SPC-04 / Service
Custom AI & ML model development
When an off-the-shelf model is not enough, we develop custom AI and ML models on your data: demand forecasting, classification and scoring, document understanding, computer vision and fine-tuned language models. Each model ships with evaluation, monitoring and a retraining pipeline, so it keeps performing after launch.
01 / Capabilities
Data readiness
We assess the data you have, fix quality issues and agree a baseline before promising any accuracy.
Model development
Classical ML, deep learning or fine-tuned foundation models, chosen for the problem, not the trend.
Evaluation
Hold-out tests, bias checks and business metrics, so you know how the model behaves before it goes live.
MLOps
Versioned data and models, deployment to your cloud, drift monitoring and scheduled retraining.
02 / How we deliver it
phase 01
Feasibility
A short study on your data that tells you honestly whether a custom model will beat the simple option.
phase 02
Prototype
A first model with measured results against the agreed baseline.
phase 03
Production
API or batch deployment, monitoring and integration into the workflow that uses it.
phase 04
Improve
Retraining and evaluation on a schedule as new data arrives.
03 / What changes
- Models measured against a clear baseline
- Deployed in your cloud and region
- Drift caught before it hurts results
- You own the models, code and training data
guides on custom ai & ml model development
04 / FAQ
Questions about Custom AI & ML model development.
How much data do we need?
It depends on the problem. We start with a short feasibility study on your real data and tell you honestly whether a custom model will beat a simpler approach before you commit to a build.
Where are the models hosted?
In your own cloud account and region by default, so data residency rules are met and you keep full control of the models and training data.
What happens when the model gets worse over time?
Monitoring tracks data drift and accuracy. When performance moves outside the agreed range, a retraining pipeline runs and the new version is evaluated before it replaces the old one.