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What Are The Various Stages Of A Startup?

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One of the most fulfilling journeys anyone can undertake is to begin a startup.  There are a lot of stories about how a startup makes it big in the market and reading them can inspire oneself to undertake a similar journey.  However, beginning a startup and scaling it up is easier said than done as there are multiple stages to running a startup.  Identifying a problem and coming up with a solution is not the only thing which matters when it comes to founding a startup but there are multiple other parameters which need to be considered along the journey.  By looking at the multiple startups which succeeded and the big picture, a startup’s journey can be quantified into stages.  Skipping any of these stages and moving on to the next stage would surely be a setup for a failure.

Read along to find out the various stages in the journey of a startup.

1) Problem discovery

Anybody can come up with an idea but the most important thing is to come up with an idea which solves a particular problem.  This stage is about discovering bottlenecks and problems faced by customers in a market.  This is the stage where a startup needs to focus on what the customer wants rather than what a startup needs to do.  This is where startups need to interview customers to find out the problems they are facing and come up with a solution.  For example, Uber discovered that customers need a simple way to hail a cab and came up with their platform which connects cabs with customers.

2) Ideating

The next stage is to find a value proposition for customers.  This begins by ideating to find opportunities and create good solutions.  There are high chances for good ideas to come up in the discovery stage during the customer interviews as they might provide their own insights and ideas.  By the end of this stage, a startup should be able to come up with a solution which solves a problem by providing a solution which an existing competitor would not provide.

3) Problem/Solution fit

There is a high likelihood of the first solution not being the right solution.  The initial plans might not work out and therefore Plan A should never be assumed as the right solution.  Sometimes the immediate solution will not nudge a customer to make a purchase.  This stage exists to make multiple iterations and if possible pivots into different product models.  During this stage, a startup needs to introduce a product design, clickable prototypes, or product features which the customers can interact with physically.  The initial problem could be solved if customers show interest and prepay for the product or have taken a certain set of actions that you can define based on your product, target and market.  For instance, in the case of freemium models actionables could mean completing a long survey, joining a waitlist and referring X number of people or applying to become a user.

ALSO READ: What Is Seed Funding And What Are The Sources For Seed Funding For Startups

4) Product/market fit

In order to go for a product/market fit, a startup would need data like customer acquisition costs (CAC) and customer lifetime value.  This could only be done with a launched product which is in use.  One of the best indicators for a good product/market fit is acquiring customers at a lower acquisition cost.  A CAC can be calculated by dividing all the costs spent on acquiring more customers (marketing expenses) by the number of customers acquired in the period the money was spent.  For example, if a company spent INR 100 on marketing in a year and acquired 100 customers in the same year, their CAC is  INR 1.  Net Promoter Score (NPS) is one of the easiest ways to measure product/market fit.  Net Promoter Score is the percentage of customers rating their likelihood to recommend a company, a product, or a service to a friend or colleague on a scale of 1-10 with 10 being highly likely and 1 being highly unlikely.

5) Scaling up

This is the stage where a startup needs to focus on diversifying their product offerings.  This is where a startup needs to iterate what is working and put in processes which make these workflows faster.  This is the stage where a company could think of hiring more resources, opening a larger office space and expanding in different areas.  For example when the hyperlocal delivery startup Dunzo began, it was limited to Bengaluru.  However, Dunzo soon expanded to other metropolitan cities to expand their operations and scale up.  

Many startups and entrepreneurs focus on scaling  up rapidly without going through the proper startup lifecycle and often end up in losses.  Building a startup could be fun but it is important to pay attention to each of these steps throughout its journey.  

 

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Inverstors Stories

Fractal Invests $20 Million in Asper.ai to Accelerate AI Solutions for Consumer Goods

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Fractal Invests $20 Million in Asper.ai to Accelerate AI Solutions for Consumer Goods

Fractal, a leading SaaS unicorn, has announced a strategic investment of $20 million in Asper.ai, an AI-driven platform focused on the consumer goods and manufacturing sectors. This funding, revealed on March 19, 2025, aims to accelerate Asper’s growth by enhancing product development and expanding its enterprise customer base.

Investment Highlights

Pranay Agrawal, Co-Founder and CEO of Fractal, expressed excitement about the partnership, noting Asper’s impressive growth over the past three years. He stated that this investment will unlock new opportunities for enterprise customers and drive further innovation within Asper.

Asper.ai’s Objectives

Mohit Agarwal, Co-Founder and CEO of Asper.ai, emphasized the need for consumer goods leaders to have a strategic ally that can adapt to their operations and transform data into actionable insights. The investment will support Asper in building its autonomous growth AI platform and attracting top talent.

Future Plans

Anuj Kaushik, Co-Founder and Chief Commercial Officer of Asper.ai, highlighted the positive market response to their offerings. With Fractal’s investment, Asper.ai plans to enhance its AI capabilities across key areas like demand forecasting and revenue growth management.

Conclusion

Fractal’s $20 million investment marks a significant step in advancing AI solutions within the consumer goods sector. The collaboration between Fractal and Asper.ai is set to redefine how businesses leverage AI for growth and efficiency in a competitive landscape.

 

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Entrepreneur Stories

Bengaluru’s Hypergro.ai Raises Rs 7 Crore to Enhance AI-Powered Advertising Solutions

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StartupStories

Hypergro.ai, a Bengaluru-based marketing technology startup, has raised Rs 7 crore in seed funding led by Silverneedle Ventures, with participation from Huddle, TDV Partners, HME Ventures, Dholakia Ventures, FiiRE, and angel investors. Founded in 2022 by Rituraj Biswas, Neha Soman, Abhijeet Kumar, and Arijit Mukhopadhyay, the company aims to revolutionize digital marketing by addressing challenges like high Customer Acquisition Costs (CAC) and low Return on Ad Spend (ROAS).

 

The startup leverages AI to create hyper-personalized video ads using user-generated content (UGC). The fresh capital will be used to enhance Hypergro.ai’s AI capabilities, expand operations, and build a specialized team focusing on data analysis, predictive algorithms, and automation.

 

Since its inception, Hypergro.ai has collaborated with over 70 brands, including several from Shark Tank India. The company’s innovative approach has led to its selection for Google’s Startups Accelerator: AI First (India) program in July 2024, providing access to critical training, mentorship, and state-of-the-art AI tools.

 

Hypergro.ai’s platform now supports a community of over 300,000 creators across India and has partnered with more than 100 brands, significantly enhancing its AI model’s accuracy and improving revenue generation for clients. As it continues to expand and refine its AI-powered marketing solutions, Hypergro.ai is set to transform the digital advertising landscape, offering businesses more effective and efficient customer acquisition and engagement strategies.

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Entrepreneur Stories

Meta Faces Another Copyright Lawsuit Over AI Training Practices

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Meta Faces Another Copyright Lawsuit Over AI Training Practices

Meta, the parent company of Facebook and Instagram, is facing fresh legal challenges over allegations that it used copyrighted materials without permission to train its artificial intelligence models, including its LLaMA series. This lawsuit adds to the growing scrutiny of AI companies’ data sourcing methods.

The Allegations

Authors such as Sarah Silverman and Michael Chabon claim Meta trained its AI models on datasets containing their copyrighted works without authorization. Plaintiffs argue this constitutes copyright infringement, while Meta defends its actions under the “fair use” doctrine, asserting that the training process is transformative and legally permissible.

Internal Discussions Raise Concerns

Court documents reveal internal chats among Meta employees discussing the use of copyrighted materials. One researcher suggested acquiring books without permission, stating, “ask forgiveness, not for permission.” These discussions highlight potential awareness within Meta of the legal risks involved.

Fair Use Debate

Meta maintains that its use of copyrighted texts to train LLaMA models is transformative and falls under fair use. The company compares this practice to Google’s precedent in Authors Guild v. Google, where copying books for search tools was deemed fair use. However, critics argue that training AI for commercial purposes does not meet fair use criteria.

Broader Implications

This lawsuit reflects wider concerns about how AI developers source training data, often relying on publicly accessible yet potentially copyrighted materials. As litigation against companies like Meta, OpenAI, and Google increases, clearer regulations may be necessary to balance innovation with intellectual property rights.

The outcome of this case could significantly impact both AI development practices and copyright enforcement in the tech industry.

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