Select Page

How to build an AI-powered recommendation system?

build recommendation system
Listen to the article
What is Chainlink VRF

The internet has transformed the way we shop, with a vast selection of products available for purchase online. However, this convenience comes at a cost, with consumers having to sort through countless options, making it an overwhelming and tiring task.

On the other hand, the challenge for online stores is how to sell more goods at a higher price and faster than their competitors. One solution is to use a recommendation system that utilizes artificial intelligence (AI) to provide personalized recommendations to users. Such a system uses machine learning algorithms that analyze user data, such as search history, purchase behavior, and preferences, to predict what products a user is likely interested in.

For consumers, the benefits of personalized product recommendations are obvious. They save time and effort by offering tailored suggestions more likely to match consumers’ interests and preferences. They also help online sellers boost revenue and profit by giving their customers personalized recommendations encouraging them to purchase more products. Besides, they help build customer loyalty and trust by improving the overall shopping experience for consumers.

Hence, personalized product recommendation systems are valuable for online sellers and buyers alike.

Whether it’s e-commerce, media streaming, or any other sector offering content to users, recommending new material is crucial to the platform’s success. Even a small increase in revenue percentage can translate into millions of dollars in profit. In fact, McKinsey estimates that personalized products account for 35% of Amazon’s revenue and a staggering 75% of Netflix’s revenue, with the numbers only expected to grow.

Recommendation systems, also known as recommender systems, offer a compelling alternative to traditional search algorithms. They help users discover new products they might have missed, offer personalized recommendations based on their preferences, and ultimately make the shopping experience more efficient and enjoyable. These systems can significantly improve customer satisfaction and drive sales by automating the search process and saving customers’ time. It’s no wonder why every large platform today comes with a recommendation system.

Technically speaking, recommendation systems use machine learning algorithms to give users personalized recommendations for products, services, or information based on their behavior, preferences, and history. According to statistics, 80% of customers are likelier to buy from a brand that provides personalized experiences. Furthermore, businesses using a recommendation engine can see a 150% increase in click-through rates, leading to increased sales and revenue. Netflix is an excellent example of a company that has leveraged the power of recommendation engines to revolutionize streaming. By providing users with customized content, Netflix has achieved lower cancellation rates, saving the company around a billion dollars annually.

But how can you build an AI-powered recommendation system for your business? In this article, we will explore the key components of an AI-powered recommendation engine, the different types of recommendation algorithms, and how to build and implement a recommendation engine that can enhance user experience and boost your business’s performance. Let’s delve deeper and explore this technology.

What is an AI-powered recommendation system?

An AI-powered recommendation system is a machine learning algorithm that is trained to rank or rate products or users. It is designed to predict the ratings a user might give to a specific item and then return those predictions to the user in a ranked list. This technology is used by many popular companies such as Google, Amazon, and Netflix to increase user engagement with their platforms. For example, Spotify may recommend songs similar to ones you have previously listened to or liked to keep you using their platform to listen to music. Amazon may suggest products to users based on the data they have collected on that particular user.

Although recommender systems are incredibly useful, they are often seen as “black boxes” because the models created by these companies are not easily interpretable. Users may not understand why a certain recommendation is being made, but they often find the recommendations helpful and relevant to their needs and interests. The results generated are often for things the user needs or wants but may not have been aware of until they were recommended.

There are many ways to build a recommender system, and the approaches can vary from algorithmic and formulaic to modeling-centric. These approaches include page rank, collaborative filtering, content-based, and link prediction. However, it is important to note that complexity does not necessarily translate to good performance, and often simple solutions and implementations yield the strongest results. For example, large companies like Reddit, Hacker News, and Google have used simple formulaic implementations of recommendation engines to promote content on their platform.

Identifying what defines a good recommendation is a challenge that many companies still struggle with. The definition of “good” recommendations helps to evaluate the performance of the recommender system that has been built. The quality of a recommendation can be assessed through various tactics that measure coverage and accuracy. Accuracy is the fraction of correct recommendations out of the total possible recommendations, while coverage measures the fraction of objects in the search space that the system is able to provide recommendations. The evaluation method of a recommendation solely depends on the dataset and approach used to generate the recommendation.

Recommender systems share several conceptual similarities with the classification and regression modeling problem. In an ideal situation, companies would want to see how real users react to recommendations and track metrics around the user to improve their recommendations. However, this is often difficult to accomplish.

Types of AI-powered recommendation systems

There are several types of AI-powered recommendation systems, including:

Collaborative filtering systems

Collaborative filtering is a technique used in recommendation systems to predict the interests and preferences of a user based on the data and patterns from many users. The basic principle of collaborative filtering is that if two users have similar tastes in one product, they are also likely to have similar tastes in other products. There are two main types of collaborative filtering approaches: memory-based and model-based.

Memory-based approaches, also known as neighborhood collaborative filtering, use the ratings of user-item combinations to predict their preferences based on their neighborhoods. User-based collaborative filtering recommends products to a user based on the preferences of similar users, while item-based collaborative filtering recommends products based on the similarity between items calculated using user ratings of those items.

On the other hand, model-based approaches use predictive models that involve machine learning to parameterize the features associated with the dataset as inputs of the model. This helps to solve an optimization-related problem. Model-based approaches include decision trees, rule-based approaches, and latent factor models.

Collaborative filtering models are advantageous because they are easy to implement, provide high-level coverage, and capture subtle characteristics without requiring knowledge of the item content. Some examples of collaborative filtering algorithms include YouTube’s content recommendations based on users who have subscribed or watched similar videos and CourseEra’s course recommendations based on other individuals who have finished existing courses that a user has completed.

Collaborative-System

Collaborative-System

The figure shows the two different users and their interests, along with the similarity in their tastes. It is found that both Jack and Robert have similar tastes, so Jack’s interest is recommended to Robert and vice versa.

Content-based systems

A content-based recommendation system is a system that generates recommendations based on a user’s preferences and profile by matching them to items they have liked previously. Instead of leveraging ratings between the target user and other users, content-based models focus on the ratings the target user provides. These models establish the level of similarity between items based on the attributes of items liked by the user. To build a content-based system, you need a strong source of item-level data associated with the attributes of the item, such as price, published year, etc., along with some user feedback on the item, which can be either implicit or explicit. Content-based models are particularly advantageous when insufficient rating data is available, as they can leverage ratings and item attributes to generate recommendations. Examples of content-based systems include Amazon’s product feed, which recommends products similar to those the user has previously purchased, and Spotify’s music recommendations. Some companies like Hacker Rank and Reddit have also used algorithmic approaches to recommend new posts to users based on factors such as time of post, number of likes, dislikes, and comments, which can be factored into a formula to generate a score for a post and hence a recommendation.

Launch your project with LeewayHertz

Incorporate a powerful recommendation system into your customer-facing app for enhanced user engagement

Content-based

Content based system

The above image shows different models of the One Plus phone. If a person is looking for a One Plus 7 mobile phone, then One Plus 7T and One Plus 7T Pro are recommended to them.

Hybrid recommendation system

Hybrid recommendation systems are designed to overcome the limitations of individual recommendation systems by combining multiple data sources, which can be achieved through two different designs: parallel and sequential. In the parallel design, multiple recommendation systems are used in parallel to generate recommendations and their outputs are combined to produce a final result. A single recommendation engine is used in the sequential design, and its output is passed on to the next recommender in the sequence.

Hybrid systems offer several advantages, including increased robustness and personalization in user recommendations. By combining different models, hybrid systems can mitigate the weaknesses of individual models, leading to more accurate and diverse recommendations. Netflix is a well-known example of a company that uses a hybrid recommendation system, combining collaborative filtering (based on user behavior) with content-based filtering (based on item characteristics) to provide more relevant movie recommendations to its users.

Hybrid recommendation

Hybrid recommendation

Knowledge-based system

A knowledge-based system in a recommendation system generates recommendations based on the user’s needs and domain expertise. It involves defining rules that set the context for each recommendation, such as criteria for when a specific product or service would benefit the user. Unlike the content-based approach, these rules do not necessarily rely on a user’s interaction history but may include other expert information or attributes of customer products and services.

One advantage of a knowledge-based system is that the recommendations can be easily explained, making it easier for users to understand why a particular recommendation was made. However, building this type of framework can be expensive, and it is better suited for complex domains where items are infrequently purchased, and data may be lacking. One benefit of this approach is that it does not suffer from the same cold-start problems as other methods.

Knowledge-based

Benefits of using AI-powered recommendation systems

Enhancing customer experience through personalization

Personalization is essential to enhancing customer experience, and a recommendation engine is vital in achieving this goal. The Epsilon research report highlights that 80% of consumers prefer purchasing from a brand that provides personalized experiences, indicating personalization can be a significant competitive advantage for businesses seeking to improve customer loyalty and satisfaction.

In today’s world of information overload, customers are inundated with choices and information, making it challenging to capture their attention. Personalization can help businesses stand out by providing customers with relevant, tailored recommendations that meet their unique needs and preferences. This, in turn, can increase engagement, drive sales, and foster long-term customer loyalty. A recommendation engine can be an effective tool for delivering personalized customer experiences. By leveraging data about a customer’s past behaviors and preferences, a recommendation engine can suggest products or services most likely to appeal to that individual, leading to a more seamless and enjoyable customer journey where customers feel understood, valued, and appreciated.

Ensuring seamless customer experience across multiple channels

Omnichannel marketing has become increasingly popular recently, as customers expect seamless service across multiple channels. However, this presents a significant challenge for brands – ensuring consistency. According to Zendesk, 35% of customers expect to receive customer service through multiple channels.

An AI-powered recommendation engine can be a game-changer to provide a consistent experience across all channels, including social media, websites, and mobile apps. The recommendation engine can ensure that users receive a similar experience everywhere by collecting and utilizing data from customer interactions on different channels. For instance, if a customer searches for a product on your website, the recommendation engine can use that data to suggest personalized products through email or other channels, maintaining a consistent experience.

Deliver relevant content

In today’s digital age, delivering relevant content has become key to customer engagement. Brands that provide personalized content are more likely to attract and retain customers. Recommendation engines play a crucial role in helping businesses achieve this goal by showing customized and relevant content to their consumers.

One of the best examples of a brand delivering personalized content is Medium, an online publishing platform that allows users to select their preferred topics and, based on their reading behavior, creates customized content lists for them. This approach ensures that users only see content they are interested in, leading to higher engagement rates and increased user satisfaction.

Businesses can tailor their content to each user’s specific interests and preferences using a recommendation engine which can be done by analyzing user behavior and data such as search queries, past purchases and browsing history. With this information, the recommendation engine can provide personalized recommendations for content that the user is more likely to engage with.

Minimizing customer frustration in the customer experience

In today’s fast-paced world, customers have a limited attention span, and irrelevant content or product recommendations can quickly lead to frustration and a negative customer experience and loss of potential sales. An AI-powered recommendation engine can help minimize this frustration by filtering and displaying personalized and relevant content to customers.

By analyzing customer behavior and preferences, a recommendation engine can provide tailored recommendations that are more likely to engage and interest customers, improving the overall customer experience and can lead to increased sales and revenue for businesses. With the help of an AI-powered recommendation engine, businesses can avoid frustrating their customers and provide a more personalized and engaging experience.

Meet customer experience expectations

As customer expectations continue to rise, delivering personalized and seamless experiences has become more important than ever before. In fact, it has become a key determinant of brand loyalty. A report by Microsoft states that 96% of customers consider customer service and experience as critical factors influencing their loyalty to a brand.

To meet these expectations, businesses can leverage recommendation engines. By analyzing customer behavior and preferences, these engines can provide tailored product recommendations anticipating their needs and desires. This personalized approach helps build stronger customer relationships and drive loyalty over time.

In addition, recommendation engines can help to create a consistent experience across all touchpoints, from the website to social media and beyond. By providing personalized and relevant content, businesses can engage customers on their preferred channels and ensure a seamless experience throughout their journey.

Boost business performance using a recommendation engine

As a business owner, you want to achieve higher sales, increase the average order value and ultimately, boost revenue. One effective way to achieve this is by leveraging the power of a recommendation engine. By showing personalized product recommendations to your customers, you can increase the chances of a purchase. An AI-powered recommendation engine can also enhance your up-selling and cross-selling strategies, making the buying process smoother and more convenient for your customers.

By implementing a recommendation engine, you can boost business performance and revenue. With the help of an AI solutions provider, you can efficiently integrate a recommendation engine into your business operations and enjoy the benefits of increased sales, higher average order value, and better revenue growth.

How does an AI-powered recommendation system work?

A recommendation engine leverages a combination of machine learning technology and data to create personalized recommendations. Data serves as the foundation of a recommendation engine, providing the necessary information to extract patterns. The more extensive the data, the greater the engine’s ability to make profitable and relevant recommendations. Recommendation engines complete a standard four-step process:

Step 1: Data collection

To build a recommendation engine, the first step is to gather data. This can include both explicit data, such as ratings and comments provided by users, and implicit data, such as order history, return history, cart events, pageviews, click-through rates, and search logs. This data is collected for every user who visits the site.

Behavioral data is relatively easy to collect, as it involves logging user activities on the site without requiring extra input. However, analyzing this data can be challenging, as filtering out less relevant logs may be necessary. Since each user has their own unique preferences, their data sets will also be distinct. As more data is fed into the engine, it becomes smarter and better at making relevant recommendations. This is the same principle used by Amazon’s recommendation engine, which suggests products to users based on their browsing and purchase history, using features like “Frequently Bought Together” and “Recommended for You”.

Step 2: Storing the data

In a recommendation system, the quality of recommendations improves as the volume of data increases. Consequently, recommendation projects often grow into big data projects due to the sheer amount of data involved.

The type of data used for creating recommendations plays a significant role in determining the appropriate storage type. The choice may include a NoSQL database, a standard SQL database, or an object storage system. The selection is primarily based on factors such as the type of data captured, ease of implementation, storage capacity, integration with other components, and data portability.

When storing user ratings or comments, using a scalable and managed database is crucial to minimize the number of tasks and focus on improving recommendations. Cloud SQL is an ideal option as it caters to these requirements and simplifies data loading from Spark. Furthermore, with cloud SQL, you can choose to use PostgreSQL, MySQL, or SQL Server for your database, depending on your preference and requirements. This flexibility is especially beneficial for organizations with unique data storage needs.

Step 3: Analyzing the data

The data is filtered using various analysis methods to find items with similar user engagement data in a recommendation system. Depending on the requirements, different analysis methods can be used.

For instance, if immediate recommendations are to be provided to the user while viewing a product, a more agile analysis is needed. Real-time systems can process data as it is created and is used for giving in-the-moment recommendations. Tools that can process and analyze streams of events are usually involved in real-time systems.

On the other hand, batch analysis requires periodic processing of the data. This approach requires enough data to be created to make the analysis meaningful, such as daily sales volume. A batch system can be suitable for sending an email later.

Another approach is near-real-time analysis, which enables data to be gathered quickly so that the analytics can be refreshed every few minutes or seconds. A near-real-time system is well-suited for providing recommendations during the same browsing session.

Step 4: Filtering the data

In a recommendation system, filtering is an essential step that helps to retrieve the relevant data required to provide recommendations to the user. To do so, we must choose an algorithm, such as content-based, cluster-based or collaborative filtering, that best suits the recommendation engine.

Content-based filtering suggests products with similar characteristics to what a user likes or views, while cluster filtering suggests products that go well together, regardless of other users’ behavior. Collaborative filtering, on the other hand, makes predictions based on user tastes and assumes that two users who liked the same products in the past will like the same ones in the future.

To represent data about ratings or interactions, we can use matrices with products and users as dimensions. We can then use algorithms such as K-Nearest, Jaccard’s coefficient, Dijkstra’s algorithm, or cosine similarity to relate the datasets of people based on the ratings or products they interact with.

After filtering and using the algorithm, recommendations are given to the user based on the timeliness of the recommendation, whether it’s a real-time recommendation or an email to be sent later.

Launch your project with LeewayHertz

Incorporate a powerful recommendation system into your customer-facing app for enhanced user engagement

How to build a recommendation system? A case study in Python using the MovieLens dataset

Numerous datasets have been gathered and made accessible for research and benchmarking purposes with respect to recommendation systems. Below is a list of top-notch data sources to consider. For beginners, the MovieLens dataset curated by GroupLens Research is highly recommended. Specifically, the MovieLens 100k dataset is a dependable benchmark dataset with 100,000 ratings from 943 users for 1682 movies. Moreover, each user has rated at least 20 movies. This extensive dataset comprises various files that furnish details on the movies, users, and ratings provided by users for the movies they have viewed.

The ones that are of interest are the following:

  • u.item: the list of movies
  • u.data: the list of ratings given by users

Contained within the file “u.data,” are ratings presented in a tab-separated list that includes user ID, item ID, rating, and timestamp. The initial lines of the file are as follows:

movielens-head

As demonstrated previously, the file discloses a user’s rating of a specific film. This file holds a total of 100,000 such ratings and will be utilized to anticipate the ratings of movies that users are yet to see.

Building a recommender using Python

Python offers numerous libraries and toolkits with diverse algorithm implementations for creating recommenders. However, when it comes to understanding recommendation systems, exploring Surprise is highly recommended. Surprise is a Python SciKit that offers a variety of recommender algorithms and similarity metrics. Its purpose is to simplify the process of constructing and analyzing recommenders.

Here’s how to install it using pip:

$ pip install numpy $ pip install scikit-surprise

Here’s how to install it using conda:

$ conda install -c conda-forge scikit-surprise

You also need to install Pandas

$ python3 -m pip install requests pandas matplotlib

Before utilizing Surprise, it’s crucial to familiarize yourself with a few fundamental modules and classes that it offers:

  • The Dataset module is utilized for loading data from files, Pandas dataframes, or even built-in datasets accessible for experimentation. The built-in MovieLens 100k dataset is one such dataset within Surprise. To load a dataset, various methods are available, including:
Dataset.load_builtin()

Dataset.load_from_file()

Dataset.load_from_df()
  • The Reader class is utilized for parsing files that contain ratings. Its default format accepts data where each rating is stored on a separate line, with the order being user, item and rating. These order and separator settings can be customized using the parameters:line_format is a string that stores the order of the data with field names separated by a space, as in “item user rating”.sep is used to specify separators between fields, such as ‘,’.rating_scale is used to specify the rating scale. The default is (1, 5).skip_lines is used to indicate the number of lines to skip at the beginning of the file. The default is 0.

Below is a program that can be used for loading data from either a Pandas data frame or the built-in MovieLens 100k dataset:

# load_data.py

import pandas as pd
from surprise import Dataset
from surprise import Reader

# This is the same data that was plotted for similarity earlier
# with one new user "E" who has rated only movie 1
ratings_dict = {
"item": [1, 2, 1, 2, 1, 2, 1, 2, 1],
"user": ['A', 'A', 'B', 'B', 'C', 'C', 'D', 'D', 'E'],
"rating": [1, 2, 2, 4, 2.5, 4, 4.5, 5, 3],
}

df = pd.DataFrame(ratings_dict)
reader = Reader(rating_scale=(1, 5))

# Loads Pandas dataframe
data = Dataset.load_from_df(df[["user", "item", "rating"]], reader)
# Loads the builtin Movielens-100k data
movielens = Dataset.load_builtin('ml-100k')

In the program above, the data is stored in a dictionary, which is loaded into a Pandas dataframe and then further into a Dataset object from Surprise.

Selecting the algorithm for the recommender system

To select the appropriate algorithm for the recommender function, it is necessary to consider the technique being used. In the case of memory-based approaches mentioned earlier, the KNNWithMeans algorithm, which is closely related to the centered cosine similarity formula discussed above, is an ideal choice.

The function must be configured to determine similarity by passing a dictionary containing the necessary keys as an argument to the recommender function. These keys include:

  • “name”: This key specifies the similarity metric to be utilized. Available options are cosine, msd, pearson, or pearson_baseline. The default is msd.
  • “user_based”: A boolean that indicates whether the approach will be user-based or item-based. It is set to True by default, meaning the user-based approach will be used.
  • “min_support”: This key specifies the minimum number of common items necessary between users to consider them for similarity. For the item-based approach, it corresponds to the minimum number of common users between two items.

The following program configures the KNNWithMeans function:

# recommender.py

from surprise import KNNWithMeans

# To use item-based cosine similarity
sim_options = {
"name": "cosine",
"user_based": False, # Compute similarities between items
}
algo = KNNWithMeans(sim_options=sim_options)

The above program configures the recommender function to use cosine similarity and to find similar items using the item-based approach.

To use this recommender, you need to create a Trainset from the data. Trainset is built using the same data but contains more information, such as the number of users and items (n_users, n_items) used by the algorithm. You can create Trainset either by using the entire data or a subset of it. You can also split the data into folds, where some of the data will be used for training and some for testing.

Here’s an example to find out how the user E would rate movie 2:

from load_data import data
from recommender import algo

trainingSet = data.build_full_trainset()

algo.fit(trainingSet)
Computing the cosine similarity matrix...
Done computing similarity matrix.
<surprise.prediction_algorithms.knns.KNNWithMeans object at 0x7f04fec56898>

prediction = algo.predict('E', 2)
prediction.est
4.15

According to the algorithm’s prediction, user E would rate the movie 4.15, which may be considered high enough to make a recommendation for it.

LeewayHertz’s expertise in recommendation system development

Content recommendation

Utilizing the power of Artificial Intelligence (AI) and machine learning techniques, the system analyzes users’ online activity, preferences, and behaviors to tailor content suggestions. This ensures a personalized user experience, with the system intelligently recommending content that aligns closely with each individual’s specific interests and browsing history.

Content-based filtering

LeewayHertz develops recommendation systems based on a refined content-based filtering, designed to enhance user experience. This sophisticated system curates recommendations by focusing on the attributes and metadata of previously liked or consumed items. In essence, if a user showed a preference for a certain type of content, the system will recommend similar items, ensuring relevance and personalization. This mechanism is driven by a deep understanding of item characteristics, creating an intuitive and engaging user experience.

Product recommendations

LeewayHertz builds sophisticated product recommendations systems, specifically designed to enrich the shopping experience on e-commerce platforms. Using their advanced recommendation engine software, they present customers with suggestions tailored to their individual interests. These recommendations are formulated from insights gathered from customers’ past purchases, search queries, and browsing behaviors. This intuitive approach helps ensure customers discover products they are likely to be interested in, resulting in a more personalized and satisfying online shopping experience.

Collaborative filtering

LeewayHertz also builds recommendation systems with a sophisticated collaborative filtering, aimed at making user experiences more personalized and satisfying. This service recommends products or items by analyzing the preferences of users who have similar interests or behaviors. By leveraging the power of group similarities, the system can enhance the relevance and accuracy of its recommendations, ensuring that users discover items that truly align with their tastes and preferences. This process creates a more intuitive and engaging user experience, based on shared user behaviors.

Visual search

This innovative recommendation system based on visual search is particularly beneficial for sectors such as e-commerce and fashion. This system utilizes image recognition technology, allowing users to upload an image and receive product or item recommendations based on that visual input. The system intelligently matches the uploaded image with similar products or items in its database, offering a unique, user-friendly, and highly personalized approach to product discovery. The focus on visual search recommendations is reflective of LeewayHertz’s commitment to offering dynamic and innovative solutions.

Hybrid recommendation systems

LeewayHertz develops robust hybrid recommendation systems, a solution designed to enhance the precision and relevance of user recommendations. This service stands out by combining several recommendation algorithms, leveraging their strengths to offer more accurate suggestions. An additional advantage of the hybrid recommendation systems is their flexibility. It is designed to seamlessly integrate with both existing systems and those newly implemented, making it a versatile solution for diverse user engagement needs.

Endnote

In today’s constantly changing business landscape, companies face fierce competition, which makes it essential for them to do everything in their stride to stay ahead of the curve. Recommendation systems are one way to gain an edge and achieve larger business goals like increasing sales, advertising revenues, or user engagement. However, success with recommendation systems requires careful consideration of their necessity and agility.

A good recommendation system will provide value to the business and its users without wasting valuable resources. Ongoing maintenance may be necessary in some cases; hence, businesses should weigh the benefits against the costs before making a decision. The ultimate goal should be a recommendation system that drives real business value.

Agility is equally important when it comes to recommendation systems. As users change their behaviors, preferences, and needs, recommendation systems must adapt to stay relevant and effective. An agile recommendation system will evolve over time, considering what works, what doesn’t, and what additional data sources might help improve recommendations.

In conclusion, the success of recommendation systems lies in their necessity and agility. Businesses that prioritize these qualities when building and deploying recommendation systems will be better positioned to achieve their larger goals and stay competitive in an ever-changing marketplace. By continuously evaluating and improving recommendation systems, businesses can offer users the best possible experience while achieving their own objectives.

Ready to take customer experience to the next level? Connect with LeewayHertz’s team of AI experts, who will show you how an AI-powered recommendation system will help!

Listen to the article
What is Chainlink VRF

Author’s Bio

 

Akash Takyar

Akash Takyar
CEO LeewayHertz
Akash Takyar is the founder and CEO at LeewayHertz. The experience of building over 100+ platforms for startups and enterprises allows Akash to rapidly architect and design solutions that are scalable and beautiful.
Akash's ability to build enterprise-grade technology solutions has attracted over 30 Fortune 500 companies, including Siemens, 3M, P&G and Hershey’s.
Akash is an early adopter of new technology, a passionate technology enthusiast, and an investor in AI and IoT startups.

Start a conversation by filling the form

Once you let us know your requirement, our technical expert will schedule a call and discuss your idea in detail post sign of an NDA.
All information will be kept confidential.

Insights

Follow Us