Methodology
Our methodology is not a software stack. It is a set of principles and processes that ensure data can be designed, generated, and maintained for long-term AI development.
We do not collect data and then determine how to use it. We design the data architecture before any recording begins.
This means defining:
Target System: What AI will consume this data
Task Requirements: What the AI must be able to do
Condition Space: What variations must be covered
Failure Modes: What must not happen
Future Use: Retraining, expansion, derivatives
Data without design is data without future.
Every dataset we create can be regenerated under the same conditions.
This requires:
Complete condition documentation
Session design templates
Speaker/subject attribution
Environment and equipment logging
Metadata that enables reconstruction
Reproducibility enables retraining, incremental expansion, comparative evaluation, and long-term maintenance.
Division of labor breaks the chain. We execute the entire process as a single organization.
Requirements Analysis
Data Architecture Design
Resource Mobilization (speakers, environments, equipment)
Recording / Acquisition
Annotation & Quality Control
Rights & Compliance Management
Documentation & Delivery
No handoff points means no information loss.
Quality is not post-hoc verification. It is built into the process.
Label Schema Design: Clear definitions before annotation
Inter-Annotator Agreement: Measured and reported
Boundary Tolerance: Task-appropriate thresholds
Label Revision: Low-agreement labels are redefined, not ignored
QC is not a filter. It is part of design.