Top 7 AI Startups of Delhi

June 27, 2022

AI is now present in almost every facet of our life, including navigation, smart living, travel, education, and money. In actuality, AI's swift takeover is only the beginning. And AI startups are here to prove it by developing previously unimaginable solutions to real-world issues and gaining large-scale investment interest. 

In 2021, Indian AI startups received a total of $1,108 million in investment. The greatest financing amount in the last few years increased by 32.5% year over year. The number of AI startups that obtained investment increased from the previous year, indicating the development of India's AI startup ecosystem.

With the widespread deployment of AI in businesses, the need for AI-driven services has skyrocketed in recent years. The view of the finest artificial intelligence businesses in Delhi is also important since they undertake operations with consumer advantages in mind. 

To assist you here is the compiled list of the best AI startups in Delhi:

1.  Data Neuron-

Based on label heuristics and settings, DataNeuron delivers fully automatic annotation/labeling with minimum validation. Because supervised learning is the most common technique in AI, the need for labeled data has skyrocketed in order to remove limits while building AI solutions. Instead of other platforms, this platform just requires a Masterlist, as well as incremental and developing annotation. It adjusts the Masterlist in real time, which is referred to as Dynamic Masterlist Support. It's an end-to-end machine learning lifecycle management platform that includes AutoML, no-code prediction, and optimization. 

Founded in 2021, Data Neuron enables data prediction without the need for coding, and AI Suggestions enhance model performance. With active learning, the platform offers strategic annotation, which captures more information in a smaller quantity of data.

2.  Ship Rocket-

Ship Rocket founded in 2012 is a web-based and AI-powered automated e-commerce fulfillment service. Its platform uses a machine-learning-based data engine to suggest courier services for a company, choose print shipping labels for courier companies, and track orders all from a single interface. It helps businesses to trace their shipments and returns. Other features include order processing, a delivery rate calculator, shipment tracking, and other tools. It's compatible with OpenCart and Amazon's e-commerce systems. The company also offers insurance plans that are based on a subscription model.

3.  Linea Digitech-

Linea Digitech founded in 2018 is a rapidly expanding app development, software development, and digital marketing firm with locations in India and Saudi Arabia. They've been integrating technologies like Big Data, Chat Bots, and Artificial Intelligence to continually improve user experience on mobile applications. Linea Digitech's AI and machine learning services and solutions help you get a competitive advantage and deliver great results for your company. They provide AI-powered mobile apps, AI-specific solutions, and intelligent data in order to assist you in developing highly scalable and cost-effective digital goods and solutions while lowering labor and infrastructure expenses. 

They have the knowledge and skills to plan and implement artificial intelligence services that are integrated with cognitive technology to support your legacy business applications. They offer a wide range of services, including User Behaviour Analytics, Advanced Business Analytics, Big Data Analytics, Natural Language Processing, Deep Learning, and more.

4.  Jupiter AI labs-

The organization uses cutting-edge technologies to develop innovative solutions. Founded in 2019, AI Labs guarantees on-time delivery of products and total transparency throughout the product development process. They are a well-known software development company committed to excellence and quality, with a desire to provide the best solutions. With significant industry knowledge and a desire to meet the needs of the financial sector across the whole value chain for asset and wealth management, hedge funds, banks, and insurance. They provide outstanding AI-driven advice, responses, and support.

5.  Absolute-

Founded in the year 2016, Absolute Foods manages the whole agri-food supply chain, from seed to harvest. Its digital platform is an AI-driven operating system that may be used to grow crops without the need for synthetic enzymes or genetically modified organisms in vertical farms, greenhouses, and open farms. Absolute Foods' software, which works with over 8,000 farmers over 25,000 acres, can detect 63 crop varieties and their best harvesting settings. It provides agricultural hardware system control software. It takes data from IoT devices, sensors, hardware systems, and satellite sources on an hourly basis and feeds it into proprietary machine-learning algorithms to generate actionable insights. Agronomy services like soil and water testing are available to farmers. 

Absolute Foods has championed production, flavor, purity, and nutrition by growing the crops without the use of chemicals or pesticides. Phytochemistry, microbiology, and artificial intelligence are used to provide precision agricultural solutions. The company also uses AI to provide buyers with real-time traceability options.

6.  Quale Infotech-

Quale Infotech founded in 2017 is a prominent end-to-end consulting and sourcing firm specializing in Robotic Process Automation (RPA) and Artificial Intelligence (AI). Quale Infotech assures company continuity and zero downtime using their technological know-how, industry insights, expertise, and patented techniques. They provide Aiwozo, an Intelligent Process Automation platform. 

Its mission is to enable businesses to become AI-driven enterprises by assisting them in creating tailored user experiences that engage customers. Quale Infotech's approaches work by applying its external expertise and patented techniques to the internal processes of its customers. They've created frameworks and tweaked them throughout several engagements to account for the ever-changing environment.

7.  Credgenics-

Credgenics, founded in 2018, provides banks and lenders with AI and cloud-based debt recovery solutions. Collection strategy, analytics for profiling and collection, automated communication for customer interaction, and other functions are included. Alternative dispute resolution, insolvency and bankruptcy, fintech regulations, and other such services are deployed using AI. On a single platform, users can handle all collections and repayments. 

Uploading data to the platform, creating actions using an automated rule defining widget, issuing notices, and then approaching borrowers using any of the five modules (cloud-based calling, automated communication, field executive android app for on-field collection, legal notice, and litigation workflows) are all part of the services that Credgenics has enhanced using AI.


There are a lot of AI firms popping up right now, each with its own distinct brand and capabilities. In this blog, we looked at some of Delhi’s most innovative AI businesses that are revolutionizing the way industries use to operate. 

As the corporate environment develops, AI will surely play a big role in the development of every industry, and it is already doing so. India, particularly Delhi has tremendous chances to become a leader in the AI-based business area in the coming years because of the AI-driven startup culture developed in recent years. 

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This is a decorative image for: A Complete Guide To Customer Acquisition For Startups
October 18, 2022

A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

  • Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
  • Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

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This is a decorative image for: Constructing 3D objects through Deep Learning
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Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

  • Highly calibrated cameras that take a photograph of the image from various angles.
  • Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

  • Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

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This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts
October 18, 2022

A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

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This is a decorative image for: GAUDI: A Neural Architect for Immersive 3D Scene Generation
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GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

  • The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
  • Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
  • GAUDI also removes GANs pathologies like mode collapse and improved GAN.
  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

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