Testing table
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed lacinia imperdiet lorem quis consectetur. Phasellus maximus vehicula nisl, eu molestie ante posuere
at. Aliquam eleifend massa eget ligula molestie aliquam. Pellentesque in diam ac purus pretium mollis. Donec aliquam, nulla id vestibulum scelerisque, quam ante efficitur tellus, in commodo leo turpis eget sem. Vivamus condimentum mauris erat, non fermentum odio porta eu. Mauris mollis elementum tellus, ac vulputate diam mollis sit amet. Integer ac egestas dui, non convallis orci. Aenean aliquet scelerisque lacus. In sed egestas mi, non volutpat ante. Aenean facilisis eleifend lobortis. Suspendisse accumsan mauris tortor, accumsan congue quam fringilla non. Vestibulum vitae placerat magna. Mauris placerat nisl ipsum, vel facilisis eros iaculis non.
Default styling
| Total User Count | 20 Users | 35 Users | 50 Users |
| Platform Fee | - | - | - |
| Per Label Rate | Unlimited Annotations | Unlimited Annotations | Unlimited Annotations |
| Savings from List ($300/user/mo.) | $18,400 | $31,500 | $63,000 |
First row highlighted
| Total User Count | 20 Users | 35 Users | 50 Users |
| Platform Fee | - | - | - |
| Per Label Rate | Unlimited Annotations | Unlimited Annotations | Unlimited Annotations |
| Savings from List ($300/user/mo.) | $18,400 | $31,500 | $63,000 |
First column highlighted
| Total User Count | 20 Users | 35 Users | 50 Users |
| Platform Fee | - | - | - |
| Per Label Rate | Unlimited Annotations | Unlimited Annotations | Unlimited Annotations |
| Savings from List ($300/user/mo.) | $18,400 | $31,500 | $63,000 |
Related Content
-
Everybody Is (Unintentionally) Cheating
AI benchmarks are quietly failing us. Studies reveal that data leakage, leaderboard manipulation, and misaligned incentives are inflating model performance. This blog explores four pillars of reform, governance, transparency, broad-spectrum metrics, and oversight, and outlines how enterprises can build trust through a centralized benchmark management platform.
Nikolai Liubimov
May 13, 2025
-
3 Annotation Team Playbooks to Boost Label Quality and Speed
Not every ML team looks the same and your labeling workflow shouldn’t either. In this guide, we break down three common annotation team setups and how to tailor your tools and processes to boost quality, speed, and scale.
Alec Harris
May 7, 2025
-
Seven Ways Your RAG System Could be Failing and How to Fix Them
RAG systems promise more accurate AI responses, but they often fall short due to retrieval errors, hallucinations, and incomplete answers. This post explores seven common RAG failures—from missing top-ranked documents to incorrect formatting—and provides practical solutions to improve retrieval accuracy, ranking, and response quality. Learn how to optimize your RAG system and ensure it delivers reliable, context-aware AI responses.
Micaela Kaplan
March 19, 2025