AI & Machine Learning

TensorFlow & AI/ML Development

We build intelligent applications powered by machine learning — image recognition, natural language processing, predictive analytics, and recommendation engines.

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TensorFlow & AI/ML Development

Google's open-source ML framework — build, train, and deploy machine learning models at scale.

  • Image recognition systems
  • Document automation
  • Fraud detection
  • Demand forecasting
  • Recommendation engines
  • Chatbot NLP backends
Overview

Why TensorFlow?

TensorFlow by Google is the world's leading machine learning framework, used in production by Google, Twitter, Uber, and Intel. EdgeSys Technologies applies TensorFlow to solve real business problems: automating document processing, predicting customer behaviour, detecting anomalies, and personalising experiences at scale.

What We Build

Our TensorFlow Capabilities

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Computer Vision

Image classification, object detection, face recognition, and OCR using CNNs and transfer learning on TF2.

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Natural Language Processing

Text classification, sentiment analysis, entity extraction, and chatbot backends with BERT and Transformers.

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Predictive Analytics

Demand forecasting, churn prediction, fraud detection, and sales forecasting models integrated into your systems.

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Recommendation Engines

Personalised product, content, and service recommendations using collaborative and content-based filtering.

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ML Model API Deployment

TensorFlow Serving or FastAPI wrappers that expose your trained models as REST APIs for your applications.

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MLOps & Model Lifecycle

ML pipelines with data versioning (DVC), experiment tracking (MLflow), and automated retraining workflows.

FAQs

Common Questions About TensorFlow

What kind of AI problems can you solve?
We tackle classification (spam, fraud, intent), regression (price, demand forecasting), computer vision (images, documents), and NLP (text understanding, chatbots) using TensorFlow and PyTorch.
Do you need a lot of data for machine learning?
The amount of data needed depends on the problem. We can use transfer learning with pre-trained models (BERT, ResNet) for good results even with smaller datasets.
How do you integrate ML into an existing application?
We deploy models as REST APIs using TensorFlow Serving or FastAPI, which your existing application calls — no changes required to your core application architecture.
Is TensorFlow or PyTorch better?
Both are excellent. TensorFlow is stronger for production deployment (TF Serving, TFLite). PyTorch is preferred for research. We use both and choose based on the project's deployment requirements.
How long does it take to build an ML model?
A proof-of-concept model takes 2–4 weeks. A production-ready ML feature integrated into your application typically takes 6–12 weeks including data preparation, training, validation, and deployment.
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Ready to build with TensorFlow?

Our TensorFlow experts are ready to turn your idea into a production-ready solution. Get a free consultation today.

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Related Technologies

We Also Work With

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React

Frontend Development

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Next.js

Full-Stack Development

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Laravel

Backend Development