From Prototype to Production: Why Most AI Projects Fail-and How to Make Yours Succeed

Innovation /

February 12, 2026 at 02:37 PM | Est. read time: 10 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. AI demos can be dazzling. A prototype that classifies documents, forecasts demand, or chats like a support agent can win instant buy-in. But moving from a promising proof of concept […]

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TensorFlow vs PyTorch: Production-Driven Technical Differences (What Actually Matters When You Deploy)

Artificial Intelligence /

February 12, 2026 at 03:24 PM | Est. read time: 10 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Choosing between TensorFlow and PyTorch is rarely about which framework is “better.” In real-world ML, the deciding factors are usually production constraints: deployment targets, latency requirements, hardware acceleration, monitoring, model

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Vector Databases Explained: Pinecone, pgvector, and Neo4j (Plus How to Choose)

Data Engineering /

February 12, 2026 at 04:13 PM | Est. read time: 10 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Vector databases have quickly become a foundational piece of modern AI-especially if you’re building applications powered by semantic search, recommendation systems, RAG (Retrieval-Augmented Generation), or LLM chatbots over private data.

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Why Semantic Search Has Become the New Standard (and What It Means for Your Business)

Artificial Intelligence /

February 12, 2026 at 04:35 PM | Est. read time: 10 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Search has changed-quietly but dramatically. For years, “good search” meant matching keywords. If a user typed “best laptop for video editing,” the search engine looked for pages containing those words,

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How to Choose an AI Model Without Compromising (A Practical, Decision-Ready Guide)

Artificial Intelligence /

February 10, 2026 at 04:44 PM | Est. read time: 10 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Choosing an AI model today isn’t just about accuracy, speed, or cost. It’s also about security, privacy, compliance, and operational control-especially when the model will touch sensitive customer data, internal

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PydanticAI: Validation and Reliability in LLM Applications (Without the Headaches)

Python /

February 10, 2026 at 04:47 PM | Est. read time: 12 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Large Language Models (LLMs) are great at generating text-but when you’re building real products, “pretty good” output isn’t good enough. You need reliable, predictable, and validated responses that won’t break

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Enterprise AI Governance: The #1 Challenge (and How to Get It Right)

Artificial Intelligence /

February 10, 2026 at 04:47 PM | Est. read time: 12 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Enterprise AI is no longer a “pilot project” conversation-it’s production, it’s customer-facing, and it’s making decisions that can affect revenue, reputation, and regulatory exposure. Yet, as organizations scale AI, one

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What Is AI Engineering (and Why the Role Is Growing So Fast)

Artificial Intelligence /

February 09, 2026 at 01:47 PM | Est. read time: 11 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. AI engineering has moved from a niche specialty to a core business capability in a remarkably short time. Companies aren’t just experimenting with machine learning (ML) anymore-they’re integrating AI into

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LangGraph and LangSmith: How to Orchestrate and Observe AI Agents (Without Losing Control)

Artificial Intelligence /

February 09, 2026 at 01:56 PM | Est. read time: 10 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. AI agents are moving fast-from “single prompt → single response” workflows to systems that plan, use tools, collaborate, and iterate. But once you go beyond a basic chatbot, two challenges

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LangChain for Enterprise Data: How to Build Secure, Production-Ready LLM Applications

Artificial Intelligence /

February 06, 2026 at 08:09 PM | Est. read time: 12 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Large Language Models (LLMs) are great at generating text-but they’re only as useful as the information they can reliably access. In most companies, the “truth” lives in internal systems: SharePoint

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