NineTwoThree AI Studio

We’re honored to be recognized as one of Clutch's Top 1000 Companies for 2024 🎉 This recognition is a testament to the hard work, passion, and dedication our team brings to every project. Here's to more in 2025!

Every F500 executive needs to know their data maturity if they want to benefit from AI. Use this guide to help: Level 0 - Physical files You’d be surprised, but a lot of organizations have warehouses full of physical files. We can do better. Level 1 - Unorganized (or barely organized) digital files By now, you’ve graduated to digital storage, in folders that might have informational labels, but probably don’t. But to really start to get insights, you’ll need more structure. Level 2 - Relational database Now we’re getting somewhere. Modern relational databases offer excellent ability to query and modify fields. But they weren’t designed with modern data science problems in mind. Level 3 - Data department To really take your data maturity to the next level, you need to invest in data pipelines and processes. A dedicated department with engineers can look at things like cleaning and normalizing data, building out an ETL layer, and more. Level 4 - Data analytics Now we’re talking. Data that DOES things. Visualizations, dashboards, basic statistics and analytics. Even basic machine learning like a linear regression or two? Level 5 - Data science Now we can start thinking about some advanced AI capabilities. We’re shifting the focus from data management to predictive and prescriptive analytics – enabling businesses to understand why things happened, and what is likely to happen in the future. Level 6 - Data-Driven Products The final boss. Using your data to build actual products that drive the business. The data is no longer the product of the team - the outcome is. You can scale this rich data out into an AI-powered product and take full advantage. Level 7 - Knowledge transfer If your data maturity is robust, you can apply it to different use cases, rather than one project. Few make it this far, but once you do, consider your project skyrocketed.

Perplexity is better than ChatGPT. This clip explains why. A big thanks to Brian Ondrako for having Andrew on the Just Get Started Podcast.

Netflix launched DVDs in January 2007. ~1,337 days later, Blockbuster filed for bankruptcy. "When will AI replace us?" This is the wrong question to ask right now. A better question is: "How are we preparing for this new reality?" The real power lies in adaptation and innovation, not fearmongering.

Want to see how #AI is revolutionizing businesses? 🚀 Our podcast host Michael Marchuk caught up with Andrew Amann, to talk about the power of custom AI solutions that align with a company's culture and values. Listen here: https://okt.to/BILfs2

How to keep up with AI trends without wasting your time

AI is redefining the way products are designed, tested, and launched. At each stage, from ideation to go-to-market, it offers unparalleled efficiency and precision. Swipe through this carousel to understand how AI empowers teams to prototype faster, identify flaws earlier, and launch with data-driven confidence.

I’m excited to announce that NineTwoThree has won the Fall 2024 Clutch Global Award as a top B2B company. This includes rewards for projects across:  - Generative AI    - Machine Learning - Chatbot  - Natural Language Processing  …and 12 other categories. Thank you to our clients for trusting us with projects setting the standard in emerging technologies. This award reflects all our team's hard work behind the scenes. Here’s to continued collaboration in 2025.

The AI backlash HarperCollins experienced this week is another example of what can happen when stakeholders feel AI is being used unethically.  Imagine if they were a publicly traded company. How would this have impacted the company’s value? Here’s my current approach to rolling out new products or features (with and without AI) while minimizing negative sentiment.  1) Research  - Attitudinal (How does our audience feel about AI?) - Behavioral (How do they currently use AI?) - Quantitative (Surveys) - Qualitative (Interviews)  Combining these four research methods can create the foundation for the rollout strategy and help participants have buy-in.  2) Positioning - What is currently on the market?  - Have our competitors done something similar?  - How will ours be different/unique?  This will ensure you don’t become just another “AI tool”. Differentiation can be baked into every part of the product.  3) Messaging  Once you have the research and positioning you can create messaging that would align with your personas and communicate the value.   4) Go-To-Market With a strong foundation, you can confidently launch your messaging across the mediums and channels that matter most to your personas with the highest probability of hitting your KPIs.  It’s deceivingly simple, but when implemented effectively, it can give all parties (customers, partners, employees) a sense of ownership over the new initiative.  Do you agree with this process? Or, do you think backlash is unavoidable with AI announcements?  New Source: The Verge

You can upload almost any document to ChatGPT and it will “read” it. Have you ever wondered how this works? It uses something called “Vector Embeddings” A technique that converts text into numerical representations. These representations capture the semantic meaning of the text, allowing AI models to understand and process information efficiently. To simplify, imagine text as images. When we see a picture of a cat, our brain instantly recognizes it as a cat. It doesn't analyze every pixel but identifies key features like whiskers, fur, and a tail. Vector embeddings work similarly. They break down the text into its core components and represent them as points in a multi-dimensional space. Words with similar meanings are placed closer together. So, when you upload a document, the AI model: Converts the text into numerical vectors. Compares these vectors to its vast knowledge base. Identifies relevant information and generates a response. This technique is driving innovation in fields from customer service to medical research. What valuable use cases could you see for this?