Finally a solution that will take you from unstructured content to a sound decision.
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Modern product organizations juggle hundreds of initiatives, features, dependencies, and metrics across multiple tools. Work lives in Jira, GitHub, Azure DevOps, Confluence, spreadsheets—and too often in people’s heads. Leaders feel the impact first: it’s hard to answer simple questions like: – What value streams do we actually have, and who owns them? – Which initiatives […]
The Validation API is a versatile tool for analyzing and validating complex content. With the recent v4 enhancements to the validation/assert-content method, the API can now provide structured, evidence-backed reasoning that incorporates factors — elements that need to be considered when evaluating an assertion and its outcome. Factors can be assumptions, contextual information, or facts […]
Learn how integrating Model Context Protocol (MCP) with Dretza enables AI agents to autonomously query descriptors, understand data relationships, and execute intelligent workflows. This breakthrough makes structured data accessible to LLMs through natural language, powering next-generation agentic applications for product categorization, inventory management, and automated data analysis.
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.
Discover powerful new capabilities in Dretza’s Extraction API, including natural language filtering, JsonPath-based filtering, and contextual extraction using business objectives and reference data. Learn how to extract only what matters — with precision, purpose, and explainability.
Learn how to improve extraction accuracy with our new feedback-driven refinement feature. This capability lets you enhance extraction definitions based on user or AI feedback—enabling better formatting, validation, and data structuring for use cases like booking receipts and invoices.
Explore the latest enhancements to the Extraction API, including advanced validation with hallucination detection and support for ground truth data. Learn how to flag inferred fields to improve accuracy reporting and streamline extraction workflows with confidence.
Learn how to use the Content API to perform automated cross-document assertions. This guide walks through validating a credit card transaction against a booking invoice using structured analysis definitions, complete with explainable results and evidence-based conclusions. Ideal for claims validation, auditing, and payment reconciliation workflows.
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 […]
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 […]
