The gains come from generative AI supercharging developers in 4 key areas:
Leading AI coding assistants like GitHub Copilot, TabNine, and Codex allow developers to generate code snippets and entire functions through conversational prompts, drastically accelerating rote programming work. Developers retain oversight to evaluate quality and customize outputs. While focused on Python currently, experts predict advances across languages and platforms. Though optimal use cases differ. Java and C# projects have seen 10-30% shorter timelines leveraging automation for routine changes. Accelerated coding paves the way for faster release cycles, reduces costs, and frees up resources to focus on innovation. But responsible implementation is key amid rising adoption. Organizations must mitigate risks around data privacy, security vulnerabilities, and reputational impacts through governance policies and controls. Upskilling developers on generative AI best practices also improves experience, and retention while maximizing productivity gains. The future is bright for symbiotic human and AI collaboration in software engineering. With disciplined adoption, generative AI unlocks speed, cost savings, and creativity for transformative gains.
So far, we have analyzed how the IT industry is leveraging AI to its advantage. But can we assure that everything described above is true? At Huenei, we incorporated the use of AI tools very early on. Given the promising landscape they offer and the technological revolution they entail, we could not refrain ourselves and had to give it a try. The incorporation of AI into our processes has helped streamline our and our client’s productivity. Through the use of Copilot, the autocomplete tool created by GitHub in partnership with OpenAI, we have managed to make code-writing tasks more efficient. Based on previously generated code, Copilot can autocomplete code lines or blocks. The decision to incorporate it was based on the good metrics achieved, with 40% of its Python suggestions being accepted by developers. It is important to keep in mind that developer intervention will always be necessary to avoid risks due to errors. AI has also assisted us in the process of executing unit tests, saving time and resources. Machine learning algorithms can analyze code and automatically generate test cases quickly, identifying possible scenarios and generating relevant data, reducing manual workload and accelerating the process. We have achieved optimization of unit testing by identifying areas of code prone to errors, allowing us to focus our efforts on critical flows. Similarly, code analysis provides us with recommendations on areas to expand testing coverage. By gathering and preparing test data, we have implemented a model that aligns with existing processes. The constant training and monitoring help guarantee risk mitigation. The results have been excellent. Leveraging intelligence represented an exciting opportunity to enhance the efficiency and quality of software development through automation, increasing the reliability of outcomes and reducing costs of the end product.
If you think that building a product only after you have started selling it to people is a mad idea, we are here to show you great minimum viable product examples that prove the opposite.
MVPs can be any ideas or products that feature only a limited set of functions or capabilities that are still enough to prove your concept in a determined market.
Whether you are working on app development or a vegan dog-treats business, building an MVP may save you time and money on the way to commercializing a finished product, and the definition doesn’t stop there, since you can also ask yourself about minimum viable channels, segments, services or promotion.
Facebook, Dropbox, and Zappos have all in common that they started as minimum viable products, proving that investing tons of money is not always a requirement for launching a big business, but the ability to listen to your market and carefully cater to them according to their feedback of your ideas.
In this article, we will show you what the different types of MVPs are and give you examples so you can get inspired and easily venture out into the wild world of product and service development. We will also explore some minimum viable product examples.
In the world of startups, it is common to see state-of-the-art tech that no one really knows what to use for. This probably happens because creators often focus on bringing finished products to the market without first considering if consumers really want them, and here’s where MVPs play a vital role in redefining business models.
Think about the overhyped Google Glasses that were about to be released in May 2014 for $1,500. The company focused so much on product features such as using a VR platform via voice commands (which sounds really nice) that they forgot people didn’t want to wear glasses in the first place.
There are two classifications of MVP: low fidelity MVPs serve for better understanding your consumer’s needs and see if your solutions are worth enough for solving their problems, while high fidelity MVPs focus more on how much would they pay for your product and getting early adopters that can later help you redefine your value proposition as you listen to them.
Choosing between high fidelity or low fidelity MVPs depends on how much time you have and how much are you willing to spend on this stage of your product development.
A landing page is a website designed to motivate visitors to carry out a specific task (give you their email, see your products or buy them) once they have clicked on a marketing communication such as an Instagram ad. This is a great way to show them what you have and prove if your communications are going the right way.
Buffer, an app designed for scheduling social media posts, is an extraordinary example of this. Their MVP was a landing page that explained the platform’s capabilities and encouraged people to sign up. However, by that time the app actually didn’t exist at all so customers were shown a message saying the service wasn’t ready and that they would be receiving updates.
Once the creators had a database of enough possible users, they started asking them if they would be willing to pay for the service. What they did is testing that hypothesis by adding prices to the landing page. This allowed them to see how many visitors would actually turn into paying customers.
Short videos are one of the most popular MVPs out there. They are zero-risk, cheap to elaborate, and effective for communicating complex ideas surrounding your product and services. They are so versatile you can post them on MVP platforms such as GoFundMe, show them to investors and even people on your way.
You would be amazed to know that Dropbox, which has a market cap of 11.9 billion, started as a 2-minute MVP that explained with paper figures how the cloud service worked.
Ad campaigns allow you to test if you are targeting the right audiences. With platforms such as Google and Facebook ads, you can even measure what are the features of your products that people appreciate.
Using CGI imagery on your ads is a creative way of testing your product’s appeal. You can do this for a fraction of the real cost of manufacturing a real product by hiring a designer at a platform such as upwork.com.
If people actually try to buy the product once they have reached your website through your social media ads, you can tell them the product is out of stock, and even give them a coupon, a gift card or a discount code they can later use when the product is available. This is great for proving if the market wants the product before you go to the manufacturing process.
If you have already passed the discovery phase of your product, crowdfunding is by excellence the best way to promote your projects. With just a simple explainer video you can easily test the market while you raise money and get early adopters.
A great example of an MVP that started as a crowdfunding project is the board game Kingdom Death Monster, which raised $12.4 million from more than 19,000 people back in 2016. Back then, they used clear images and a great explainer video before they had started production.
This MVP consists of creating an illusion of a product, which translates into people thinking they are experiencing the real thing while you are actually using a human resource behind the
curtains. The Wizard of Oz is adequate for analyzing the demand of a product while you keep the operational costs low.
A noteworthy example of The Wizard of Oz is Zappos, a shoe company that was acquired by Amazon in 2009 for $1.2 billion. This business started with its founder Nick Swinmurn posting pictures online of shoes that he didn’t have in stock but that were for sale in stores nearby his home. Once customers bought him a pair of shoes through his simple website he would manually process the order, buy the shoes and send them.
In Huenei we have a specialized team of engineers that build MVPs for your tailored software projects. Our methodological approach starts with a need analysis and then we design a solution proposal for proving concepts no matter the resources.
Predictive analytics powered by artificial intelligence have immense potential to revolutionize healthcare and other industries.
By analyzing vast amounts of patient data, AI algorithms can identify individuals at risk for certain diseases and predict which treatments will be most effective for each patient. In this article, we explore how AI-enabled predictive analytics tools can help healthcare organizations achieve key objectives.
One major healthcare goal is detecting diseases at the earliest stages when they are most treatable. AI predictive analytics support this mission by pinpointing patients likely to develop illnesses based on risk factors in their data. Doctors can then take preventative action with lifestyle changes or early interventions before diseases progress, improving outcomes.
Healthcare aims to enhance patient outcomes. AI predictive analytics support this by forecasting how patients will likely respond to different treatments. Doctors can then customize treatment plans to each patient’s predicted needs, boosting the chances of successful therapies.
Lowering healthcare expenses is a constant pursuit. AI predictive analytics curb costs by reducing ineffective therapies. Algorithms analyze patient data to determine optimal treatments, avoiding expensive trial-and-error approaches.
Precision medicine is rising, with treatments tailored to individuals. AI predictive analytics are key, assessing genetics, lifestyles, and health histories to create personalized plans. This leads to more targeted, effective care.
AI predictive analytics also identify health trends across populations by processing large datasets. Providers can then develop focused interventions to boost community-wide outcomes.
At Huenei, we specialize in ethical, privacy-focused AI development including predictive analytics. Our solutions enable organizations to leverage AI while protecting patient data through strong security policies. Contact us today to explore how our AI expertise can help your healthcare organization pursue vital goals.
How can you make a Chat GPT integration with OpenAI models into a software development successfully?
Technology advances by leaps and bounds and provides us with more solutions and possibilities to explore in the world of development, which can take us to unimagined places. The need to be constantly at the forefront of this range of possibilities, leads us to be in training and learning 24/7, which allows us to incorporate new expertise to, for example, integrate OpenAI models within projects with cutting-edge technology, such as a Chat GPT integration.
In this blog post we want to share with you how together with one of our large clients we have managed to implement a concrete business case where we made a Chat GPT integration into a custom software solution.
The objective of the application is to provide a dynamic and flexible training platform for the sales force of a renowned pharmaceutical laboratory, with the ability to obtain online information without the need to perform previous data uploads, saving costs and time.
The software solution, beyond including standard user, group and profile administration functionalities, contains modules related to training management: roles, suggested exams per role, exam form and results tracking per exam, per role and per group.
The important innovation we achieved is the integration with Chat GPT combining two of its main functionalities: Information Search and Text Analysis.
After a series of concept tests carried out by our team of Prompt Engineers together with business specialists on the client’s side to refine the parameters that allow us to obtain information in an accurate, reliable and fair way in terms of the amount of bytes sent and received to optimize costs, we concluded the following:
The sum of your scores will give you a final result that is recorded and will be part of your training record through integration with your LMS (Learn Management System).
The results are amazing with a tremendous positive impact for the client in terms of cost and time due to the high degree of automation of the process for training your sales force.
Organizations are constantly seeking ways to boost productivity, streamline processes, and improve customer experience; Generative AI is helping them achieve that thanks to OpenAI benefits.
Generative AI can be particularly useful in business software applications in several ways. Let’s go through some OpenAI benefits:
Generative AI can help businesses streamline operations, reduce costs, and make better decisions by leveraging the power of data analysis and machine learning. However, businesses must exercise quality control over the generated content to ensure its accuracy and consistency.
In conclusion, Generative AI is proving to be a game-changer for businesses looking to improve their operations and customer experience. OpenAI benefits organizations by providing them with advanced data analysis, personalization, training, predictive maintenance, and regionalization capabilities. By leveraging these benefits, businesses can increase productivity, reduce costs, and make better decisions. However, it is crucial for businesses to ensure the accuracy and consistency of the results through quality control measures. With OpenAI model-powered development services, businesses can create customized models that deliver real results and take their operations to new heights.
As a provider of OpenAI model-powered development services we can help you create custom models that deliver real results and take your business to new heights.
