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Content Analysis

Smart Matching at Scale: Efficient LLM Use with Data Integrity in Mind

Struggling with massive outcome sets in LLM applications? Instead of overloading your prompts with thousands of options—like SKUs, sizes, or ingredients—use semantic pre-filtering to dynamically narrow choices. This efficient prompt engineering technique reduces token usage, cuts inference costs, speeds up responses, and boosts accuracy by presenting only relevant options to the language model. Optimize your AI workflows by keeping prompts focused and letting your LLM deliver faster, more accurate results.

mathieu.isabel 
Content Analysis Privacy

🩺 Privacy‑First Content Analysis: Summarizing a Medical Note After Anonymization

Electronic health records are goldmines of clinical insight—but they’re also brimming with protected health information (PHI).With the Content API you can strip identifiers, keep context, and still run downstream AI tasks in a single pipeline. 📄 Sample Note (Before) Here’s a snapshot of a fake clinical note before we proceed with the analysis. 1) Define Text Extraction […]

mathieu.isabel 
Content Analysis

From PDF to JSON: Extracting Credit‑Card Statements with the Content API

Financial documents are full of rich, structured information hiding in messy layouts.In this walkthrough we’ll create an extraction analysis definition, upload a sample credit‑card statement, OCR the raw text, and then run our extraction to get clean, machine‑readable data — all with four API calls. StackLanguage: cURL (easy to translate to JS/Python SDK)Document: statement.pdf (2‑page Visa […]

mathieu.isabel 
Content Analysis Data Science

Content Analyzer – Audio Transcription Support

Today, we’ll explore a recent enhancement to the Content Analyzer: audio file analysis. This new capability allows businesses to uncover powerful insights from voice interactions by transcribing and analyzing audio files. In this blog post, we’ll showcase how the Content Analyzer works with a real-world example—a contact center call recording. Imagine a scenario where a […]

Mathieu Isabel 
Solver

Navigating Grey Zones – Picking an ETF

In this blog post, we explore how to use Solver, an optimization tool, to select the best ETF based on multiple factors. By inputting criteria such as Management Expense Ratio (MER), asset value, historical returns, and sector exposure, Solver helps investors make data-driven decisions. The post walks through a practical example where we balance cost, performance, and exposure to select an ETF that meets specific investment goals. With this approach, investors can efficiently navigate the complex process of ETF selection to achieve their financial objectives.

Mathieu Isabel