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Generating better blog posts with LLMs →
Observe, evaluate and optimize your AI data extraction pipelines
Trace unstructured data flows with Datograde and evaluate them with human and AI feedback. Build AI data extraction pipelines faster and optimize them continuously.
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Sunnythisweekend scales travel weather content with AI
3,600 landing pages in 4 weeks

CiaoHello builds bilingual language learning app
80% more multi-channel content
Evaluate
Unified platform accelerating human and AI-driven evaluations
Human experts can score and comment on AI outputs, then track issues for every data extraction use case. Use schemas and data checks to automatically grade extracted data for accuracy and recall. Combine the best of both worlds to build trust at scale.
Upload
Upload datasets into Datograde to as a starting point for generating content and delivering data into your AIs.
Edit
Edit datasets manually, or use visual tools to perform common data operations like inserts, joins, and pivots.
Convert
Convert data points, records, and datasets from one format to another.
Prompt Engineering
Tools for creating and engineering prompts for large language models.
Validate
Automatic schema generation, data validation, and tools for manual data review and labelling.
Blog
Building a simple data tool is hard. Here are the lessons we learned along the way:
How to build a company research agentGenerating Better Blog Posts with LLMsData extraction using generative AI [2025 guide]AI and LLM ObservabilityWhat is AI and LLM Evaluation?Web Scraping with AWS LambdaHow to extract data from PDFs with PythonThe Ultimate Guide to Fine-Tuning AI Models: Comparing Offerings from OpenAI, Google, Meta, and MoreNew: Advanced Merge Tools and Dynamic PipelinesNew: Files, Merge, and SaveNew: Markdown, Text, and TSVBuilding an Automatic Schema Generator for Arbitrary Datasets