Our Process
From Brief to Dataset in 5 Days
You describe what you need. We build the team, train them on your requirements, deploy, complete QA, and deliver a verified dataset all within one business week.
The 8-Step Pipeline
Every project follows the same structured process to ensure consistent quality, on-time delivery, and full transparency.
- Step 1
Brief Submitted
Day 0
Share your data requirements — whether you need data collected, generated, annotated, or all three.
- Define data collection & annotation requirements
- Set accuracy targets and SLA expectations
- Specify data types (audio, video, image, text, 3D)
- Agree on delivery formats (JSON, CSV, custom schemas)
- Step 2
Pod Formed
Day 1
A dedicated team is assembled and aligned to your domain and project scope.
- Domain-matched annotators and collectors assigned
- Team lead and QA reviewers onboarded
- Secure workspace and tooling provisioned
- Communication channels established
- Step 3
Team Training & Calibration
Day 1-2
Teams undergo task-specific training aligned with your guidelines and quality benchmarks.
- Interactive training on your annotation guidelines
- Calibration using sample datasets
- Inter-annotator agreement benchmarking
- Edge case alignment and guideline refinement
- Step 4
Data Acquisition & Capture
Day 2 – 3
We source and capture high-quality raw data across controlled and real-world environments.
- Controlled single/multi-speaker recording setups
- Environment variation (indoor, outdoor, noise conditions)
- Device diversity (mobile, studio, field recorders)
- Real-world scenario simulation
- Step 5
Data Preparation & Curation
Day 3
Raw data is processed and standardized to ensure it is ready for annotation and model training.
- Audio trimming and segmentation
- Noise filtering and normalization
- Silence and artifact removal
- Metadata structuring and tagging
- Step 6
Annotation & Labeling
Day 3 – 4
Structured annotation workflows are applied with real-time monitoring and progress tracking.
- Parallel annotation across distributed teams
- Real-time progress dashboards
- Daily completion reporting
- Edge case escalation and handling
- Step 7
QA Review
Day 4
Automated and human validation ensures accuracy and consistency
- Automated validation checks
- Human review and sampling audits
- Edge case deep-dive validation
- Final accuracy benchmarking
- Step 8
Analytics & Delivery
Day 5
Final dataset delivered with insights and documentation
- Quality metrics and reports
- Structured dataset delivery
- Feedback loop for improvements
- Final accuracy benchmarking
Why Teams Choose DeepAnnotate
We’re not a crowd platform. We’re a managed annotation partner built for teams that can’t afford bad data.
5-Day Turnaround
From brief to delivery in under a week. No months-long onboarding.
95%+ Guaranteed Accuracy
Multi-layer QA with automated + human review on every project.
Dedicated Pods
Your own trained team not anonymous crowd workers picking up random tasks.
Direct Communication
Talk to your pod lead directly via Slack or email. No ticket queues.
Scale On Demand
Your own trained team not anonymous crowd workers picking up random tasks.
Enterprise Security
Your own trained team — not anonymous crowd workers picking up random tasks.
Our SLA at a Glance
95%+
Guaranteed Accuracy
5 Days
Pilot Turnaround
24hrs
Response Time
100%
Free Re-annotation
Frequently Asked Questions
Everything you need to know about working with DeepAnnotate AI.
What is the minimum dataset size you support?
We support projects ranging from small pilot datasets to large-scale production pipelines. Most engagements begin with a pilot phase and scale based on requirements.
How do you handle complex or edge-case scenarios in data?
Edge cases are identified early through sampling and continuously monitored during production. We apply custom annotation guidelines and multi-level validation to ensure consistency and accuracy.
What data formats do you deliver?
We deliver structured outputs in flexible formats such as JSON, CSV, or custom schemas, along with aligned media files (audio, text, or metadata).
Can the pipeline scale for large production workloads?
Yes, our infrastructure is designed to scale seamlessly from pilot to high-volume production, with consistent quality and turnaround times.
How is data quality measured and maintained?
Quality is tracked using defined metrics, automated validation checks, and human review layers, ensuring high accuracy across all deliverables.
What happens if the delivered data does not meet expectations?
We perform targeted re-evaluation and correction cycles based on feedback, ensuring the final dataset aligns with agreed quality benchmarks.
Do you support real-time or streaming data workflows?
Yes, we support near real-time and streaming data pipelines depending on project requirements, including continuous ingestion and annotation workflows.
How do you ensure data security and compliance?
We follow strict data governance practices, including secure storage, controlled access, and compliance with global privacy standards.
Can annotation guidelines be customized for specific use cases?
Yes, all annotation workflows are fully customizable based on model requirements, domain specificity, and desired output formats.
What types of AI use cases do you support?
Our datasets support a wide range of applications including physical AI,speech AI, conversational systems, audio intelligence, and large language model training.
Get a Custom Quote
Tell us your data type and volume. We’ll send a detailed proposal within 24 hours.