Our Services

AI Training Data & Annotation Services

You bring the data and requirements. We provide domain experts, QA workflows, and project management to deliver annotation, validation, evaluation, and review at the quality your models demand.


LLM Training Data

  • RLHF preference ranking and comparison data
  • SFT instruction-response pairs and multi-turn conversations
  • Prompt-response evaluation and scoring datasets
  • Domain-specific fine-tuning data for code, math, science, legal, medical, and creative tasks

Agentic AI & Tool Use

  • Function-call evaluation and tool-use annotation
  • Multi-step reasoning chain validation
  • Agent trajectory labeling and failure detection
  • Grounding, retrieval quality, and source-use assessment

Multimodal Annotation

  • Bounding boxes, polygons, segmentation masks, and keypoints
  • Video object tracking, frame review, and action recognition
  • Speech-to-text transcription, diarization, and intent labeling
  • OCR, document regions, 3D point cloud, and LiDAR annotation

Domain-Expert Labeling

  • Medical professionals for clinical NLP and healthcare annotation
  • Legal experts for contract review and legal document analysis
  • Finance specialists for fintech, compliance, and business data
  • Engineers, STEM experts, and code reviewers for technical annotation

AI Safety & Evaluation

  • Red teaming, jailbreak testing, and prompt injection probing
  • Bias detection across demographics, regions, and languages
  • Hallucination identification, factuality checks, and contradiction review
  • Content safety classification, toxicity labeling, and policy evaluation

Multilingual Data

  • Native-language experts across 40+ languages
  • Cultural adaptation, localization QA, and regional context review
  • Cross-lingual consistency checks and translation validation
  • Multilingual chatbot training, evaluation, and safety data

From Scope to QA-Checked Delivery

1. Project Scoping & Requirements

We define the project objective, dataset type, annotation goals, quality standards, timeline, output format, and delivery requirements.

  • Use case and annotation objective
  • Dataset type and volume
  • Timeline and delivery milestones
  • Quality standards and acceptance criteria
  • Output format and submission requirements

2. Data Collection & Preparation

We receive, review, clean, structure, and prepare the data for annotation while checking privacy and compliance requirements.

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

3. Annotation Guideline Preparation

We create clear annotation instructions so every annotator understands the task, labels, edge cases, and quality expectations.

  • Label definitions and taxonomy
  • Positive and negative examples
  • Edge-case handling
  • Reviewer instructions
  • Formatting and output standards

4. Annotation Tool Setup

We configure the right annotation tool, workflow, label schema, user roles, and export format for the project.

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

5. Pilot & Calibration

We run a pilot batch to validate guidelines, test annotator understanding, identify issues, and calibrate quality before full production.

  • Representative sample annotation
  • Annotator understanding check
  • Guideline issue identification
  • Quality expectation alignment
  • Pilot feedback and correction

6. Production Annotation

The production team completes annotation using approved guidelines while project managers track progress, consistency, and delivery milestones.

  • Task assignment and workload management
  • Consistent label application
  • Progress and productivity tracking
  • Reviewer escalation for edge cases
  • Regular delivery updates

7. Quality Assurance & Feedback

Reviewers check annotation quality, correct errors, update guidelines when needed, and provide feedback loops for continuous improvement.

  • Sampling-based or full QA checks
  • Error identification and correction
  • Annotator feedback and recalibration
  • Guideline updates when needed
  • Quality score tracking

8. Final Validation & Delivery

We conduct final validation, package the annotated data, confirm output quality, and deliver the dataset for client approval.

  • Final quality and consistency validation
  • Completeness and format checks
  • Annotated data packaging
  • Delivery documentation
  • Final submission and client sign-off

How We Work Together

You share the data and requirements. We handle annotation, validation, QA, and delivery through a clear process designed for speed, quality, and transparency.

1. Share Your Requirements

Tell us about your dataset, annotation goals, quality standards, timeline, and preferred delivery format. Reach out at sales@genmorphicsai.com.

2. Scope & Plan

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

3. Pilot & Align

We complete a pilot batch to validate guidelines, quality expectations, and communication flow before full production.

4. Full Delivery & Support

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

FAQ

Common Questions

Things people usually ask before getting started.

AI training data annotation is the process of adding structured labels, ratings, or human feedback to raw datasets so machine learning models can learn from them. It includes labeling images and video, writing or ranking text responses for LLMs, transcribing audio, extracting structured data from documents, and evaluating model outputs for accuracy, safety, and policy adherence.