Custom Data Collection

This task zeroes in on the meticulous process of curating bespoke datasets, pulling from diverse sources like niche forums, proprietary systems, or real-time interactions (e.g., capturing technical jargon or user habits). Our collectors work to deliver data that’s not just varied but precisely aligned with your AI’s unique needs, driving superior performance and targeted insights in even the most specialized fields.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are instrumental in orchestrating the acquisition and refinement of data for Custom Data Collection within AI training workflows.

We handle strategic oversight, team coordination, and quality assurance, with a significant emphasis on training and onboarding workers to source and curate datasets that align perfectly with client-specific AI objectives.

Training and Onboarding

PMs design and implement training programs to ensure data collectors understand target domains, sourcing techniques, and project requirements. For instance, collectors might be trained to extract technical jargon from engineering forums or capture user behaviors from IoT device logs, guided by examples and client briefs. Onboarding includes practical tasks like gathering sample datasets, feedback loops, and alignment sessions to refine collection strategies with AI goals. PMs also establish workflows, such as multi-source validation for diverse or hard-to-access data.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., collecting 15,000 industry-specific records) and set metrics like relevance, diversity, or data completeness. They track progress through dashboards, resolve sourcing challenges, and adjust methods based on worker insights or shifting client priorities.

Collaboration with AI Teams

PMs connect data collectors with machine learning engineers, translating technical specifications (e.g., dataset size for model training) into actionable collection plans. They also manage timelines, ensuring custom data is delivered in sync with AI development cycles.

We Manage the Tasks Performed by Workers

The collectors, curators, or researchers perform the hands-on work of gathering and refining custom datasets. Their efforts are targeted and resourceful, requiring adaptability and domain awareness.

Common tasks include:

Labeling and Tagging

For custom datasets, we might tag collected entries as “medical terminology” or “user click pattern.” In user behavior projects, they label actions like “purchase intent” or “abandoned cart.”

Contextual Analysis

Our team evaluates sourced data, tagging a manufacturing log with “machine downtime” or a social media thread with “consumer sentiment,” ensuring alignment with AI needs.

Flagging Violations

Workers review collected data, flagging irrelevant entries (e.g., off-topic posts) or incomplete records (e.g., missing timestamps), maintaining dataset quality and usability.

Edge Case Resolution

We tackle complex sourcing challenges—like obscure industry data or privacy-sensitive records—often requiring creative solutions or escalation to domain specialists.

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

Data Volumes Needed to Improve AI

The volume of custom-collected data required to train and enhance AI systems varies widely, depending on the specificity and complexity of the target application.

General benchmarks provide a framework, tailored to unique needs:

Baseline Training

A functional model might require 5,000–20,000 custom-collected samples per category (e.g., 20,000 user interactions for a retail app). For highly niche datasets, this could rise to 50,000 to ensure depth.

Iterative Refinement

To boost performance (e.g., improving accuracy from 80% to 95%), an additional 3,000–15,000 samples per focus area (e.g., rare user behaviors) are often needed. For instance, refining jargon recognition might demand 10,000 new entries.

Scale for Robustness

Large-scale or specialized applications (e.g., global industry AI) require datasets in the hundreds of thousands to capture edge cases, regional variations, or unique scenarios. A custom model might start with 100,000 records, expanding by 25,000 annually as needs evolve.

Active Learning

Advanced systems leverage active learning, where AI identifies gaps for further collection. This reduces total volume but requires ongoing effort—perhaps 500–2,000 samples weekly—to sustain improvement.

The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure relevance and variety in every dataset.

Multilingual & Multicultural Custom Data Collection

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

Our team is equipped to source and refine data from global or niche channels, ensuring tailored, culturally relevant datasets that meet your specific AI objectives.

We work in the following languages: