AI Operations & Quality
Enabling more accurate, reliable, and useful AI.
Annotation, training data, LLM evaluation, human review, prompt testing, moderation support, and AI quality reporting.
Artificial intelligence is changing how companies process information, automate work, and deliver digital services. Doing it well still depends on data preparation, evaluation, monitoring, and human judgment.
SHLNK provides AI operations and quality services so organisations can get more from their data and keep AI work running to an agreed standard.
What this includes
01
Data Annotation & Labeling
Annotating and labeling text, image, audio, video, and other formats, including categorisation, tagging, and transcript alignment.
02
AI Training Data Preparation
Organising, preparing, cleaning, classifying, and validating training data for development and evaluation.
03
LLM Evaluation
Reviewing large language model responses for relevance, quality, instruction-following, detail, consistency, and other criteria.
04
AI Response Quality Review
Human review to classify irrelevant, missing, confusing, inconsistent, or inaccurate AI responses and highlight the problems.
05
Human-in-the-Loop Operations
Reviews, verifications, classifications, approvals, and other checks where generated content needs a person in the process.
06
Prompt Testing & Evaluation
Testing prompts against standard instructions and use cases, then highlighting what works and where issues appear.
07
AI Content Moderation Assistance
Reviewing and classifying text, images, and other content against your moderation policy, including escalation where instructed.
08
Image, Audio & Video Annotation
Object labeling, image categorisation, speech-to-text alignment, audio tagging, video categorisation, and related annotation tasks.
09
AI Quality Assurance & Reporting
Checklist reviews, error classification, statistics, and reports that show common issues and where quality can improve.
10
AI Data Quality & Consistency Checks
Checks for irrelevant, missing, incorrect, or incorrectly labeled items, and other data issues.
Our approach
We start from your expectations and the instructions for annotation, quality rules, and review. Quality assurance and escalation follow the data type, the complexity of the task, and the factors that matter to the project.
Who it is for
AI developers, machine learning companies, technology businesses, research institutions, training-data providers, and organisations adding AI to their products and services.
The value
Better-quality training data, consistent human quality checks, and thorough evaluation of AI responses for larger AI projects.