Image annotation
Object tagging, bounding boxes, and scenario-based training for vision models.
FIELD NOTE 01 · JAKARTA → WORLD
Language, annotated into stable form
A Jakarta-based micro team delivering annotation-intensive AI data work, reviewed against your guidelines before anything ships, with measured, verifiable quality.
PASS RATE 99.1–99.3%
FIELD NOTE 02 · THE FORMATION
A focused team for detail-critical AI data work
Aethen AI is an Indonesia-based team providing AI data services with a focus on language-driven and annotation-intensive workflows. Rules are read, applied, and checked; the output settles into stable, verifiable data.
Guidelines are grammar: task specs and guidelines reviewed before work begins, output checked against them before submission.
A micro team, not a crowd: hands-on execution, accountable handling, no subcontracted batches.
Contained by default: happy to sign an NDA to protect your data from day one.
- 20+
- Contributors
- Jakarta
- Indonesia
- NDA
- Data-safe & ready
FIELD NOTE 03 · CORE SERVICES
Core Services
End-to-end AI data services across image, video, audio, and LLM workflows, with QA built in.
Video & frame annotation
Frame-based annotation and video-related tasks where applicable.
Audio transcription
Transcription and audio-related tasks, including script-based recording projects.
LLM services
i18n support, RLHF-related annotation and evaluation, and prompt-level quality checks.
Safety red teaming
Red teaming and content evaluation to harden model behavior against misuse.
Quality Assurance
QA handling, feedback incorporation, and rework cycles to protect data quality.
FIELD NOTE 04 · APPLICATIONS
Industry Focus & Use Cases
After training AI models, we aim for results that apply across the following industries.
Language & Speech
Dataset support for Indonesian and English languages, including speech and text workflows.
Multimodal AI & Moderation
Safety and trust workflows, including content moderation dataset support.
FIELD NOTE 05 · WORKFLOW & QA
Workflow & Quality Assurance
A simple, QA-led workflow that keeps delivery timely without sacrificing quality.
- 01
Task and guideline review
The rules are read before anything moves.
- 02
Annotation or data processing
The work is executed per guidelines.
- 03
Internal Quality Assurance
Output is checked against the guidelines before submission.
- 04
Feedback & rework handling
Client feedback is folded in through structured rework cycles.
- 05
Final delivery & version tracking
Stable output is delivered with clear version tracking.
We are comfortable adapting to updated guidelines and iterative feedback loops as requested by clients or partners.
FIELD NOTE 06 · FIELD TOOLING
Tools & Platforms
As a micro team, we stay flexible on how to fulfill client requests and adapt to the tools you already use.
Spreadsheet-based tracking and QA
Internal documentation and communication
Client-provided annotation platform documentation
FIELD NOTE 07 · ENGAGEMENT
Engagement Model
- Pilot or trial-based onboarding: prove quality on a small scope first.
- Phase-based or batch delivery: aligned with your project milestones.
- Flexible workload: matched to project requirements and availability.
FIELD NOTE 08 · OUR WORKS
Our Works
Indonesian Script Recording Project
Short-sentence recording review based on client guidelines for an Indonesian-language audio dataset.
2,225Recordings completed
99.1–99.3%Passing rate
At a glance
- Guidelines · client and internal QA guidelines the work was checked against
- Tracking & response data · batch review records with per-item status
- NDA & contract · agreement on file from day one
FIELD NOTE 09 · START A PILOT
contact@aethenai.com
Send us your spec
Tell us the rules and the scope, and we'll come back with a measured plan. Don't miss our updates, keep in touch.