From Human Wave to Data Factory: Inside 360's AI Annotation Platform TLP

The Underestimated Step: Annotation

Model practitioners know an iron law: the model's ceiling is set by data. With identical frameworks and compute, differing data quality yields vastly different models. Between raw data and trainable data lies the final step — annotation.

Traditional annotation relies on Excel task dispatch, WeChat file collection, manual spot-checks, and constant chasing. Tasks scatter, status is opaque, rework cycles repeat — annotation teams tire, algorithm teams tire more. Worse, results sit on personal laptops; reuse for the next training run means half a day just finding data.

360AI's AI Annotation TLP aims to straighten this out.

Project and Resource Groups: Organize First

Before annotation, answer: whose task, whose data, who can see? Many projects' chaos starts at organization, not annotation.

TLP's "Project Management" uses a two-level structure: Group → Resource Group . Each group has a clear Project Owner and Resource Group Admin , with transparent permissions — who creates tasks, who manages resources, who views data.

Annotation Workbench: Make Every Task's Progress Visible

The core "Data Annotation" page is restrained: a prominent "New Annotation Task" button top-left, below a task list showing five key columns — Task Type, Task Status, Annotation Progress (labeled count / total), Rejected Count, Creator .

From creation to in-progress to done, each task's stage and blocker are visible. For team leads it's a battle map; for algorithm engineers it's a progress bar — no more guessing when qualified data arrives.

The standalone "Rejected" column is the page's most masterful design: it turns QA feedback into an explicit process step. Quality issues are intercepted and corrected during the process, not discovered at delivery. Traditional "spot-check pass rate" is post-hoc; here reject-relabel forms a closed loop — quality is forced upward.

Not Just Humans: Models and Compute Too

Many still equate annotation with "human labeling," but modern platforms are more than labor-management tools. TLP's philosophy: "People + Models + Compute + Cost" — the platform handles all four.

People are the core asset — annotator throughput, quality, stability must be visible and managed.

Models are the efficiency lever — machine first pass, human correction and verification is now the industry standard workflow; pure human-wave tactics cannot support LLM-era data volumes in cost or speed.

Compute is the foundation — data preprocessing and model inference consume resources; unified scheduling means teams don't maintain their own clusters.

Cost is unavoidable — data is continuous investment; per-label cost directly determines project scale.

Orchestrating these four in one platform turns annotation from "human wave tactics" into an engineered data production line .

One Platform, Four Roles

Annotation is never a solo job. A task from dispatch to delivery involves several roles; TLP gives each a dedicated view:

Project owners see capacity and progress; annotators see their work orders; algorithm engineers see data readiness; admins see resource usage and permissions. The same task list shows capacity to the owner, work orders to annotators, schedule to algorithms, ledger to admins.

When all roles view the same real-time data, the "telephone game" cost of collaboration drops to near zero. This is the fundamental difference between a platformized tool and "passing folders around" — change the tool, the collaboration mode changes naturally.

The Real Killer Feature: Data Closed Loop

TLP isn't an island. On 360AI Platform it integrates tightly with neighbors:

Annotated data flows directly into TLM Data Management for training and evaluation. After model training and deployment, production badcases flow back as new annotation tasks.

Annotation → Training → Deployment → Feedback → Re-annotation — each model iteration thickens the data asset.

This is the essential difference between platformized annotation and one-off outsourcing: outsourcing ends at delivery; here annotation shifts from a one-off project to a compounding asset . Data grows thicker in your own hands; the model's moat is built inch by inch.