Synthetic Data Generation

This task crafts lifelike datasets from scratch—think fabricated patient records or mock financial transactions (e.g., “simulated MRI scan” or “synthetic trade log”)—tailored to mimic real-world patterns. Our team generates these controlled, diverse data points, enabling AI to train robustly and ethically without relying on restricted or scarce originals.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are essential in orchestrating the creation and validation of data for Synthetic Data Generation within AI training workflows.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to produce synthetic datasets that meet precise AI needs while upholding realism and ethics.

Training and Onboarding

PMs design and implement training programs to ensure workers master data synthesis techniques, realism standards, and domain-specific requirements. For instance, they might train teams to generate synthetic medical histories with plausible vitals or mock customer interactions, guided by real data samples and generation tools. Onboarding includes hands-on tasks like crafting test datasets, feedback loops, and calibration sessions to align outputs with AI objectives. PMs also establish workflows, such as realism checks for complex synthetic scenarios.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., generating 20,000 synthetic records) and set metrics like data fidelity, diversity, or compliance with privacy standards. They track progress via dashboards, address generation flaws, and refine methods based on worker insights or evolving project goals.

Collaboration with AI Teams

PMs connect data generators with machine learning engineers, translating technical specifications (e.g., statistical distributions for model training) into actionable synthesis plans. They also manage timelines, ensuring synthetic datasets align with AI development and deployment schedules.

We Manage the Tasks Performed by Workers

The generators, curators, or synthesizers perform the detailed work of creating and refining synthetic datasets for AI training. Their efforts are creative and technical, requiring domain knowledge and precision.

Labeling and Tagging

For synthetic data, we might tag entries as “generated patient age” or “mock transaction.” In validation tasks, they label data like “realistic outlier” or “synthetic baseline.”

Contextual Analysis

Our team designs data, crafting “simulated ECG readings” with accurate anomalies or “fake purchase histories” with seasonal trends, ensuring AI learns from credible, varied inputs.

Flagging Violations

Workers review synthetic datasets, flagging unrealistic patterns (e.g., improbable values) or gaps in diversity (e.g., uniform demographics), maintaining quality and utility.

Edge Case Resolution

We address complex cases—like rare disease simulations or niche financial scenarios—often requiring advanced generation tools or escalation to domain experts.

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

Data Volumes Needed to Improve AI

The volume of synthetic data required to train and enhance AI systems depends on the complexity of the target domain and the scarcity of real data. General benchmarks provide a framework, tailored to specific needs:

Baseline Training

A functional model might require 10,000–50,000 synthetic samples per category (e.g., 50,000 generated patient profiles). For highly sensitive or sparse domains, this could increase to ensure coverage.

Iterative Refinement

To boost accuracy (e.g., from 85% to 95%), an additional 5,000–15,000 samples per issue (e.g., unrealistic patterns) are often needed. For instance, refining a model might demand 10,000 new synthetic records.

Scale for Robustness

Large-scale applications (e.g., enterprise AI) require datasets in the hundreds of thousands to handle edge cases, variability, or rare scenarios. A synthesis 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 synthetic data gaps for further generation. This reduces total volume but requires ongoing effort—perhaps 1,000–5,000 samples weekly—to sustain quality.

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

Multilingual & Multicultural Synthetic Data Generation

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

Our team is equipped to create and refine synthetic data reflecting global contexts, ensuring realistic, culturally relevant datasets tailored to your specific AI objectives.

We work in the following languages: