Why the Choice Matters
Training data sets the ceiling for your model. A partner that labels inconsistently, misses edge cases, or delivers late does not just slow you down; it quietly caps how good your model can become. Relabeling a dataset is almost always more expensive than labeling it well the first time.
Start With Your Data, Not the Vendor List
Before you compare vendors, write down what you need: data types, annotation methods, class list, expected volume, accuracy target, and deadline. A one-page brief makes quotes comparable and exposes vendors who cannot handle your task.
Insist on a Pilot With Your Own Data
Demo datasets always look good. Ask every shortlisted vendor to annotate the same representative sample of your data, including a few hard cases. Compare accuracy, consistency, turnaround, and the questions they ask. Good partners ask a lot of questions.
Ask How Quality Is Measured
“High quality” means nothing without a method. Ask how many review stages there are, who does the reviewing, how disagreements are resolved, and what you receive with each delivery. Look for measurable targets and batch-level QA reports.
Check Security and Compliance
Your data may include personal information or unreleased product imagery. Confirm NDAs, access controls, where data is stored, how long it is kept, and whether the vendor follows GDPR-aligned practices.
Understand Pricing and Hidden Costs
Per-object, per-hour, and fixed-project pricing each suit different tasks. Ask what is included: setup, guideline writing, re-work, QA, and project management. The cheapest rate often becomes the most expensive once corrections are counted.
The Checklist
- A written brief with classes, volume, and accuracy target
- A pilot on your own data, including hard cases
- Documented, multi-stage QA with delivery reports
- NDA, access controls, and clear data retention
- Transparent pricing with re-work included
- A named day-to-day contact and clear escalation path
About Smart Annotahub
Smart Annotahub is a managed data annotation company based in Ha Noi, Viet Nam. Our in-house annotators, QA leads and project managers turn raw image, video, 3D, geospatial, text and audio data into training-ready datasets for teams building computer vision, robotics and language AI. Every project starts with a free pilot, runs on your guidelines and tools, and ships with multi-stage quality checks.
Our Services
Frequently Asked Questions
How does the free pilot work?
Send us a sample of your data and your guidelines. We annotate it at no cost, report accuracy and turnaround, and return a precise quote, usually within a few working days.
How do you ensure annotation quality?
Every batch passes annotator self-checks, peer review and a dedicated QA lead. We agree accuracy targets up front and share QA reports with each delivery.
Can you work in our annotation tool?
Yes. Our team works in your platform or ours and delivers in the formats your pipeline expects, such as COCO, YOLO, Pascal VOC or custom JSON.
How is my data kept secure?
All work is done by our in-house team under NDA, with role-based access and no data leaving approved environments. See our Data Privacy Notice for details.
How is pricing calculated?
Per object, per hour or per project, depending on the task. See Pricing for reference rates, or request a pilot for an exact quote.
Want to see the difference on your own data?
Request Free Pilot
