Cracking the Code: How to Achieve Product-Market Fit for Your AI SaaS

May 27, 2024

Achieving product-market fit is a crucial milestone for any AI SaaS (Software as a Service) company. It signifies that your product is effectively meeting the needs of your target market.  We recently hosted a webinar with Mr. Thiyagarajan Maruthavanan on 16th May, to delve into this crucial concept. 

In this blog is a breakdown of the key takeaways for achieving that spot where your AI solution resonates with the perfect audience.

Product Market fit has several definitions, it is a nebulous concept. He described it as a feeling that feels like going down a mountain. If you’re going in the wrong direction, it will be as though you are pushing a rock up the mountain but for those who have experienced product market fit, it will feel like rolling the rock down the hill. One unit of effort leads to ten units of progress. When you’re in the wrong direction, it will feel as though ten units of effort lead to one unit of progress

So what is Product-Market Fit?

Simply put, it's when your product perfectly clicks with a specific audience. It solves their problems, makes their lives easier, and ultimately, leaves them feeling satisfied.

Think of it as finding that sweet spot where your product resonates with the right people. They become not just users, but fans. 

Product-market fit is the point at which your product satisfies the needs of your target market better than any alternatives. It's about creating a product that customers love and are willing to pay for. For AI SaaS products, this means offering solutions that solve specific, high-value problems using artificial intelligence and machine learning technologies.

The process of achieving and maintaining product market fit is not just to think about reaching it, but also to stay updated and keep track of what Rajan calls ‘Technology Earthquakes’, which in today’s date and time is GenAI.

The process to PMF

To reach your product market fit, he gave us three steps.

The Crawl

In his insightful webinar, Mr. Thigarajan Maruthavanan emphasized that the journey to achieving product-market fit begins with the crucial "Crawl" phase. This foundational step is often misunderstood, with many believing that immediate execution is the key. However, true success lies in meticulous preparation and ideation. Here's a deeper dive into the "Crawl" phase:

1. Validate Your Idea

The initial focus should be on whether you’re working on the right idea. This step can be particularly challenging, especially for engineers who might find it easier to conceptualize within their comfort zone of technical stacks. As new applications, especially in the field of Generative AI, continue to emerge across various domains, identifying the right idea becomes crucial.

2. Build a Well-Rounded Team

A startup's success is heavily dependent on its team. Mr. Maruthavanan suggests forming a balanced team that includes a founder (ideally with a background in product management or sales), 2-3 AI engineers to handle the technical aspects, a designer to ensure the product's visual and functional appeal, and a product marketing specialist to drive the market strategy. This diverse mix of expertise ensures that every critical area of your startup is covered.

3. Focus on User Experience

Rather than rushing to create a full-fledged product, the focus should be on developing a Minimum Viable Product (MVP). An MVP allows you to test core functionalities with real users and gather invaluable feedback early on. The key here is simplicity coupled with exceptional user experience (UX). A well-designed, straightforward tool can provide more insights and user engagement than a complex, feature-rich product that overwhelms users.

In the crawl phase, navigating the idea maze is essential. This means being flexible and open to evolving your concept as you gather feedback and insights. With the right idea, a strong team, and a focus on user experience, your startup can effectively lay the groundwork for future success.

By taking these steps seriously during the crawl phase, you set a solid foundation for your startup, ensuring that when it's time to walk and eventually run, you are well-prepared and poised for success.

The Walk Phase

As your startup progresses from the "Crawl" phase, the next critical step is the "Walk" phase. Mr. Thigarajan Maruthavanan emphasized that this stage is all about refining and scaling your product to meet market demands. Here’s a closer look at what this phase entails:

1. From Pilot to Production

During this phase, the focus shifts to moving your product from pilot testing to full-scale production. It’s essential to choose a use case that not only demonstrates the capabilities of your product but also ensures that the results are accurate and reliable. Consistency in your product’s performance builds user trust and paves the way for broader adoption.

2. Key Focus Areas: Accuracy, Reliability, Low Latency, and Delightful UX

In the "Walk" phase, it’s crucial to maintain a strong emphasis on accuracy, reliability, and user experience. These elements are foundational to building a product that users can depend on and enjoy using. Accuracy ensures that your product delivers the expected results, reliability guarantees that it works consistently, minimum delays or lag in response times will ensure a seamless and smooth user experience. Users should feel that the product is responsive and efficient, which can significantly impact their overall satisfaction, and a delightful UX keeps users engaged and satisfied.

3. GenAI: A New Perspective on AI

Generative AI (GenAI) represents a new era in artificial intelligence, offering a different approach compared to traditional AI. Mr. Maruthavanan highlighted that GenAI can be viewed as an API rather than a complex system that requires a dedicated AI team. As an application builder, you can leverage GenAI APIs to integrate advanced AI functionalities without the need to build and maintain a specialized AI infrastructure.

4. Leverage Your Own Data

To gain a competitive edge, it’s important to build systems that can collect and utilize your own data. This proprietary data can provide insights and advantages that set your product apart in the market. Data-driven decision-making and continuous improvement based on user feedback and usage patterns are key to success.

5. Regulation and Responsible AI

As AI technology evolves, so do the regulatory frameworks governing its use. It’s crucial to stay informed about relevant regulations and ensure that your product adheres to these guidelines. Additionally, practicing responsible AI involves ethical considerations, transparency, and accountability in how your AI systems are developed and deployed.

By focusing on these critical aspects during the "Walk" phase, you can effectively transition your product from pilot testing to full-scale production, ensuring that it meets the needs of your users while maintaining high standards of performance, reliability, and ethical responsibility.

The Run

The final phase in the journey to product-market fit is the "Run" phase. This stage is all about scaling your product and expanding your user base. Mr. Thigarajan Maruthavanan outlined several key strategies to help startups achieve success at this stage:

1. Know Your Audience

Understanding your audience is critical for sustainable growth. Analyze user groups, or cohorts, to identify who remains engaged with your product and why. This analysis helps you pinpoint your most valuable customers and avoid attracting "tourists"—users who are unlikely to stick around and contribute to long-term success. By focusing on the right audience, you can reduce churn and build a loyal user base.

2. Retention is King

User retention should be at the heart of your product roadmap. Develop and prioritize features that keep users engaged and coming back for more. Retention strategies might include enhancing user experience, adding new functionalities based on user feedback, and creating value-added services that deepen user engagement.

3. Packaging with Impact

Positioning and messaging are crucial, especially for an AI product. Tailor your communication to highlight the unique value proposition of your AI solution. Clearly articulate how your product solves specific problems and why it stands out from competitors. Effective packaging can significantly influence user perception and adoption.

4. Building Trust

Trust is a key factor in convincing potential buyers of your product's value. Be transparent about how your AI product works and address any concerns users might have about AI implementation. Building trust involves providing clear information, demonstrating reliability, and showcasing successful use cases. Transparency and honesty can help alleviate fears and build confidence in your product.

5. The Power of Influence

Influence plays a major role in product adoption. Identify AI influencers or become a thought leader in your industry. Thought leaders can significantly boost your product's visibility and credibility. Engaging with influencers can help amplify your message and reach a broader audience, driving adoption and trust.

6. Embrace the Pivot

Flexibility is essential in the fast-paced world of AI. There may be times when you need to adjust your strategy—this is known as pivoting. Pivoting is not a sign of failure but a necessary course correction to stay aligned with market demands and user needs. Maintain a growth mindset, be prepared to adapt, and view pivots as opportunities for improvement.

7. Hustle and Hack

Success in AI requires hard work, resourcefulness, and a willingness to experiment. Embrace the hustle—work diligently to overcome challenges and explore new opportunities. Be innovative and willing to "hack" solutions that drive progress. This combination of effort and adaptability is crucial for thriving in the competitive AI landscape.

By implementing these strategies during the "Run" phase, you can effectively scale your product and expand your user base, ensuring long-term success and growth in the dynamic world of AI.

Latest Blogs
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.

Reference Links

This is a decorative image for: Constructing 3D objects through Deep Learning
October 18, 2022

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

Reference Links

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.

Reference Links

This is a decorative image for: GAUDI: A Neural Architect for Immersive 3D Scene Generation
October 13, 2022

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.

Reference Links

Build on the most powerful infrastructure cloud

A vector illustration of a tech city using latest cloud technologies & infrastructure