Explainable AI

Explainable AI
Proprietary patented AI algorithm .
Approach to AI with mathematical proofs.
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Fastest training
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Smallest Footprint
20 times smaller embeddings then deep learning embeddings
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Our Vision is To Make AI Explainable and Affordable.


with Current AI

The foundation of all recent AI is deep learning which is a black box and unexplainable

Deep learning models are costly to create and store.

Huge Storage and Compute Costs
MLOps has become very important

Time Consuming Activity
Training and retraining takes a lot of time.

Deep learning is costly and concentrated in the hands of a few


Solution - KE Sieve

Minimal computing. Works on CPU and GPU.

Fast training

Incremental learning


Provides an approach to AI with mathematical proofs.
Basis for foundational models without DNN


Neural Search - Neural++

Alpes embeddings are 438(bit) embeddings

Quick and Seamless Integration

Unlock Value from Business Process using GPT-3 with Alpes

Generative Pre-Trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text. It is the third-generation language prediction model in the GPT-n series created by OpenAI.

We can help you leverage the power of GPT-3 for your products.

Our Offerings

Data Intelligence

We have delivered the data solution for recommendation engines, summarisation tasks, risk assessment, fraud detection and more.

Language Processing

We have built various text models for recommendation engines, conversational AI, contact center agent assistance, next word prediction, summarisation and more.

Image Processing

Alpes team has been researching for years on new image processing algorithms and we are pioneer in this space. We have delivered solutions such as removing background, cartoonize, video tagging, objecting detection and identification and more.


Alpes integrates multiple AI module for process automation. We have delivered process automation for automated building of slide deck, contact center customer classification and prospect identification, amazon dynamic product combo building and more.

Case Studies

Natural Language Processing

Problem: Improve agent productivity suggesting right answers to customer questions.

Solution: Collected 650 tickets and applied NLP techniques like stemming, lemmatization to extract the core of the tickets. The core sentences were classified using Alpes algorithm to form the base model. This model learns incrementally every day.

Result: 90% accuracy in showing relevant results.

Image Processing

Problem: Build a background removal tool from photos.

Solution: Using image processing techniques we identify the main object in focus. The object pixel co ordinates are identified and the remaining pixels are converted to white..

Result: Background was removed properly in 90% of cases..


Problem : Automate generation of presentation based on user inputs provide prediction of what presentation should contain and how content has to be arranged

Solution: Multiple prediction module such as Chart Prediction, Icon prediction, Diagram prediction, free space prediction on images, title and sub-title prediction were and added in a work flow to achieve the required automation

Result: Solution was successfully delivered and its live now for users.

Data processing

Problem: To build classification model to identify between fraudulent/Non-fraudulent transactions.

Solution:Using Attributes and transactions data for remitter and beneficiary, applying feature engineering and domain expertise to lower false positives, Tuning of feature space and achieving the best accuracy rate from the various detection models

Result:Reduced fraud related costs, Relationship analysis of fraudulent networks and collusions, Improved data credibility, uncovered hidden correlations.

Why Us ?

Fastest training

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.


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.

Fraction of costs for storage and compute

Model size storage and compute savings of more than 20 times


Dr Kumar Eswaran


Leading the research in Alpes, Dr. Kumar carries 37 years of research experience in the application of computers in the areas of industrial image processing, AI and pattern recognition, electromagnetics, fluid mechanics, and structural mechanics. He has used many numerical techniques such as Finite Element Methods (FEM), Finite Differences, Boundary Integral Equation (BIE), and minimization methods to solve various problems.