Alpes AI Platform

Add an AI stack to your product in minutes using APIs.

Powered by Alpes NN(patented), a faster alternative to deep neural networks.

Try Alpes AI free Check out API

Do the following problems sound familiar?

Trouble finding the right data engineering team?
What are the right features for your data set?
Which is the best neural network architecture for your problems?
How do you handle incremental training data?
Do you worry about giving all your data to FAMG(Facebook/Amazon/Microsoft/Google)?
Do you start your training, wait for days only to find that the data is not enough or DNN architecture is wrong?
How can you be sure that the current AI system you have is the best?

Well now there is an easy way to do AI, Alpes AI Platform.

Deep neural networks are not the only way to do AI.

Try the Alpes approach and see the difference.

Why us?

Our patented algorithm is awesome! A true alternative to deep neural networks, it has all the advantages of DNNs with none of it's problems.


Alpes has the fastest learning algorithm with O(NlogN) time complexity. This will allow you to iterate training with your data fast to find the right feature set.

Incremental Learning

Algorithm can perform incremental learning. When fed with new training data you don't need to run the whole training process again. This will allow your products to imbibe new data on the fly while making decisions.


Clear understanding of why the system is learning or not learning. AI is not a blackbox anymore. This will allow you to fine tune your data and get the right data to make your product learn better.

Clear APIs

Well defined generic train and test APIs for all kinds of data. Specific APIs for speech, vision and text problems. Easily integrate into your developer stack.

Multiple hosting models

Host the trained models yourself so that your data never leaves you. Or run the trained models from the Alpes cloud.

Case Studies

Alpes AI solving real world business problems


Enable voice and conversational intelligence into your products


Apply a natural language layer to your products


Integrate Computer Vision into your existing product

Endless Possibilities

Protoype Alpes NN solutions

Recommendation Engine

Power your shopping cart recommendation engine with Alpes NN.


Text classification, summarization and sentiment analysis have all given good results.


Disease prediction has worked well on public datasets.

Try the Alpes NN learning engine

As easy as 1,2,3

  • Be Part
    Of Our

  • API access

    We are currently in beta mode and access to API is invite only. Please signup here

  • Training

    Extract features from your data. Call the upload API and then the train API. You will have your trained model ready in minutes.

  • Test/Predict

    Use our test or predict API calls for testing or to make a prediction on your data.

  • Download model

    You can download the trained model and use it in your application or you can directly run the model from our servers.

Live demo of API in use



The above research work on Neural Networks, stems from our discovery of an algorithm that can separate N points in d-dimensional space by hyper-planes, in such a manner that all points are separated from one another by hyperplanes. This algorithm leads to a very important breakthrough in neural network research. Because it enables one to train a neural network and arrive at the configuration of the neural network and determine all the weights of the processing elements in a non-iterative manner
1. It takes only an order of NlogN multiplications, where N is the number of points (samples) in d-dimensional space.
2. The number of planes q needed to separate N points in d-dimensional space is (only) approx. q=log2(N); this is especially true when the dimension d of the space is large: N < 2^d
3. It is non- iterative and has to terminate successfully.
For example: 50,000 random points in a 25 dimensional unit sphere can be separated from one another by only 27 planes.

Advantages of the new method

A) Mathematical Basis: Most importantly , these new algorithms have a mathematical basis and water-tight proofs which guarantee their success whenever and wherever patterns are recognizable by given features and the algorithms are deterministic and non-iterative. The algorithm determines the architecture.
B) Accuracy: Whatever problems can be solved by the conventional Neural Network (NN) or Deep Learning technique can be solved by these new algorithms. Several particular cases have already been taken and solved to reinforce this view- viz
(i) digit recognition MNIST,
(ii) Face-recognition viz. Extended Yale data base, and
(iii) Alphabets.
C) Time: The time complexity of the algorithm is NlogN and is as fast as the FFT. For instance when we apply our method to the Extended Yale database consisting of approximately 11,000 face images each consisting of (30 X 30 pixels)i.e., 900 dimensions. The time taken to separate each data point (image) was only 40 milliseconds in a Laptop (21 planes were needed); and the time taken to classify 3700 test images was 2.5 minutes. By the conventional Back-propagation method the neural network took almost 4 days of training on the same machine.
D) Restart Feature: This method does not need to restart when new data is found during the learning process (or later). The algorithm can start learning from where it has left off. It also makes optimal use of information (as described above). One may contrast the above situation with the usual Back Propagation algorithm (discovered by Rumelhart, Hinton and Williams in 1986 and used in every NN and Deep Learning Application), where there is no proof and no guarantee of success and is also iterative and non-deterministic and even the initial architecture needs be guessed, and therefore solely based on trial and error.

Our Team

Founded in March 2018 by experts in Machine learning and Computer vision

Dr. Eswaran Kumar

CEO, M.S. (IIT Kanpur), Ph.D (University of Madras)

Dr. Pia Mukherjee

Vice President, Ph.D University of Cambridge

Sandeep Reddy

Principal Architect

Our Mission and Vision

We at Alpes are striving hard, to Apply our research for real-world impact in health, science, energy and many more domains. Alpes is one of the world leaders in artificial intelligence research and its application for positive impact.
Our motivation in all we do is to maximise the positive and transformative impact of AI. We believe that AI should ultimately belong to the world, in order to benefit the many and not the few, and we’ll continue to research, publish and implement our work to that end.

Our Customers

Contact Us

Headquarter in Hyderabad


+91 40 - 67263430


T-HUB IIIT-H Campus, Gachchibowli Hyderabad TS 500032 India


R&D centre

Villa No 8, Villa Springs, Kowkur Village, Bolarum Secunderabad 500010 India