Machine learning engineering.
Machine learning starts with a well-defined question and data that can answer it. We frame the problem, build and evaluate models, and put the pipelines in place to run them reliably in production.
Models that hold up after launch.
A model that scores well in a notebook still has to work with tomorrow’s data. We begin by defining what is being predicted, how it will be used, and how success is measured, then assess whether the available data supports it. From there we build baselines before complex models, evaluate honestly on held-out data, and set up the pipelines, versioning, and monitoring that let a model be retrained and trusted over time.
Problem framing
Translate a business question into a measurable ML task, and identify when rules or analytics would be the better answer.
Predictive models & classification
Supervised models for scoring, categorization, risk estimation, and detection, evaluated against a clear baseline.
Recommendation systems
Ranking and personalization approaches matched to your catalog, interaction data, and cold-start constraints.
Forecasting
Time-series models for demand, usage, or capacity planning, with uncertainty communicated rather than hidden.
Data pipelines
Reliable ingestion, cleaning, and feature preparation so training and production use consistent data.
MLOps
Experiment tracking, model versioning, deployment, and drift monitoring so models can be maintained and retrained.
Clear deliverables.
Shared direction.
Every project is scoped around your needs. These are typical outputs we define together at the start.
Explore our process- A problem definition and data readiness assessment
- Baseline and candidate models with an evaluation report
- A deployed model service or batch pipeline as scoped
- Training, versioning, and monitoring setup
- Documentation for retraining and handover
- Companies with historical data and a recurring decision to support
- Products that would benefit from personalized recommendations
- Teams planning inventory, staffing, or capacity with forecasts
- Organizations whose existing models need reliable pipelines and monitoring
- Python
- pandas
- NumPy
- scikit-learn
- XGBoost
- PyTorch
- MLflow
- Airflow
- dbt
- FastAPI
- Docker
- AWS SageMaker
- Google Vertex AI
- PostgreSQL
Your questions, answered.
How much data do we need for machine learning?
It depends on the problem, the number of signals, and how noisy the outcome is. A data readiness review looks at volume, quality, labels, and history before committing to a modeling approach.
Can you guarantee a level of model accuracy?
No. Achievable performance depends on the data and the problem. We agree on evaluation metrics and a baseline early, report results honestly, and discuss whether they are good enough for the intended use.
What is MLOps and do we need it?
MLOps covers the practices that keep models working in production: reproducible training, versioning, deployment, and monitoring. Its scope should match how often the model changes and how much depends on it.
Should we use machine learning or a language model?
Structured prediction from tabular or time-series data is usually a better fit for classical ML, while language tasks often suit LLMs. Some products combine both, and discovery helps decide.
From our insights.
RAG vs Fine-Tuning vs Prompting: Which Does Your LLM App Need?
Retrieval gives a model knowledge, fine-tuning shapes its behavior, and prompting is where you should almost always start. Here is how to choose.
How to Evaluate LLM Features Before and After Launch
LLM features need their own testing discipline. A practical framework for eval sets, metrics, human review, and regression checks that hold up in production.
Often paired with.
AI development
Turn a real business problem into a usable AI-powered product or feature.
ExploreGenerative AI
LLM features, knowledge assistants, and agents designed with evaluation and guardrails.
ExploreBackend & cloud
APIs, databases, integrations, and cloud infrastructure your product can rely on.
Explore