Enhance contextual sensitivity with engine annotation
Annotation for every step of the output process
Our data experts provide detailed annotations at every stage of the LLM logic chain, optimizing responses and reasoning capabilities. This includes increased contextual sensitivity, understanding, and reduced ambiguity in output.
Enhanced insights
Gain detailed and thoughtful insights into the thought processes and train-of-thought of your LLM, empowering you to better understand its behavior
Improved model scoring
Elevate the quality of your language model through writeups, quality control assessments, and human-in-the-loop feedback. Our annotations contribute to refining your LLM's performance and accuracy on an intricate level
Expert natural language feedback
Access expert natural-language comments, critiques, and feedback on a wide array of data types. This expert guidance ensures that your model's responses align with the highest standards of accuracy and reliability.
Reviews from early adopters
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"We had a novel task that we needed to complete on a short time scale. The Pareto team worked very closely with us to onboard, disambiguate, and scale up for fast task completion. We're continuing to work with the same pool of high quality raters for our newer tasks."
Prajit Ramachandran
Founding Researcher @ Character.AI
Join hundreds of fast-growing teams who count on Pareto to ensure factuality and honesty in their language models.
Leverage engine annotation for multiple use cases
Causal Reasoning Pair Annotation
Problem
An LLM is unable to understand the correct meaning of the phrase "I drew a bath" and accurately identify the causal relationships between actions and their outcomes within the context of this idiom.
Solution
In-depth engine annotation addresses this problem by engaging workers to provide detailed human insights and annotations. Workers analyze the given sentences to clarify the idiom's meaning and establish causal reasoning. They identify discrepancies and align the model's understanding with the intended meaning.
Benefits
- Enhanced Understanding: Workers help the model grasp idiomatic expressions and their causal relationships better, improving its language comprehension.
- Improved Accuracy: Through annotations, the model can provide more contextually accurate responses, enhancing user interactions.
- Cultural and Contextual Sensitivity: The annotations ensure that the model considers regional or cultural variations in language usage, making it more user-friendly.
- Reduced Ambiguity: By resolving ambiguities in language, this annotation improves the model's capacity to disambiguate phrases and expressions.
How it works
Describe your project
We help you develop clear project guidelines, determine the ideal evaluation team, and set a cost-effective hourly rate to fit your timeline
Match with top evaluators
We assemble your team same-day from our vetted network. If you have unique needs, we can find the right experts in just 3–5 days
Project managed & quality assured
We support data evaluators to deliver the highest quality data with paid trials, expert review and feedback, gold standard items, and more QA techniques
Built by and for a new generation of data workers
The infrastructure behind human data collection is antiquated. We’ve joined forces with seasoned data labelers, annotators, prompt engineers, and crowdwork researchers to redefine the relationship between workers and requesters.
Pareto operates on the principles of equitable compensation, collaborative management, and expert evaluation and feedback. Our mission is to empower talented and diverse professionals worldwide to contribute to AI training.
Enterprise-grade scale and quality
Fully managed service
Our project managers are just a Slack message or email away.
24/7 Global support
Our distributed team of experts offer assistance around the clock.
Pay-as-you-go
Up-front and transparent pricing tailored to your project requirements.
Common Questions
How long does it take to get set up with Pareto.AI?
+Our team can have you up and running with Pareto.AI in as little as 24 hours. Interested in getting started? Speak with our team!
Can I use Pareto.AI for a one-time project, or do I need to commit to a long-term contract?
+You do not need to commit to a long-term contract. Pareto.AI offers cost-effective and on-demand pricing. Fair hourly rates are set based on the expertise and skills of the workforce you need.
What measures does Pareto.AI take to ensure work quality?
+We create precise guidelines and cost estimates upfront. Your project manager reviews project timelines, costs, and success criteria with you before each batch of tasks to ensure results that meet or surpass your expectations.
Does Pareto.AI offer post-project support?
+Absolutely. Your project manager remains accessible to assist with any inquiries or issues that may arise following the project's completion. Should any outcomes fall short of your project's requirements, inform us within a five-day period after submission, and we'll either revise the work or provide a credit refund.
Can Pareto.AI assist with international projects outside the US?
+Pareto collaborates with companies worldwide, adapting to different time zones and team requirements. We have experience in handling international projects with ease. Our data experts are distributed across the globe, ensuring uninterrupted and reliable service around the clock.
How experienced is the team at Pareto.AI?
+Pareto boasts an elite network of prompt engineers, annotators, and evaluators with expertise in finance, healthcare, engineering, and more. We also recruit, train, and upskill people from all walks of life, striving to create a rewarding career in data work for anyone with the right ambition.
What types of projects can Pareto.AI support?
+Pareto.AI is adept at handling a diverse array of manual, data-centric tasks and operations for AI companies. From fine-tuning LLM's with human feedback to data curation and labeling, we do it all. Just share your objectives with us, and we'll customize our AI-driven workflows to suit your specific requirements.
Ensure factual accuracy for your models
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