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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.

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What We Do

End-to-End AI Data Services

You provide the data and requirements. We handle annotation, validation, review, and quality control through domain-vetted experts and managed QA workflows.

LLM Training Data

Our experts write, rank, and evaluate RLHF, SFT, and prompt-response datasets. Real domain knowledge in every preference label, instruction pair, and model evaluation.

RLHFSFTInstruction Tuning

Agentic AI & Tool Use

Our annotators evaluate function calls, trace reasoning chains, and find failure points in agent workflows. This is the human feedback loop agentic AI systems need.

AgentsFunction CallingReasoning

Multimodal Annotation

We annotate image, video, audio, OCR, and multimodal datasets with bounding boxes, segmentation, keypoints, video tracking, transcription, and structured labels.

VisionVideoAudio

Domain-Expert Labeling

Your data is reviewed by domain-matched professionals across healthcare, legal, engineering, STEM, finance, code, safety, and multilingual workflows.

HealthcareLegalFinance

AI Safety & Evaluation

Our evaluators stress-test models for jailbreaks, bias, hallucinations, factuality issues, unsafe responses, and policy violations before deployment.

Red TeamingBias TestingSafety

Multilingual Data

Native-language experts across 40+ languages annotate, translate, localize, and validate data with the cultural context global AI products require.

40+ LanguagesLocalizationTranslation QA

How We Deliver

From Scope to QA-Checked Delivery

  1. Plan & Prepare

    Steps 1–3

  2. Set Up & Pilot

    Steps 4–5

  3. Produce & Deliver

    Steps 6–8

Phase 1Plan & Prepare

Step 1: Scoping & Requirements

Agree the objective, data, quality bar, timeline, and output format.

  • Annotation goals
  • Acceptance criteria
  • Milestones
1/8

Step 2: Data Preparation

Receive, clean, and batch your data, with privacy checks built in.

  • Verification
  • Deduplication
  • Compliance
2/8

Step 3: Annotation Guidelines

Clear instructions covering every label, example, and edge case.

  • Label taxonomy
  • Examples
  • Edge cases
3/8

Phase 2Set Up & Pilot

Step 4: Tool Setup

Configure the annotation tool, label schema, roles, and export.

  • Label schema
  • Access roles
  • Export format
4/8

Step 5: Pilot & Calibration

A pilot batch validates guidelines and aligns quality before scaling.

  • Sample batch
  • Annotator checks
  • Quality alignment
5/8

Phase 3Produce & Deliver

Step 6: Production Annotation

Trained annotators label at scale while PMs track progress.

  • Workload planning
  • Escalations
  • Progress updates
6/8

Step 7: QA & Feedback

Reviewers check and correct work, then feed lessons back to the team.

  • Sampled or full QA
  • Recalibration
  • Quality scores
7/8

Step 8: Validation & Delivery

Final checks, packaging, and handoff for your sign-off.

  • Format checks
  • Documentation
  • Client sign-off
8/8

Engagement Process

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.