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Custom Machine Learning Models
Models trained on your own data rather than an off-the-shelf API, built with TensorFlow, PyTorch, Scikit-learn, or Keras and evaluated against the metric your business actually cares about.
Custom Machine Learning Development Services
Build custom machine learning models, predictive systems, and data-driven product features with an India-based engineering team serving businesses across the USA. With 35+ engineers across ML, MLOps, and applied AI, we take a model from training data to production and keep it accurate once it is live.
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10+
Years of Industry Experience
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350+
Global Clients
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35+
Tech Engineers
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20+
Industries Served
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10+
Years of Industry Experience
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350+
Global Clients
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35+
Tech Engineers
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20+
Industries Served
Trusted by teams building serious products
Where Machine Learning Pays Off for Your Business
Machine learning earns its place when a decision repeats often enough that a rule-based system stops keeping up: which lead to call first, how much stock to hold, which transaction looks wrong, which support ticket is about to escalate. A model learns those patterns from your own history and keeps scoring them long after a static report would have gone stale.
Getting there takes more than a notebook. It takes clean training data, honest evaluation against a baseline, and somewhere for the model to actually run. Our ML engineers handle the full path — data preparation, feature engineering, model training and validation, deployment, and monitoring — with solutions compliant with GDPR and PCI-DSS. If a simpler approach beats a model on your data, we will tell you that instead of building one.
Machine learning is one part of a wider practice. If you are still deciding what to build, explore our complete AI development services for the end-to-end product view, or see how generative AI development handles the language and content side of the same problem.
Spotlight Our All-Inclusive Machine Learning Development Services
From the first labelled dataset to a monitored model in production, each capability below is engineering work we do in-house, not a wrapper around someone else’s API.
Did you know?
Did you know most teams already hold the data a model would need?
The history sitting in your CRM, logs, and warehouse is usually enough to train a first predictive model.
Why Choose Us as Your Trusted Machine Learning Partner?
Six reasons clients keep coming back to Zygobit for model work, from first prototype to production rollout.
Still deciding by gut feel? A model trained on your own history turns the repeat decisions into something you can measure.
Technology Stack Powering Our Machine Learning Work
The frameworks, data tooling, and MLOps platforms our engineers work in day to day, from training libraries like TensorFlow and PyTorch through to Spark, Kafka, and MLflow for pipelines, deployment, and versioning.
Python
R
C++
Machine Learning Applied to your Industry
The useful model is the one trained on your industry’s data and judged by your industry’s metrics. These are the sectors our ML engineers have shipped forecasting, scoring, vision, and language models for.
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/ 02Healthcare
/ 03Finance
/ 04E-Commerce
/ 05Manufacturing
/ 06Automotive
/ 07Energy
/ 08Transportation
/ 09Restaurant
/ 10Real-estate
/ 11Logistics
/ 12Fashion
Machine Learning We Have Shipped
Selected case studies where intelligent systems moved the metric in production, from models and pipelines to product features customers use every day.
Need autonomous agents that act on the predictions our ML models make? That's a different offering — purpose-built tooling, evals, and observability for production agents.
Frequently asked questions about machine learning & AI
What founders and operators ask before starting an ML or AI initiative with us.
Machine learning fits decisions you make repeatedly on data you already collect: forecasting demand, scoring leads or credit risk, predicting churn, flagging anomalies and fraud, reading documents and images, and sorting or searching text. If a rule-based system keeps needing new rules to stay accurate, that is usually the point where a model earns its place.
Yes. We train models on your own historical data rather than wrapping a general-purpose API, using TensorFlow, PyTorch, Scikit-learn, or Keras depending on the problem. Every model is measured against a simple baseline first, so you can see what the model adds before it goes anywhere near production.
Enough history of the outcome you want to predict, plus the fields that plausibly explain it. That usually lives in the systems you already run, such as your CRM, transaction records, product logs, or a data warehouse. If the data is messy or incomplete, data preparation and feature engineering are part of the work rather than a prerequisite you have to finish alone.
Yes. Most of our model work connects to software that is already live. A model is typically served behind an API your product or internal tools call, so predictions appear inside the workflow your team already uses instead of requiring a separate system or a rebuild.
We deploy and version models with MLOps tooling such as MLflow, Kubeflow, DVC, and Vertex AI, and keep the training pipeline reproducible. Once live, accuracy is monitored so drift shows up as data changes, and the model can be retrained on newer data rather than quietly degrading.