Azumo AI Capabilities Overview

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‍Azumo AI Capabilities: Presentation Discussion‍

This comprehensive discussion accompanies our AI capabilities presentation. Whether you’ve just watched the presentation or want to dive deeper into the concepts, this narrative provides the context, insights, and detailed explanations that bring our AI expertise to life. Think of this as the conversation you’d have with our team after the presentation – where we unpack the technology, share real stories, and explore what AI transformation really means for your business.

Let me take you on a journey through the AI revolution and show you how Azumo has positioned itself at the forefront of this transformation. What we’re witnessing isn’t just another technology trend – this is a fundamental shift in how businesses operate, compete, and deliver value to their customers.

When we founded Azumo back in 2016, we had a vision that might have seemed ambitious at the time. We believed that the confluence of mobile, data, and cloud technologies would drive all companies to build intelligent applications. Fast forward to today, and that vision has become reality in ways that even surprised us. As Saif Ahmed from Omnicom told us recently, behind every huge business win is a technology win, and he specifically called out how our teams, stacks, and packages came together to achieve low-latency and real-time GenAI on their 24/7 platform.

Let me step back and explain why this moment in history is so significant.

The Perfect Storm: Why AI is Exploding Now‍

Artificial intelligence seems to be everywhere right now, but this isn’t sudden. It’s the result of multiple technological advances hitting critical mass. Several trends have converged to create this moment.

First, the volume of data has exploded. We’re not just talking about text – images, video, audio, sensor data, and more are now being processed and used to train AI systems.

Second, cloud infrastructure has made it possible to store and process this data at massive scale. You can’t run these models on traditional setups – the cloud made supercomputing accessible.

Third, GPU computing power has increased by six orders of magnitude. That’s a massive leap forward – not an incremental change. It’s what made training today’s large models feasible.

And finally, the transformer breakthrough in 2017, via Google’s “Attention is All You Need” paper, transformed how natural language processing works and paved the way for today’s large language models.

Now we’re seeing what’s effectively a Cambrian explosion in model development. Dozens of models are being released, each one more capable than the last.‍

Understanding the Technology: How LLMs Actually Work‍

Large language models (LLMs) have three main components, and understanding them helps you make smarter decisions.

  1. Embeddings: This step converts your input into numerical representations. Whether it’s text, images, or audio – everything becomes vectors. Domain-specific training is critical here.
  2. Transformer Architecture: This is the core of the model. Unlike older sequential models, transformers look at all input simultaneously. But there’s a catch – context windows are limited, so what you feed into the model matters.
  3. Unembedding Matrix: This step translates the processed vectors back into readable output. It also controls randomness, creativity, and filtering for quality and safety.

Choosing the Right Model: It’s Not One-Size-Fits-All‍

There is no universal best model. It depends on your use case. Here are the major considerations:

  • Start with your goals. What are you trying to accomplish?
  • Accessibility: Open-source models like LLaMA give you control. Proprietary models like GPT-4 offer performance but come with limits.
  • Evaluate the sponsor: Larger backers like OpenAI, Google, and Anthropic ensure support and stability.
  • Practicality: Don’t overdo it. A 70B parameter model may be overkill and costly.
  • Ethics: Know what your model was trained on. Provenance matters.

Real-World Impact: Where AI is Making a Difference

AI is delivering results across industries:

  • Retail & E-commerce: Visual search, dynamic pricing, recommendation engines, and automated product descriptions.
  • Healthcare: AI-assisted imaging, risk prediction, note processing, and drug discovery.
  • Finance: Fraud detection, algorithmic trading, personalized advice, and document analysis.
  • Manufacturing: Predictive maintenance, quality control, and real-time supply chain management.

We’re still early – AI will touch every part of the economy.

The Azumo Advantage: Why Our Approach Works

Azumo stands out because:

  • We started early: We built our first NLP app in 2016.
  • We understand the full stack: Vector databases, MLOps, cloud, models – we’ve got it all covered.
  • We have the talent: Talent is the limiting factor for AI adoption, and we solve it with our nearshore team.

McKinsey data shows AI roles are among the hardest to hire for:

  • AI Data Scientists: 78% of companies report difficulties
  • ML Engineers: 70% report issues
  • Data Engineers: 69% face hiring challenges

Our nearshore model gives you access to trained, time-zone-aligned talent.

  • We deliver full solutions: Not just models – full applications. Model training, optimization, deployment, and long-term support.

Success Stories: AI in Action

  1. Meta: We built AIML, a custom NER-based supplier discovery tool. What took hours now takes seconds. Delivered in 8 weeks with a small team.
  2. Discovery Channel: We developed a voice platform for Spanish-speaking users. Our custom NLP system powered interactive trivia content across Latin America.
  3. Omnicom: We built a real-time GenAI system for psychographic and sentiment analysis of social media data. It powers always-on cultural insights.

The Data Foundation: Where Every AI Journey Begins

Every AI transformation starts with data readiness. We guide companies through five levels of maturity:

  1. Descriptive analytics
  2. Diagnostic analytics
  3. Predictive analytics
  4. Prescriptive modeling
  5. Generative modeling

Jumping to GenAI without this foundation is a recipe for failure. We assess and strengthen your data before building.

Our Development Process: From Concept to Production

Our approach is structured for success:

  1. Data Preparation and Labeling: Garbage in, garbage out. We clean and structure data upfront.
  2. Model Selection and Implementation: Custom or off-the-shelf – we help you choose and configure the best.
  3. Training and Optimization: We iterate until the model performs. Hyperparameter tuning, validation, and testing.
  4. Deployment and Integration: We put it into production and integrate with your apps.
  5. Ongoing Optimization: Models need updates. We support and maintain them long-term.

The Talent Challenge: A Strategic Advantage

The AI talent shortage is real. Most companies struggle to fill roles like:

  • Data Scientists
  • ML Engineers
  • Data Engineers
  • AI Architects

Our model solves this with vetted talent across Latin America – people who are already working on cutting-edge AI problems. You get a deep bench without the hiring grind.

Looking Forward: The First Inning

This is just the beginning. Models will get better, cheaper, and easier to use. The companies that invest now will lead in the future.

Azumo is ready to help. We’ve helped startups and Fortune 50 firms use AI to drive real value. We’re ready to do the same for you.

The future belongs to intelligent apps. Are you ready to build them?

Let’s Talk

Ready to start your AI transformation? Let’s explore how Azumo can help you harness the power of artificial intelligence to drive innovation, efficiency, and competitive advantage in your organization.

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