AI / ML Development
Models trained on your problem.
When off-the-shelf isn't enough, we build custom machine-learning models and pipelines around your data, your metrics, and your edge cases, with evaluation and monitoring baked in from day one.
What's included
What we build
How we work
Discovery & scope
We start with a call to understand the problem, the constraints, and what success looks like, then agree a clear scope and timeline.
Design & architecture
We map the solution before writing code: the flows, the data, and the architecture, so there are no surprises mid-build.
Build & iterate
We ship in short cycles with working software you can see, give feedback on, and course-correct early.
Launch & support
We deploy, monitor, and hand over a clean, documented codebase, and stay available for what comes after launch.
What you walk away with
- A model trained and tuned on your data
- Reproducible training and data pipelines
- Clear evaluation against your metrics
- Monitoring to catch drift in production
Typical stack
Related work
AI Interview Platform
An end-to-end hiring engine that screens resumes, ranks candidates, and orchestrates AI-led interviews, deployed on AWS for elastic scale.
Frequently asked
When do we need a custom model vs. an off-the-shelf LLM?
If a general model already solves it, we'll tell you to use it. Custom ML makes sense when you have specific data, metrics, or edge cases a general model can't handle well.
What data do you need?
It varies by problem. In discovery we assess what data you have, its quality, and what's needed to hit your target metric.
How do you measure success?
We agree the metric up front and build an evaluation harness, so improvements are measured rather than assumed.
What happens after deployment?
We add monitoring for drift and performance so the model keeps working as your data changes.
Explore other services
Need ai / ml development?
Tell us what you're trying to build. We'll scope it with you in a 30-minute call.