AI-ready data / Supervised Fine-Tuning Data

Supervised Fine-Tuning Data

Prepare consistent instruction-response examples for an agreed target behavior.

What the service does

From raw input to reviewable structure.

Prepare consistent instruction-response examples for an agreed target behavior. Labels and instructions are calibrated with your team on a small pilot before larger batches begin.

Typical tasks

Instruction-response preparation
Conversation formatting
Duplicate review
Split integrity checks

Typical input

What you provide.

Approved examples, task specification and target format.

Typical output

What you receive.

Reviewed examples with format checks and dataset split metadata.

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.

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

JSONLJSON

{ "messages": [{ "role": "user", "content": "Say hello" }, { "role": "assistant", "content": "Hello" }] }

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.

Task-specific assistantsModel adaptation

Supported formats

Agree the schema first.

JSONLJSON

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?

Approved examples, task specification and target format. 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 JSONL, JSON; 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.

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