You will get Policy-Based Data Labeling & Rule-Driven AI Evaluation
Project details
I provide policy based data labeling and AI evaluation services for teams that require accuracy, consistency, and strict adherence to annotation guidelines.
This project focuses on rule driven text classification and structured annotation, where each label is applied according to predefined rules, priority hierarchies, and override conditions. Typical tasks include identifying statement intent, urgency levels, scope, and other structured attributes used in AI training and evaluation pipelines.
My approach emphasizes instruction fidelity, consistency across edge cases, and standards based decision making rather than subjective interpretation. Labels are applied carefully and reviewed to ensure correctness and repeatability, which is essential for reliable model performance and evaluation.
With a background in civil engineering and experience working on structured annotation workflows, I bring analytical rigor, process discipline, and attention to detail into every project. This service is well suited for AI evaluation teams, research groups, and operations heavy organizations that depend on high quality labeled data.
This project focuses on rule driven text classification and structured annotation, where each label is applied according to predefined rules, priority hierarchies, and override conditions. Typical tasks include identifying statement intent, urgency levels, scope, and other structured attributes used in AI training and evaluation pipelines.
My approach emphasizes instruction fidelity, consistency across edge cases, and standards based decision making rather than subjective interpretation. Labels are applied carefully and reviewed to ensure correctness and repeatability, which is essential for reliable model performance and evaluation.
With a background in civil engineering and experience working on structured annotation workflows, I bring analytical rigor, process discipline, and attention to detail into every project. This service is well suited for AI evaluation teams, research groups, and operations heavy organizations that depend on high quality labeled data.
AI Development Type
Model Tuning, Recommendation SystemWhat's included
| Service Tiers |
Starter
$35
|
Standard
$45
|
Advanced
$50
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | - | - | - |
Detailed Code Comments | - | - | - |
Knowledge Graph | - | - | - |
Model Documentation | - | - | - |
Ontology | - | - | - |
Source Code | - | - | - |
Taxonomy | - | - | - |
Frequently asked questions
About Nana Kwabena Kwarteng
AI Data Annotation, Evaluation & QA | Rule-Based Text Classification &
Tema, Ghana - 4:31 am local time
I have hands on experience in text and image annotation, document labeling, structured data preparation, and policy driven text classification. My work includes evaluating statement intent, urgency, and scope under strict rules, as well as performing QA reviews to ensure consistency and correctness across datasets.
I also support assessment quality assurance and interview integrity evaluation for AI mediated hiring workflows. This involves reviewing recorded sessions against defined policies, identifying inconsistencies or violations, and ensuring fairness and compliance in automated evaluation systems.
With a background in civil engineering, I bring strong analytical thinking, process discipline, and attention to detail into every project. I am comfortable working with technical datasets, engineering documentation, and standards driven workflows where accuracy matters more than speed.
If you are looking for a reliable partner for AI data annotation, evaluation, or QA work, I deliver accurate, secure, and timely results.
Steps for completing your project
After purchasing the project, send requirements so Nana Kwabena Kwarteng can start the project.
Delivery time starts when Nana Kwabena Kwarteng receives requirements from you.
Nana Kwabena Kwarteng works on your project following the steps below.
Revisions may occur after the delivery date.
Review dataset and labeling guidelines
Confirm scope, annotation rules, priorities, and edge cases before starting.
Apply policy based labelling
Label data strictly according to defined rules and evaluation criteria.