AI-ready data / Data Cleaning & Preparation

Data Cleaning & Preparation

Make input data more consistent before annotation, training or analysis.

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What the service does

From raw input to reviewable structure.

Make input data more consistent before annotation, training or analysis. Labels and instructions are calibrated with your team on a small pilot before larger batches begin.

Typical tasks

  • Deduplication
  • Schema normalization
  • Missing-field review
  • Format validation
Typical input

What you provide.

Raw records, required schema and transformation rules.

Typical output

What you receive.

Cleaned records with a change log and flagged exceptions.

Demo / Illustrative example

Make the output tangible.

This synthetic example shows the structure of a record. It is not a client dataset or a claim of project performance.

Labels with a clear purpose.

Deduplication is defined in the project guidelines. Ambiguous examples are flagged for review instead of silently forced into a category.

CSVJSONLParquet by agreement
{ "record_id": "demo-1", "duplicate": false, "status": "schema-valid" }
Human-in-the-loop quality

Quality is a process. Not a percentage on a page.

Acceptance thresholds, review methods and sampling are agreed for each project. Calibration happens before volume.

LEVEL 1

Annotator review

LEVEL 2

Peer review

LEVEL 3

Quality reviewer

LEVEL 4

Sample audit

LEVEL 5

Client feedback loop

We document uncertainty and disagreement, revise guidelines with your team and keep an audit trail of corrections. Specialist medical, legal or financial review is available subject to project requirements and qualified reviewer availability.

Use cases

A fit for your data workflow.

Data teamsAnnotation pipelines
Supported formats

Agree the schema first.

CSVJSONLParquet by agreement

Shared responsibilities.
Clear delivery options.

You retain responsibility for source rights, lawful access, required approvals and intended use. We agree secure transfer, retention and deletion arrangements in the project scope.

  • Client-approved guidelines and representative inputs
  • A project owner for edge-case decisions
  • An agreed acceptance rubric and sample audit
  • Pilot, batch or milestone-based delivery
  • Versioned exports and a documented handover
Project questions

Scope the work with confidence.

What do we need to provide?

Raw records, required schema and transformation rules. You also provide lawful access and usage rights, security requirements, acceptance criteria and a project owner who can resolve ambiguities.

How is annotation quality measured?

We agree a task-specific rubric, calibration pilot and sampling plan. Peer review, quality review and client feedback inform acceptance. No universal accuracy percentage is advertised.

Can you handle specialist subject matter?

Specialist medical, legal, financial or expert RLHF work is available subject to project requirements and qualified reviewer availability. Suitability is confirmed during scoping.

What delivery options are available?

Pilot batches, milestone-based deliveries or a scoped recurring workflow. Typical formats include CSV, JSONL, Parquet by agreement; exact schemas, tools, volumes and timelines are agreed before starting.

Your next stage starts here

Build a growth system
that works smarter.

Start with one business challenge.
We will map the smallest practical next step.