AI Training Data, Annotation, and Evaluation Services for Enterprise AI Teams
Industries We Serve
What We Do
End-to-End AI Data Services
Six service lines covering the data work behind LLM, multimodal, and agentic AI systems.
LLM Training Data
RLHF, SFT, and prompt-response datasets written and ranked by domain experts.
Agentic AI & Tool Use
Function-call evaluation, reasoning-chain tracing, and agent failure analysis.
Multimodal Annotation
Image, video, audio, and OCR labeling — boxes, segmentation, keypoints, tracking.
Domain-Expert Labeling
Clinicians, lawyers, and engineers reviewing the data that needs their expertise.
AI Safety & Evaluation
Red teaming, bias detection, and hallucination review before you deploy.
Multilingual Data
Native speakers across 40+ languages for translation, localization, and QA.
Our Process
From Sample Data to Production Delivery
A clear five-step process from share to deliver, built for the speed and quality enterprise AI teams expect.
Share Requirements
Tell us about your dataset, annotation goals, quality standards, timeline, and delivery format.
- Dataset type and volume
- Quality targets
- Timeline and format
Scope & Pilot
We review your requirements, recommend the right workflow, and complete a pilot batch to align quality expectations.
- Workflow recommendation
- Representative pilot batch
- Quality alignment
Build Guidelines
We create or refine annotation guidelines, label definitions, examples, edge cases, and acceptance criteria.
- Label taxonomy
- Edge-case handling
- Acceptance criteria
Scale Production
A trained team completes annotation with reviewer oversight, QA checks, and project management support.
- Trained annotation team
- Reviewer oversight
- Progress tracking
Deliver & Improve
You receive versioned output with QA notes, correction handling, and feedback-based iteration.
- Versioned dataset delivery
- QA notes and corrections
- Feedback-driven iteration
Domain-Matched Teams
Experts assigned by use case, language, and quality history
Pilot-First Approach
Validate guidelines and quality on a small batch before scaling
Multi-Level QA
Reviewer checks, expert audits, and inter-annotator agreement tracking
Transparent Delivery
Milestone-based handoff with versioned output and clear acceptance criteria
Free scoping call. No commitment required.
Why Genmorphics
Built for Enterprise AI Data Needs
Annotation, validation, and evaluation built to enterprise standards: vetted experts, measured quality, secure delivery, and a pilot-first approach to scale.
Domain-Vetted Experts
Every annotator is screened by a domain lead before joining a project. Clinicians on medical work. Lawyers on legal. Engineers on code. No crowd workers on specialist tasks.
Managed QA at Scale
Multi-level review, inter-annotator agreement tracking, and reviewer adjudication built into the workflow. We measure quality per project and report it alongside delivery.
Data Security First
NDA per annotator, role-based access, support for client-controlled environments, and audit trails on every annotation action. We follow your security model, not the other way around.
Pilot-to-Production Path
Start with a 3 to 5 day pilot batch. We validate guidelines, calibrate the team, and stress-test tooling before scaling. Production only begins when both sides agree on what good looks like.
Our Trainers
Our Expert Team
Here are a few of the professionals who deliver quality on client projects.
Nuzhat
Multimodal QA Lead
Shafew
Data Extraction Expert
Peyal
Bounding Box QA
Saaquib
Edge Case QA Reviewer
Elena Volkov
Multilingual Content Expert
Dr. Kenji Tanaka
AI Safety Evaluator
For Domain Experts
Active Project Categories
Open work for domain experts across the areas our clients need most.
Testimonials
What Clients Say
Feedback from AI teams using Genmorphics for annotation, evaluation, validation, and quality-controlled data delivery.
David P.
Head of AI, Series B AI Startup
“Two annotation vendors burned us before Genmorphics. Nafis's team was the first group that actually understood what RLHF data needs to look like. 50K annotations at 98% agreement. Did not think we could hit that on the timeline we had.”
Maria S.
VP of Engineering, Healthcare AI Company
“Nuzhat ran QA across a 100-person annotation team on our multimodal medical dataset. Inter-annotator agreement stayed above 90% the entire ramp. After retraining on her team's labels, our model error rate dropped 34%. We stopped second-guessing the annotations.”
Thomas W.
CTO, Legal Tech Startup
“Shafew handled PDF data extraction across our entire contract corpus. Entity extraction hit 96.7%. He flagged edge cases our in-house lawyers had missed and walked us through why specific extractions were tricky instead of only sending numbers.”
Ready to Build Better Training Data?
Send us your sample task or project requirements. We'll recommend the right workflow, expert team, timeline, and pricing model.
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