Custom Synthetic Data Creation

This task builds worlds from scratch—think “fake crash” crafted in a sim or “odd chat” spun in a script (e.g., “rare glitch” shaped, “mock sale” staged)—to train AI where real data hides. Our team forges these fakes, fueling models with safe, smart stand-ins.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are pivotal in orchestrating the creation and validation of data for Custom Synthetic Data Creation within specialized AI workflows.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to craft datasets that enhance AI’s ability to learn from simulated scenarios effectively.

Training and Onboarding

PMs design and implement training programs to ensure workers master scenario crafting, data simulation, and realism validation. For example, they might train teams to shape “virtual crowd flow” in a model or stage “fake outage” in a log, guided by client specs and AI standards. Onboarding includes hands-on tasks like generating synthetic records, feedback loops, and calibration sessions to align outputs with AI training goals. PMs also establish workflows, such as multi-step reviews for believability.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., crafting 15,000 synthetic records) and set metrics like scenario accuracy, realism fidelity, or data consistency. They track progress via dashboards, address simulation flaws, and refine methods based on worker insights or evolving client needs.

Collaboration with AI Teams

PMs connect creators with machine learning engineers, translating technical requirements (e.g., high variance for edge cases) into actionable data tasks. They also manage timelines, ensuring synthetic datasets align with AI training and deployment schedules.

We Manage the Tasks Performed by Workers

The creators, simulators, or data analysts perform the detailed work of crafting and validating synthetic datasets for AI training. Their efforts are imaginative and technical, requiring precision and domain awareness.

Labeling and Tagging

For synthetic data, we might tag events as “sim rush” or “mock fail.” In complex tasks, they label specifics like “hypothetical delay” or “virtual spike.”

Contextual Analysis

Our team shapes scenes, staging “rare storm” in a feed or crafting “odd user” in a trace, ensuring AI gets lifelike lessons.

Flagging Violations

Workers review datasets, flagging unreal quirks (e.g., “too perfect” as “flawed”) or off-base data (e.g., unlikely stats), maintaining dataset quality and reliability.

Edge Case Resolution

We tackle complex cases—like bizarre sims or privacy tweaks—often requiring deep tweaking or escalation to domain experts.

We can quickly adapt to and operate within our clients’ specialized platforms, such as proprietary sim tools or industry-specific systems, efficiently processing batches of data ranging from dozens to thousands of records per shift, depending on the complexity of the scenarios and annotations.

Data Volumes Needed to Improve AI

The volume of synthetic data required to enhance AI systems varies based on the diversity of scenarios and the model’s complexity. General benchmarks provide a framework, tailored to specific needs:

Baseline Training

A functional synthetic model might require 5,000–20,000 crafted records per category (e.g., 20,000 mock events). For varied or rare cases, this could rise to ensure coverage.

Iterative Refinement

To boost accuracy (e.g., from 85% to 95%), an additional 3,000–10,000 records per issue (e.g., weak sims) are often needed. For instance, refining a model might demand 5,000 new creations.

Scale for Robustness

Large-scale applications (e.g., multi-scenario models) require datasets in the hundreds of thousands to handle edge cases, unique fakes, or new domains. A creation effort might start with 100,000 records, expanding by 25,000 annually as systems scale.

Active Learning

Advanced systems use active learning, where AI flags tricky sims for further crafting. This reduces total volume but requires ongoing effort—perhaps 500–2,000 records weekly—to sustain quality.

The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and synthetic precision across datasets.

Multilingual & Multicultural Custom Synthetic Data Creation

We can assist you with custom synthetic data creation across diverse linguistic and cultural landscapes.

Our team is equipped to craft and analyze synthetic data for global contexts, ensuring accurate, contextually relevant datasets tailored to your specific AI objectives.

We work in the following languages: