with an honest read on whether machine learning is the right tool — and what a first step costs.

Prefer email? [email protected]