Machine learning
Machine learning models that learn from your data
We prepare your data, build and deploy models with TensorFlow, PyTorch and scikit-learn, and keep them monitored in production.
A model is only as useful as the data and the pipeline around it. We build both, and explain what the model decides.
Data cleaning and feature engineering, training and tuning, deployment with versioning and monitoring, and interpretability so your team can trust the results.
What we build with machine learning
Four parts of every ML project we take on.
Deep learning and neural networks
Models in TensorFlow, PyTorch and Keras: CNNs for computer vision, RNNs for time series and transformers for language.
ML operations and deployment
Production pipelines with MLflow, Kubeflow or SageMaker, with automated training, versioning, A/B tests and monitoring.
Feature engineering and data processing
Cleaning, normalization, dimensionality reduction and feature selection that turn raw data into useful features.
Model optimization and explainability
Tuning for accuracy, inference speed and model size, with SHAP and LIME to show why a model predicts what it does.
The tools we build models with
- TensorFlow
- PyTorch
- Scikit-learn
- Keras
- Hugging Face
- Python
Six steps take a product from first call to launch. The team that starts it stays to support it.
Discovery
Your goals, users and constraints, turned into a roadmap.
Design
Interfaces and flows built on research and your brand.
Build
Sprints with working software to try every week.
QA
Manual and automated tests before every release.
Launch
CI/CD pipelines and cloud setup for repeatable releases.
Support
Monitoring, fixes and improvements after launch.
01
One team from first call to support
The people who scope your product also design, build and support it.
02
Working software every week
Weekly demos and reviews let you see progress and change course early.
03
Direct access to engineers
You talk to the engineers building your product, and one project manager keeps the plan.
Products we built
Neptune EHS and SayThread are products CodeSwift built. Each links to its own site.
EHS software for safety and compliance teams, a product CodeSwift built.
- Incidents
- CAPA
- Lockout/Tagout
- HazCom
SayThread
A voice ecosystem across your Android phone and your Windows or Linux computer, built by CodeSwift. In beta.
- 27 languages
- Untangle
- Rewrite by voice
- Meeting recaps
“Great products start with a clear direction and a team that can execute.”
Why work with us
Data work included
We handle collection, cleaning, feature engineering and validation, so the model starts from data you can trust.
Explainable results
SHAP and LIME show which inputs drive each prediction, which helps with trust and regulatory review.
Progress you can see
We work in iterations, so you see results early and choose the next step with evidence.
Built for production
Models ship with versioning, monitoring and retraining, so they keep working after launch.
FAQ
What kinds of ML problems can you solve?
Supervised learning, forecasting, computer vision and language problems, from classic models to deep neural networks, chosen for your data and goals.
Do you handle data preparation and cleaning?
Yes. We handle the whole data pipeline, from collection and cleaning to feature engineering and validation.
Can you explain how your models make predictions?
Yes. With tools such as SHAP and LIME you see what a model predicts and why, which matters for trust and compliance.
Startyour projectwith CodeSwift
Tell us what you want to build and we'll plan the first steps with you.
Get in touchPrefer email? Write to our teams in Rawalpindi and Salt Lake City.
connect@codeswift.org