Services · Intelligence
Machine learning engineered for production
From matching algorithms to fine-tuned language models, we build the data and ML systems behind intelligent products, and run them reliably in production.
Why it matters
Models that work in a notebook often fail in production. Data is messy, costs grow and nobody can tell if accuracy is slipping.
Reliable data pipelines feeding models and dashboards
Models measured against clear, business-level metrics
Automated retraining and monitoring
Lower inference cost through the right model size
What's included
End to end, one team.
- Data engineering
- Pipelines, warehouses and feature stores that keep data clean and current.
- NLP & matching
- Parsing, classification, entity extraction, ranking and recommendation.
- Fine-tuning
- Adapt open-weight and hosted models to your domain and tone.
- Evaluation
- Benchmarks, golden datasets and regression tests for every model change.
- MLOps
- Versioning, CI for models, deployment, monitoring and drift alerts.
- Computer vision
- Document, image and video understanding for real workflows.
How it works
Four steps to launch.
- 01
Data audit
Assess data quality, gaps and labelling needs.
- 02
Baseline
Ship a simple, measurable model first.
- 03
Improve
Iterate with evaluation, fine-tuning and better features.
- 04
Operate
Deploy with monitoring, retraining and cost controls.
FAQ
Good questions
Do we need a lot of data to start?
Not always. Modern foundation models let us start with small, well-chosen datasets, then improve as data grows.
Let's build
Have a project in mind?
Tell us what you're building. We reply within one business day.