NineTwoThree Resources

Investing in Machine Learning with Confidence: Steps to Secure ROI

AI works, but only if you approach it the right way. Before starting your AI project, let's take a moment to learn from others' mistakes. From defining success metrics to building with the right tools, we've got the insider tips to help you avoid common pitfalls and set your AI strategy up for real success.

Author:

Pavel Kirillov

Seamlessly Integrating AI into Mobile Apps

This resource explores the seamless integration of Artificial Intelligence into mobile applications. We'll examine key benefits, explore strategic implementation approaches, and dissect real-world examples of successful AI-powered mobile experiences. We'll address the unique challenges and considerations that arise when deploying AI on mobile devices, paving the way for a deeper understanding of this rapidly evolving field.

Author:

Pavel Kirillov

Effective Guardrails for Your GenAI Apps

Generative AI is revolutionizing businesses, offering exciting opportunities, but it’s not without its risks. From chatbots creating fake policies to digital assistants suggesting questionable actions, the potential for misuse is real. Before looking into the world of GenAI, it’s essential to ensure proper guardrails are in place to protect your brand and users.

Author:

Pavel Kirillov

From Zero to Million-Dollar Product - Real-Life Case Study with DataFlik

Predicting home sales before they hit the market is a game-changer for real estate professionals. DataFlik, one of the country’s largest providers of wholesale residential real estate data, partnered with NineTwoThree to turn this vision into reality.

Author:

Pavel Kirillov

ROI of Data Science — Real-Life Case Study with Amerit

Every day, we hear about how AI and machine learning can "revolutionize industries," but rarely do we see real-world examples that break down the how. That’s why we’re sharing the story of how NineTwoThree helped Amerit Fleet Solutions transform their repair process with a data-driven ML solution—predicting errors with over 70% accuracy and saving time, money, and their reputation.

Author:

Pavel Kirillov

Key Elements for Timely Completion of Machine Learning Projects

Whether you’re looking into AI for the first time or looking to refine your approach, this guide is packed with easy-to-follow insights and advice. From project planning to tackling the unexpected, we’ll walk you through everything you need to know to keep things running smoothly. Ready to make your AI project a success? Let’s jump in!

Author:

Pavel Kirillov

How Data Maturity Can Skyrocket Your AI Projects to the Next Level

Imagine your AI projects powered by flawless data—because that’s where the magic happens! Whether you’re troubleshooting an existing AI product or just embarking on your data-driven journey, understanding the role of data maturity can be transformative. Explore the impact of elevating your data practices to create AI solutions that are smarter, faster, and more secure.

Author:

Pavel Kirillov

Self-hosted vs Third-party LLMs

The AI revolution is changing how many forward-thinking organizations operate. Enterprises that we work with have saved thousands of hours and millions of dollars by strategically integrating AI into their operations. By now, it’s clear the best-in-class companies of tomorrow will use AI as a core part of their business. Anyone who wants to jump on this trend would be wise to start exploring now.

Author:

Pavel Kirillov

A Framework For Presenting Complex Machine Learning Concepts for Non-Technical Stakeholders

Machine learning projects are as much about alignment as they are about algorithms. Whether you're updating stakeholders on progress, securing buy-in, or managing expectations, clear communication is key to success. But keeping everyone informed—especially across teams with varying technical expertise—can feel like an uphill battle. How do you simplify complex updates without oversimplifying? How do you keep stakeholders engaged and in sync with project goals? We’ve put together a practical framework to help you bridge the gap between technical details and stakeholder priorities.

Author:

Pavel Kirillov

Balancing Complex and Simple ML Solutions

The AI revolution has created a steady stream of flashy demos, incredible products, and life-changing announcements. Followed closely by sky-high expectations for what’s possible with AI.

Author:

Pavel Kirillov

Author:

Pavel Kirillov

NineTwoThree was selected by the CR Innovation Lab to help build an experimental chatbot that combines the power of AI with CR's expertise to answer your questions and offer product recommendations.

Tackling Hallucinations in LLMs with RAG

The rise of Large Language Models (LLMs) like ChatGPT has unlocked a new capability for technology-focused organizations. It’s not just about transforming existing processes like customer service and FAQs - LLMs are creating new use cases and features that weren’t possible before.

Author:

Pavel Kirillov

Helping Students Perform Self-Evaluations 80% Faster with Gamification

Custom Software Development

By collaborating with NineTwoThree Studio, PMI reduced their assessment time from three hours to just 40-60 minutes. The gamified approach dynamically adjusted question difficulty based on student performance, making the process more engaging and efficient.

Author:

Pavel Kirillov

Predict Qualified Leads from Previous Consumer Actions

There's thousands of organizations sitting on a gold mine of insights for their data. The tough part is finding them.

Author:

Pavel Kirillov

Predictive Lead Scoring Increases Insurance Company's Top Line

In the competitive world of insurance sales, efficiency is paramount. Cultivating a prospect from initial contact to a closed deal is a complex process, often riddled with wasted time and missed opportunities. This case study looks into how NineTwoThree partnered with a mid-sized insurance company to revolutionize their lead generation strategy using the power of Machine Learning (ML).

Author:

Pavel Kirillov

Building a Privacy-First LLM

In the evolving landscape of AI technology, the integration of Large-Language Models (LLMs) into organizational workflows have increased. For security-focused entities, the deployment of LLMs presents unique challenges, particularly concerning data privacy and reliability. This case study offers an in-depth exploration of the process involved in building and integrating Privacy-First LLMs. It sheds light on how organizations can effectively leverage AI while prioritizing security and compliance. By adopting privacy-focused systems, security-conscious entities can successfully navigate the complexities of AI integration while safeguarding the integrity and confidentiality of their data.

Author:

Pavel Kirillov

How Retrieval Augmented Generation Powers Enterprise Generative AI Projects

As with any groundbreaking technology, Generative AI has imperfections. One of the most prominent of these limitations is the challenge faced by LLMs in delivering accurate, sourced, and contextually appropriate content. This main limitation makes it almost impossible for a company to stake its reputation based on an LLM’s unknown output. How would you feel if a brand you trusted lied to you while you asked them questions about your account? This fear has led to the inception and growing relevance of retrieval-augmented generation (RAG). With RAG companies have more control over the materials an LLM uses for its information and the answers it gives users.

Author:

Pavel Kirillov

Integrating IoT Solutions for The Golf Course and Increasing Downloads by 304%

Internet of Things

Edge IoT

Author:

Pavel Kirillov

Realizing the depth of the challenges and recognizing the need to act swiftly, SWEE sought external help. They turned to NineTwoThree, a renowned app development company known for breathing life into struggling projects. With an impressive portfolio that included multiple successful Internet of Things (IoT) applications, NineTwoThree was the perfect ally for SWEE.

Why Semantic Search Will Change the Internet

While LLMs are trained on large amounts of data, they don’t have the ability to understand the context and intent of the words they are trained on. This is where semantic search enters the conversation. Semantic search has the ability to recognize the underlying meaning of search queries and return results based on the meaning, even if the search query doesn’t contain the exact keywords that are used in the results. Before we can dive into how semantic search makes LLMs better, we need to first look at semantic search itself.

Author:

Andrew Amann

Integrating With Dorel Baby Monitors & Selling Thousands at Target

When it came to developing their pioneering initial baby monitor application, Dorel Industries recognized the importance of finding a collaborator who shared their passion for technical pushing boundaries. That's why they turned to NineTwoThree Studio

iOS

Author:

Andrew Amann

How to Evaluate and Hire an App Agency

Ready to Build Your Dream App? Finding the right app agency can be the difference between a successful launch and a failed project. Our Flipbook, "How to Evaluate and Hire an App Agency," guides you through the process, from defining your app's goals to selecting the perfect partner. Learn key questions to ask, red flags to avoid, and best practices for a seamless collaboration. Let's turn your app idea into reality!

Author:

Andrew Amann

The Complete Guide to Mobile App Development Planning And Costs

When you're investing time, effort, and a substantial amount of money into the app development market, you're naturally looking for a worthwhile return. That's where a solid grasp of the cost structure comes into play.

Author:

Pavel Kirillov

ChartMogul: Scaling Users by Creating an Android App

NineTwoThree Studio and ChartMogul, a leading SaaS subscription analytics platform, developed and launched an Android app that expanded its user base.

Author:

Andrew Amann

Building a Saas From 0 to $100K ARR in 9 Months

Business Models

With Altar Live, we recognized an untapped potential in serving religious communities worldwide. Understanding the importance of faith and the growing need for digital solutions for congregations, we tailored our offerings to cater specifically to churches. Our unique approach and deep understanding of this niche market resonated profoundly, leading us to achieve an annual recurring revenue (ARR) of $100,000.

Author:

Andrew Amann

Predicting 74% of All Houses Sold In The United States Every Month

DataFlik approached NineTwoThree to build a machine-learning model that generates prioritized lists of motivated sellers more accurately than a human.

Author:

Pavel Kirillov

Using Machine Learning to Improve Maintenance Costs in Manufacturing

Discover how applying machine learning algorithms to hydraulic system components can improve overall maintenance based on this case study.

Edge IoT

Author:

Pavel Kirillov

Our Unique Process For Developing Dozens of Consumer Mobile Apps

Learn about the steps to develop a consumer mobile app, from researching the target market to designing and coding the app, branding, and revenue models.

iOS

Author:

Pavel Kirillov

Lessons From Building a Mental Health ChatBot

In this case study we discuss how our client decided to question if ChatGPT could be trained to be a replacement for a therapist.

CMS

Author:

Pavel Kirillov

Fast Development in High-Growth Software Startups

Custom Software Development

B2B Collaboration. Dock's software platform drives transparency and collaboration between businesses. NineTwoThree developed features to elevate that platform.

Author:

Ventsi Todorov

How to Develop a Successful B2B Application

Explore the process and methodologies for designing powerful B2B mobile and web apps that address unique complexities and solve business problems.

iOS

Author:

Ventsi Todorov