Pricing models.
Engineered for scale.
Flexible structures designed precisely for how your AI team operates. Scale dynamically from early prototype to enterprise throughput without hidden fees.
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Designed for your delivery model
Whether you need turnkey project delivery, flexible elastic labeling, or dedicated pods of specialists, we offer transparent billing models.
Project-based
Fixed-Scope Predictability
Ideal for well-defined annotation campaigns, model benchmarks, and pilot validation with guaranteed SLAs.
- Fixed-cost project scoping & clear SLAs
- Multi-stage QA pass & edge-case triage
- Dedicated project delivery manager
- COCO, YOLO, VOC, CSV, JSON export formats
- Turnaround in 3–5 business days
Volume-based
Dynamic Bandwidth Scaling
Designed for continuous ML pipelines requiring flexible high-throughput capacity and automated batch ingestion.
- Progressive tiered per-unit discounts
- Priority API & direct S3/GCS bucket ingest
- Guaranteed monthly throughput capacity
- Real-time drift & accuracy tracking metrics
- Flexible rollover of unused monthly volume
Custom Teams
Dedicated Annotation Pods
Exclusive, hand-picked subject matter experts trained strictly on your proprietary guidelines and infrastructure.
- 100% dedicated annotators & QA reviewers
- Domain experts (Medical, Geospatial, Legal, PhD)
- Direct Slack, Teams & daily standup sync
- Custom on-prem, VPN, or VPC deployment
- SOC2 Type II, HIPAA, and GDPR certified
LLM-based
Generative AI & RLHF
Human-in-the-loop alignment, RLHF preference scoring, red-teaming, and synthetic dataset refinement.
- Pairwise response ranking & reward modeling
- Multi-turn dialogue curation & roleplay
- Adversarial red-teaming & prompt injection tests
- Hallucination detection & citation verification
- Bilingual & domain-specific expert evaluators
Why AI Teams Choose Engai
Built For Enterprise MLPrecision Annotation for High-Entropy Data
Deliver gold-standard ground truth across multidimensional sensors, dense taxonomies, and non-trivial edge cases that commodity platforms miss.
Algorithmic & Human-in-the-Loop QA Protocols
Quality is baked into every batch with multi-tier consensus scoring, automated validation benchmarks, and rapid feedback calibration.
Elastic Capacity & Dynamic Pipeline Scaling
Instantly scale annotation throughput up or down to align with sprint cycles and model iterations, backed by transparent unit economics.
Native Integration with Your MLOps Ecosystem
Direct synchronization with private storage (S3, GCS, VPC), customized API endpoints, and real-time collaboration via your engineering channels.
Dedicated Specialists & Domain Lead Annotators
Direct access to vetted subject-matter annotators trained specifically on your proprietary guidelines and strict enterprise compliance frameworks.
Estimate Your Costs for ML projects
Configure your data modality, choose the target annotation method, and estimate your approximate monthly or project budget.
1 Pick application field
2 Select annotation method
3 Specify how many objects to label
4 Approximate total estimate
*Unit rate: $0.015 per objects. High-volume discounts apply for large batch orders.
5 Validate with a calibrated pilot
Frequently Asked Pricing Questions
Do you offer free proof-of-concept (POC) pilots?
Yes. For serious ML production pipelines, we offer a calibrated pilot on a sample subset of your data (typically 50–500 images, hours of audio, or sample documents) to validate guidelines and guarantee quality acceptance criteria before full rollout.
How does volume-based discounting work?
As your monthly labeling volume increases, your unit rate drops automatically across pre-agreed tiers. Volume discounting scales up to 40% for large recurring datasets with predictable pipeline cadence.
Can we switch between Project-based and Dedicated Custom Teams?
Absolutely. Many teams start with a project-based pilot to test labeling speed and accuracy, and transition to a dedicated custom team pod when continuous model re-training and active learning workflows begin.
How do you handle ambiguous cases and quality disputes?
Every project includes a defined acceptance threshold (e.g. 99% accuracy or IoU > 0.85). Any asset flagged as ambiguous is reviewed by senior QA leads and discussed during periodic guideline calibration sessions.
Start Free Piloting your ML project !
fill up this form to send your pilot request
Discover how Engai's Data Pipeline and AI Infrastructure platform can make your organization AI-ready:
Automatically discover and map all datasets with contextualized inventory
Effortlessly manage ML lifecycle and address governance gaps
Drastically reduce deployment time by mitigating edge-case risks
Immediately detect and respond to model drift to minimize threat impact
Proactively apply zero-trust protection mechanisms for proprietary data
Live Infrastructure Telemetry
Zero-Trust Dataset Confidentiality
Your requirements and proprietary sample assets are encrypted in transit and never used for public model training.