Our Services

Develop your AI model with

Genmorphics AI Solution

We deliver end-to-end data annotation services for AI teams, combining domain experts, strict quality control, and scalable workflows tailored to your project goals.


LLM Data Training

  • Reinforcement Learning (RLHF)
  • Supervised Fine-Tuning (SFT)
  • Unmatched Precision for Models
  • Comprehensive Guidance

Data Analysis

  • Analyzing large datasets
  • Extracting meaningful insights
  • Data cleaning and preprocessing
  • Exploratory data analysis (EDA)

Machine Learning

  • Image Recognition
  • Speech Recognition
  • Recommendation Systems
  • Anomaly Detection

Custom AI Solutions

  • Business Requirements
  • Consulting
  • Prototyping
  • Development & Implementation

Model Optimization

  • Model Deployment
  • Performance Optimization
  • Drift Monitoring
  • Accuracy Monitoring

Data Services

  • Strategic Guidance
  • AI Consulting
  • Use Case Identification
  • Roadmap Development

Work process

1. Project Scoping

Define the overall scope and requirements of the data annotation project to ensure alignment with client or internal expectations.

  • Annotation objective and use case
  • Data type and annotation type
  • Volume, timeline, and delivery milestones
  • Quality targets and acceptance criteria
  • Output format and submission requirements

2. Data Collection and Preparation

Receive and prepare raw data to make it suitable and ready for annotation while ensuring consistency and data integrity.

  • Data receipt and verification
  • Removal of corrupted or irrelevant data
  • Data structuring and batching
  • Duplicate and error handling
  • Privacy and compliance checks

3. Prepare Annotation Guidelines

Create clear and detailed annotation guidelines to ensure consistent and accurate labeling across all annotators.

  • Label definitions and taxonomy
  • Annotation rules and conventions
  • Positive and negative examples
  • Edge case handling
  • Formatting and output standards

4. Annotation Tool Setup / Selection

Select and configure the annotation tool that best fits the project requirements and data type.

  • Tool selection based on data type
  • Label schema configuration
  • User roles and access setup
  • Task distribution setup
  • Output format validation

5. Initial Pilot

Conduct a small-scale pilot annotation to validate guidelines, tool setup, and annotator understanding before full execution.

  • Annotating a representative data sample
  • Evaluating annotation consistency
  • Identifying guideline or tool issues
  • Collecting early feedback

6. Data Annotation Execution

Perform full-scale data annotation following approved guidelines and refined processes from the pilot phase.

  • Task assignment and workload management
  • Consistent application of labels
  • Tracking progress and productivity
  • Flagging ambiguous or unclear cases

7. Quality Assurance and Feedback

Review annotated data to ensure it meets quality standards and provide feedback to annotators for correction and improvement.

  • Sampling-based or full QA checks
  • Error identification and correction
  • Annotator feedback and recalibration
  • Updating guidelines if needed

8. Final Validation and Submission

Conduct final checks to confirm data completeness and quality before delivering the annotated dataset to the client or downstream team.

  • Final quality and consistency validation
  • Completeness and format checks
  • Packaging annotated data
  • Final submission and client sign-off

Collaborate with Genmorphics AI Solution

Here is what happens after you reach out for our services. We keep the process clear and practical so your team can move quickly.

1. Share your requirements

Tell us about your dataset, annotation goals, preferred quality standards, and expected delivery timeline by emailing business@genmorphicsai.com.

2. Scope and plan

Our team reviews your needs, recommends the right workflow, and shares a clear project scope with timelines and pricing.

3. Pilot and alignment

We start with a pilot batch to validate guidelines, quality expectations, and communication flow before full execution.

4. Full delivery and support

Once aligned, we scale production, deliver in agreed milestones, and continue QA support throughout the engagement.