Business model and value proposition ChatGPT and prompt engineering were the engines we used to convert a technical capability into a predictable commercial engine. In the first product sprint we focused on building AI-driven services that solved a concrete pain: teams needed fast, high-quality content, code scaffolding, and data enrichment
Correlation vs Causation Defined Building on the foundation we established earlier, the difference between correlation and causation is the single most important distinction you’ll make when moving from descriptive analytics to causal inference. Correlation describes a statistical association: two variables move together more often than not, which you can quantify
Why Conversational AI Matters Conversational AI has moved from novelty to business-critical infrastructure because it changes how users interact with systems at scale. In the first interaction with a customer you want reliable intent recognition, contextual follow-up, and consistent tone—capabilities that modern conversational AI platforms provide through models, orchestration, and
Why enhance bar charts If your Power BI reports rely on basic bar charts, you’re leaving insight—and user attention—on the table. Readers scan dashboards quickly, and simple visual tweaks change whether they notice a trend or miss it entirely; enhancing bar charts improves readability and directs focus to the right
What is Conversational AI? When you picture a customer chatting with a brand on your website or an engineer querying logs via a Slack bot, that’s the everyday surface of conversational AI: systems that let people interact with software using natural language instead of menus or API calls. At its
Attention concept overview Building on this foundation, think of an attention mechanism as a dynamic routing layer that lets the model decide which parts of the input deserve computation and which can be ignored. In the first 100 words we’ll anchor core terms: attention mechanism and self-attention are the primitives
What Is an Entity in Slate Building on this foundation, think of a Slate entity as the canonical schema unit that represents a thing you care about inside Slate CRM—the object type you query, report on, and run workflows against. An entity in Slate is not merely a form or
Early Rule-based and Symbolic NLP Building on this foundation, it helps to revisit how rule-based NLP and symbolic methods shaped early natural language processing research and production systems. In the 1970s–1990s, rule-based NLP emphasized explicit grammars, handcrafted lexicons, and symbolic knowledge representation rather than statistical learning. These approaches prioritized interpretable
Defining Analytics Engineering and Scope Building on this foundation, we need a precise, practical definition so you can decide where to invest engineering effort and team skill. Analytics engineering is the discipline that turns raw ingestion into trusted, documented datasets ready for analysis, sitting squarely between data engineering and analytics/BI.