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Please note: Pricing is based on the average time required for these type of studies. Final cost may vary depending on scope, complexity, and specific project needs.
What it is
Data collection is a service designed to help ML and AI companies gather high-quality, human-centered data to power, fine-tune, or evaluate models. When synthetic data or automation falls short, we step in to identify real-world data requirements and set up effective collection efforts from defining the task to capturing data signals with precision.
When to Use It
Model development: When models need training, fine-tuning, or evaluation based on real-world human input
Early product prototyping: When behavioral data or inputs are needed to simulate edge cases or natural interactions
Post-launch tuning: When live data is sparse, biased, or missing key signals
Failures in automation: When heuristics or synthetic data can’t cover critical variation
Outcomes & Impact
Get model-ready data: Tailored collections that match your labeling, quality, and diversity criteria
De-risk AI launches: Validate assumptions, fill gaps, and uncover unexpected edge cases
Accelerate iteration and launch: Cut delays by running focused, rapid data pipelines
Increase model accuracy: Capture the “long tail” of real-world input for better generalization
Why It Matters
Your AI is only as good as the data it learns from. Training on weak or misaligned data is like building rockets with the wrong fuel. Strategic data collection ensures your models are grounded in the reality they’ll face. Invest in the right inputs, and the output takes care of itself.
What Data Collection Is Not
It’s not analysis. Gathering data is the first step and making sense of it comes later in partnership with your science team(s).
It’s not one-size-fits-all. Good data collection is intentional and tailored, not just dropping in a survey or turning on tracking.
It’s not passive. It requires thoughtful scoping and design to avoid bias, ensure quality, and respect users’ privacy.
What to Expect
Kickoff alignment (30–60 min): Define model needs, use cases, and success criteria
Design & setup (2–5 days): Finalize participant tasks, collection protocols, logistics, and tooling
Execution window (varies by scope): Manage recruitment, run collection sessions, ensure data integrity
Handoff & debrief (varies by scope): Share data package, top takeaways, and operational recommendations
Typical timeline: 1–3 weeks from kickoff to usable data. *May vary depending on data collection requirements
Deliverables: Data collection plan, task instructions, participant materials, QA checklists, collected datasets (raw and cleaned if it isn’t already pipped to your company’s backend), and a brief insight summary if applicable