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Our consultants share decades of collective expertise in software development on the Chariot blog through tutorials, analyses, and insights on the business of software.

AI Is Writing Code. But Who’s Responsible for What It Gets Wrong?

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A recap of our recent Tech Talks for Health Tech with Layer 8 Security Last week, Chariot Solutions joined forces with our partners at Layer 8 Security to host an evening of honest conversation about a pressing question for businesses. As AI takes on more of the work of writing software, who owns the consequences when something goes wrong? Moderated by Jeff Lipson of Layer 8 Security, the panel featured Dan Costantino and Matt Winter, two practitioners with deep experience…

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Chariot Solutions in 2025: AI, Modernization, and the Evolving Role of Technologists

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2025 was a year of acceleration—not just in tools, but in how organizations think about building, shipping, and sustaining software. At Chariot Solutions, our blog reflected the questions we were hearing most often from clients, partners, and the broader technology community: How do we move from experimenting with AI to using it responsibly on a daily basis? What does modernization really mean when legacy systems still power critical workflows? And as technology evolves faster than ever, what does it mean…

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Event Recap: What Does It Mean to Be a Developer in the Age of AI Agents?

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What Does It Mean to Be a Developer in the Age of AI Agents? October 15, 2025, Philadelphia, PA At a recent Chariot Solutions panel discussion hosted at Certara (thanks Martin Snyder!), technology leaders gathered to explore one of the most pressing questions facing the software industry today: how is the developer’s role evolving as AI agents become integral to the software development lifecycle? Moderated by veteran tech executive Grace Francisco, co-founder and partner at EMG Worldwide, the conversation brought…

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Philadelphia’s Healthtech Moment: Chariot’s Tracey Welson-Rossman Calls for a New Era of Collaboration

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Greater Philadelphia’s innovation economy is evolving—and it’s time to see it as more than just “eds and meds.” In a recent guest post for Technical.ly, Tracey Welson-Rossman, Chief Marketing Officer at Chariot Solutions, explores how software engineers, data scientists, and digital health innovators are transforming the region’s healthcare landscape. Her article highlights the growing momentum in healthtech, from AI-driven diagnostics to digital engagement platforms, and introduces Chariot’s new initiatives—the Health Tech Heat Map and Tech Talks for Health Tech—created to…

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Adding Conversational AI to Your App — Part 3: The UX of Chat Interfaces: What We Gain and Lose

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In Part 1 and Part 2, we focused on the mechanics of translating natural language into commands and making the chat more context aware, leading to a more conversational experience. But stepping back, there’s a bigger question: what does a natural language interface actually do for the user experience? After all, if we’re going to replace buttons, input elements, and menus with free-form words, we should be clear about what we gain, and what we give up. What Natural Language…

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Adding Conversational AI to Your App — Part 2: Adding Conversation History to the Context

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Tool Limitations In Part 1, we went over converting natural language requests into commands, using LLMs. That’s a nice improvement from having to enter slash commands, but the current tool still has two major limitations: No flexible editing: There’s no way to update or delete an existing item using natural language. When referring to a todo, you have to use its list position (e.g., “the second item”, “1”), instead of describing it naturally (“Delete the milk reminder”, “Make filing taxes…

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Adding Conversational AI to Your App — Part 1: Converting Natural Language into JSON Actions

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What We’re Building When I recently paired with Claude Code to build a text-based to-do app (code available on Github, disclaimer: mostly generated by Claude), I wanted to add a simple natural language interface. In this post, we’ll build that feature using TypeScript, Node, and an OpenAI model, without using any special frameworks that might get in the way of learning.  We’ll start with the gpt-3.5-turbo model. It’s not the most powerful model, but it’s cost-effective. In future posts, we’ll…

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Migrating Java Applications to Kubernetes

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Your CTO messages you out of the blue one day: How much effort would it be to run XYZ on Kubernetes? Shudder. Although there are some nuances to the process, it is fairly straightforward. This article assumes some knowledge of containers, Kubernetes, and JVM applications. Our goal is to migrate the application with as few changes as possible and for the application to run as well or better on Kubernetes. Use a Cgroups-Friendly JDK The first step in migrating your…

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Unleashing AI in Marketing: Key Insights from Slice Communications & Chariot Solutions Webinar

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AI is reshaping marketing as we know it, but how do we use it effectively, ethically, and strategically? On January 15, 2025, Slice Communications and Chariot Solutions hosted a webinar titled “Unleashing AI in Marketing: Strategies to Transform Your Business” to tackle this pressing question. Featuring a panel of AI experts, digital strategists, and marketing leaders, the discussion explored real-world applications, ethical considerations, and the future of AI-driven marketing. If you missed the live session, here’s a breakdown of the…

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Populating a Data Lake with AWS Database Migration Service and Amazon Data Firehose

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Data lakes are great for holding large volumes of data, such as clickstream logs. But such data has limited usefulness unless you can combine it with data from your transactional, line-of-business databases. And this is where things get tricky. Simple approaches, such as replicating entire tables, don’t scale. Streaming approaches that include updates and deletes require logic to determine the latest value (or existence!) of any given row. All of which has to be translated into static data files in a data lake.

In this post I look at one approach to solve this problem: AWS Data Migration Service to capture changes from the source database and write them to a Kinesis Data Stream, and Amazon Data Firehose to load those records into Iceberg tables.

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