AI Data & Annotation · Built around a defined population

AI Dataset Collection

Population claims hold only if the target population, critical slices, source rights, consent evidence, sampling rules, rejections, and lineage remain visible from the collection specification through release.

A usable training set starts with the population it has to represent and the sources it may draw from. We collect against that definition and keep each record tied to its source, proposed usage basis, consent evidence, collection rule, and rejection history.

Collection closes with a sampling specification, source-rights record, lineage-preserving protocol, and slice scorecard showing the population gaps and restrictions accepted for this version.

Plan a dataset collectionExplore AI data services
Illustration of AI Dataset Collection: a team preparing and validating a dataset for AI use

Some of the 500+ brands we've worked with

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  • Yves Rocher
  • Yemeksepeti
  • Sporx
  • Silverline
  • Halk Yatırım
  • Jack Martin Menswear
  • Odamax

Steps, gates, and who decides

How we work

Collection starts from a population and sampling specification. The protocol then decides which records enter, which are rejected, and what evidence stays attached to both outcomes.

  1. Frame the dataset

    We write down the intended use, target population, important slices, source options, sampling limits, security boundaries, and the evidence required for acceptance.

    AI assist
    An initial sampling specification is drafted from the stated purpose and population.
    Human gate
    You confirm that the specification reflects the dataset you actually need. You confirm the target population, slices, and acceptance targets are correct.

  2. Review sources and permissions

    For each source, we record provenance, the proposed usage basis, applicable consent evidence, access conditions, and any records that must be excluded or isolated.

    AI assist
    Compiles provenance and usage-basis notes for each candidate source for review.
    Human gate
    Only sources with a reviewable basis move into collection. Your data owner approves which sources have a reviewable rights basis.

  3. Collect without losing lineage

    We run the protocol, preserve source links, deduplicate records, flag defects, and watch for source drift. Rejections and transformations stay in the version history.

    AI assist
    Flags likely duplicates and source drift while collection is running.
    Human gate
    A sampled record must trace back to its source and collection rule. Your data owner decides which flagged defects require exclusion.

  4. Challenge coverage and accept

    We compare target-slice coverage, rights and consent evidence, lineage completeness, and rejected sample rate with the agreed targets before preparing the release.

    AI assist
    Compiles the coverage and acceptance report from the collected evidence.
    Human gate
    You decide which documented gaps are acceptable for this version. You decide which documented coverage gaps are acceptable for this version.

Named artifacts you keep

What you get

The dataset arrives with its collection history. Your reviewer can see the intended population, source decisions, rejected records, weak slices, and unresolved restrictions.

  • Dataset

    Dataset and sampling specification

    A reviewable definition of the purpose, population, critical slices, source options, sampling constraints, and acceptance targets.

  • Policy

    Candidate-source rights and consent register

    A source-level record of provenance, proposed usage basis, consent evidence, access conditions, and unresolved restrictions.

  • Playbook

    Collection protocol and provenance record

    The collection steps, filters, deduplication rules, lineage events, rejections, transformations, and version history used to build the dataset.

  • Report

    Slice coverage and source-acceptance scorecard

    A slice-level view of coverage, defects, rights evidence, lineage completeness, rejected samples, and the open gaps considered at acceptance.

Scope and honest limits

When to bring us in

This is the right job when the dataset must stand for a named population and someone will later ask, record by record, where the material came from and why it was admitted.

A good fit when

  • Your intended model, evaluation, or research use is clear, but nobody has translated that purpose into a population and sampling specification.
  • Critical populations and slices matter, yet coverage follows whichever sources are easiest to open rather than the sampling constraints you agreed.
  • Rights, consent, security restrictions and rejected samples are recorded during collection, but the history disappears before review.
  • The intended use has been stated, but the target population, critical slices, sampling constraints, and acceptance targets still conflict.
  • Candidate sources are available, though nobody has weighed their usage rights, consent, provenance, and security conditions in a single pass.
  • Your records are being collected, but deduplication, defect controls, coverage checks, acceptance sampling, and lineage are not applied consistently.
  • A dataset version is ready to hand over, yet its gaps, exclusions, rejected samples, and review owner are missing from the release record.

Better handled as other work when

  • You need a legal conclusion that a source or proposed use is permitted. The rights file preserves the evidence, while your qualified authority makes that judgment.
  • You need one collected version to represent every population, condition, or later use. Its acceptance applies only to the stated purpose and sampling design.
  • You need data purchased or third-party access secured as part of collection. We can record candidate sources, but acquisition requires a separate agreement.

If one of these is closer to your situation, start here instead: Explore AI data services

Engineers who ship production AI

This is the part of Zeo that writes and ships code. Our senior engineers build agents, chatbots, and RAG pipelines, along with the automation and data work around them, and they keep operating those systems once they're live. We've worked with more than 500 brands since 2011.

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Tools we use

Tools behind this work

  • DVC

    the version history the acceptance review compares this collection pass against

  • Scale AI

    the managed collection operation keeping consent evidence tied to each record

  • Label Studio

    the collection log tying source, decision, and rejection reason together as one record

  • Snorkel AI

    the programmatic rule layer the coverage-acceptance check runs against for systematic bias

  • Tonic

    the synthetic supplement filling a population gap real sourcing can't reach

Next step

Define the population before opening the sources

Share the intended use, the population that matters, and the sources under consideration. We'll turn them into a collection specification your owners can review before records enter the dataset.
Plan the work

Before you decide

Questions we hear a lot

Share the intended use, target population and priority slices, possible sources, known rights and consent requirements, security boundaries, sampling constraints, and acceptance targets. We also need access to the people who can review the sources and decide whether a documented coverage gap is acceptable.