Managed Data
Annotation for
High-Growth Startups
We help ambitious AI startups scale training data without building in-house annotation operations. Speed, quality, and flexibility designed for rapid iteration.
What We Do
We provide end-to-end managed annotation services, designed for machine learning teams that care about quality, consistency, and delivery timelines.
- Bounding boxes
- Polygons
- Semantic segmentation
- Frame labeling
- Object tracking
- Event classification
- Classification
- Named Entity Recognition (NER)
- Sentiment analysis
You focus on modeling. We handle the labeling.
We also handle guideline design, task setup, and ongoing QA reporting.
Who We Work With
We specialize in serving seed to Series B startups that need to move fast. We understand the unique constraints of high-growth teams: tight deadlines, evolving schemas, and the need for pixel-perfect quality.
Typical Clients
- • Computer vision startups
- • Robotics and autonomous systems teams
- • Retail and industrial AI companies
- • Y Combinator & Techstars Alumni
How We’re Different
Most annotation vendors sell labor. We deliver managed outcomes.
Dedicated Project Manager
For every engagement, ensuring clear communication.
Defined Guidelines
Clearly defined annotation guidelines and acceptance criteria.
Multi-pass QC
Quality control with measurable accuracy metrics.
Flexible Scaling
From small pilots to large production volumes.
Predictable Delivery
Through SLAs, not crowdsourcing.
One Accountable Partner
You get a partner, not a platform to manage.
Quality Assurance
Quality is built into our workflow, not added at the end. If quality drops, we catch it early — before it affects your model.
Data Security & Confidentiality
We understand that training data is sensitive. We are happy to align with your internal security requirements.
NDAs
Standard for all engagements
Controlled Access
Strict data access controls
No Retention
No reuse beyond project scope
Secure Transfer
Encrypted storage procedures
How Pricing Works
Pricing depends on data modality, complexity, volume, and SLA requirements. All projects start with a paid pilot.
How to Get Started
- 1
Share Overview
Brief overview of dataset and use case
- 2
Pilot Plan
We design a pilot scope and annotation plan
- 3
Delivery & Scale
Pilot delivery with QA metrics, then scale