Data Labeling Services
Let our skilled data labeling specialists meticulously enrich your datasets, ensuring your AI models achieve maximum accuracy and performance.
Data Labeling Services
At Dignep Group Pvt.Ltd., we know that great machine learning starts with crystal-clear data. Our dedicated team takes pride in delivering data labeling services that are both precise and reliable—always handled with care by trained professionals who truly understand the value of clean, well-organized data.
Whether it’s images, video, audio, or text, our experts are equipped to annotate a wide range of data formats, ensuring your projects get the attention and expertise they deserve. We believe every piece of data matters, and treat your datasets as if they were our own.
Why Our Data Labeling Makes the Difference
Your model’s accuracy and performance hinge on the quality of its training data. We go beyond basic labeling by applying industry best practices for every use case, whether you’re building computer vision, speech, or NLP systems. Our process includes thorough validation and multi-stage quality checks, so you can be confident your models are learning from the most accurate datasets possible.
How Our Data Labeling Works
Consultation: We sit down with you to understand your goals, data types, and project requirements.
Specification: Together, we define guidelines for consistent, high-quality annotations—no guesswork, just clarity.
Annotation: Our skilled team labels your data according to your exact specifications, using secure, scalable platforms.
Quality Assurance: Experts review and verify all annotations, resolving ambiguities and correcting errors.
Delivery & Feedback: We deliver your labeled data, ready for training. Need adjustments? We’re here to iterate until it’s right.
Long-Term Support: As your needs grow, we adapt our services—expanding volume, adjusting guidelines, or integrating with your pipeline as your priorities shift.
Scalable Solutions, No Matter Your Size
From pilot projects to industry-scale datasets, our approach flexes to match your ambitions. Startups get the same care and attention as our largest clients. With Dignep, you get reliability, transparency, and a collaborative partner invested in your AI success.
Data Labeling Services
Get high-quality, human‑in‑the‑loop annotation for text, images, audio, and structured data—powered by trained teams in Nepal and aligned with your AI and LLM projects.
Designed for real AI workloads
We don’t just label for toy datasets. Our processes are tuned for production‑grade machine learning, LLM evaluation, and retrieval‑augmented generation (RAG) systems.
Flexible and scalable teams
Scale from small pilots to large, ongoing projects with dedicated labeling pods, QA reviewers, and project coordinators based in Nepal.
Quality and governance built in
Clear guidelines, multi‑layer QA, and transparent reporting help you trust your labels and meet internal quality, privacy, and compliance expectations.
What You Can Use Our Data Labeling For
We support a wide range of use cases—from traditional machine learning to modern LLM and RAG systems.
NLP, classification & LLM evaluation
Intent detection, topic tagging, sentiment, toxicity, entity labeling, and LLM response scoring for chatbots, assistants, and internal tools.
Document labeling & retrieval
Segment and tag documents, define relevance labels, and build ground‑truth datasets for retrieval evaluation and RAG quality checks.
Computer vision & content safety
Image categorization, basic bounding boxes, content safety tagging, and media classification for risk, compliance, or user experience.
Structured data tagging
Labeling of events, anomalies, and custom categories across tabular datasets, logs, and monitoring data to support ML and analytics.
Development & public sector projects
Data labeling for governance, infrastructure monitoring, survey data, or donor‑funded programs where quality and consistency are critical.
Domain‑specific labeling
We work with your subject‑matter experts to define task‑specific taxonomies and examples tailored to your sector and risk profile.
Our Data Labeling Process
A structured pipeline to ensure consistency, speed, and auditability across your labeling projects.
Task definition & taxonomy
We work with your product, data, or research team to define clear tasks, label taxonomies, edge cases, and quality targets.
Guidelines & tooling setup
We create annotation guidelines with examples, set up labeling tools (your platform or ours), and prepare data sampling and privacy controls.
Pilot batch & calibration
A small pilot batch helps calibrate interpretation, measure inter‑annotator agreement, and refine definitions before scaling.
Production labeling & QA
Trained labelers execute at scale with layered QA (self‑check, peer review, and lead review) plus regular feedback loops with your team.
Reporting & iteration
You get visibility into volumes, error types, and quality metrics so we can jointly tune guidelines and improve over time.
Quality, Security, and Tooling You Can Trust
As an ISO/IEC 20000‑1:2018 certified software company, Dignep brings structured processes, documentation, and support practices to data labeling as well.
Quality controls
Security and privacy
Tools and integration
We can work in your preferred labeling platform or set up one for you. Our teams are familiar with common SaaS tools and custom in‑house tools, and we coordinate closely with your data engineering and MLOps teams.
Flexible Pricing and Engagement Models
Different AI teams have different data needs. We offer flexible data labeling engagement models so you can start small, prove value, and then scale.
Pilot labeling project
A one‑time batch to test guidelines, validate quality, and estimate full‑project effort. Ideal if you are just starting with a new AI use case or data source.
Includes: guideline design, pilot batch, QA review, and recommendations for scaling.
Ongoing monthly labeling
A dedicated team working on a consistent labeling backlog every month. Good for organizations with continuous data inflow and regular model retraining cycles.
Includes: stable team, monthly volume commitments, and predictable billing.
Project‑based labeling
Fixed‑scope data labeling tied to specific datasets, deadlines, and milestones. Suitable for clearly defined AI projects or evaluation campaigns with set timelines.
Includes: detailed scope, milestones, and clear acceptance criteria.
Frequently Asked Questions about Data Labeling
Here are some common questions AI and data teams ask when they start working with us on data labeling projects.
Talk through your data labeling needsWhat types of data can Dignep label?
We label text, documents, images, basic video and audio segments, and structured/tabular data. If your use case involves a custom format, we can usually adapt our process with an initial pilot and additional onboarding.
Can you support LLM evaluation and safety reviews?
Yes. Our teams can label LLM outputs for quality, relevance, safety, and policy compliance. We also support A/B comparisons between prompts or models to help you choose the best configuration for your use case.
Will you work inside our existing labeling tool?
We can work in your preferred labeling platform if you already have one in place, or we can recommend and configure a third‑party or custom solution. Tool selection usually depends on your security, volume, and integration requirements.
How do we get started with a data labeling project?
We usually begin with a short discovery call to understand your data, use case, and success criteria. From there, we propose a pilot batch and guideline design. After the pilot is validated, we scale to full production labeling.
Ready to take the next step?
Let us take care of your data preparation, so you can focus on what really matters: building and deploying groundbreaking AI solutions.
