DataConsultant helps AI, product, data and risk teams design and develop instruction datasets that teach models the behaviours, formats and task patterns required for approved use cases. We turn target behaviours and source material into governed instruction-response examples with task taxonomies, authoring rules, expert review, quality controls, metadata and release documentation.
Model fine-tuning, deployment and production monitoring are separate activities unless explicitly included in the agreed statement of work.
The service is useful when model teams need deliberate examples of target behaviour rather than a larger volume of unstructured text.
A controlled data-development engagement that translates approved AI behaviours into a task taxonomy, example specification, authoring workflow, quality model and governed training dataset. The emphasis is on useful coverage, traceable decisions and repeatable production rather than raw example count alone.
Not automatically included: base-model selection, fine-tuning execution, GPU or API costs, model deployment, red-team testing, formal legal review, certification or production operations unless separately scoped.
Start with representative failure cases, target behaviours and current examples so the right intervention can be separated from prompt engineering, retrieval, model selection or evaluation work.
Scope is configured around the model, downstream task, risk profile, source rights, production distribution, domain complexity and the evidence needed for release.
Define the task families, intents, difficulty levels, user contexts, output types and edge cases the dataset must represent.
Translate target behaviour into precise rules that authors and reviewers can apply consistently.
Create or transform examples using appropriate subject-matter, language and workflow expertise.
Apply defined controls before examples are accepted into a release candidate dataset.
The operating flow keeps source decisions, authoring, review and release evidence connected so model teams can reproduce what entered the dataset and why.
Acceptance criteria are service-specific and agreed during design. Illustrative control dimensions below show the type of evidence the workflow can produce; they are not client performance claims.
Align task families, edge cases, languages, output formats, quality thresholds and protected evaluation boundaries before volume becomes the main production metric.
Instruction data should be tied to an approved model-development objective. Fine-tuning is not automatically the right solution when prompting, retrieval or workflow design can address the requirement more simply.
Examples that reflect domain-specific questions, terminology, document patterns, business rules and expected answer structure.
Teach models to produce defined schemas, fields, tags, classifications, extracted values or other machine-consumable outputs.
Represent approved tone, completeness, formatting, refusal, escalation and response-boundary expectations.
Develop supervised examples for approved tool-selection or structured function patterns where supported by the selected platform.
Increase controlled coverage of rare task variants, ambiguity, difficult inputs, exceptions and known failure modes.
Create language- and locale-aware examples with explicit reviewer qualifications, terminology and cultural context requirements.
Deliverables are selected according to the task, model workflow and client controls. The table distinguishes the purpose of each asset so buyers can scope what is actually needed.
| Deliverable | Decision or operational purpose | Typical content | Client input needed |
|---|---|---|---|
| Instruction data specification | Define what a valid example must contain | Roles, fields, task rules, response rules, exclusions, schema | Target behaviour and platform constraints |
| Task taxonomy & coverage matrix | Control distribution and edge-case coverage | Task families, difficulty, domains, languages, failure cases | Representative production demand and risk priorities |
| Authoring & review rubric | Standardise human judgement | Quality criteria, examples, anchors, escalation, adjudication | Domain and policy approval |
| Release candidate dataset | Provide model-ready training examples | Validated instruction-response records with agreed metadata | Approved sources and target schema |
| Quality report | Show evidence of acceptance controls | Checks, defect categories, reviewer agreement, exceptions, corrections | Acceptance thresholds and risk tolerance |
| Dataset card / release documentation | Support traceability and responsible reuse | Purpose, sources, version, schema, limitations, intended and excluded uses | Governance and ownership decisions |
| Handover & change-control pack | Support future maintenance | Versioning, change triggers, review process, responsibilities, open issues | Operating owner and maintenance model |
Instruction data quality depends on clear target behaviour, source permission and access to people who can resolve ambiguous examples.
Use a representative sample to validate task instructions, reviewer calibration, defect taxonomy, acceptance criteria and the real effort required per example.
Training examples can influence model behaviour and can carry privacy, security, intellectual-property and model-risk implications. Controls therefore extend from source selection through release and future reuse.
Define whether personal or sensitive information is permitted, minimised, redacted, transformed or excluded, and align processing with applicable legal and organisational requirements.
Control source access, authoring environments, reviewer permissions, exports, logs, storage and handover according to the agreed data classification.
Record where source material came from, why it is eligible for the intended use and which restrictions apply to transformation, training, retention or redistribution.
Review task and source distribution for material gaps, over-representation, excluded groups, language imbalance and other coverage risks relevant to the use case.
Use benchmark exclusions, duplicate screening, release boundaries and controlled access to reduce accidental reuse of protected evaluation material.
Version datasets and specifications, record material changes, define re-review triggers and keep a traceable relationship between examples and approval decisions.
DataConsultant does not publish an approved fixed fee for this service. Current public market offers use materially different definitions of “training data” — from small self-service preparation tasks to specialist enterprise instruction-tuning programmes — so a single inferred INR rate would not be a reliable like-for-like benchmark.
A commercial proposal is prepared after the task taxonomy, source readiness, example type, volume, domain expertise, languages, review depth, security controls, release format and acceptance criteria are clear. Timeline is confirmed after scoping for the same reason.
Where useful, the engagement can begin with a scoped pilot to establish production effort, reviewer alignment and defect patterns before a larger dataset build is approved. Any ongoing production model is agreed separately rather than assumed.
Request an Instruction Data QuoteA useful engagement starts by distinguishing behaviour that should be learned through tuning from information that should remain in prompts, retrieval systems, tools or controlled business logic.
Share the model goal, task examples, data constraints and review expectations. We can help define the dataset, quality gates, client responsibilities and commercial scope before production begins.
The value proposition is based on delivery discipline and transparent controls rather than unsupported claims about model accuracy or financial outcomes.
Examples are organised around the actual task, decision or workflow the model must support instead of generic prompt collections.
Source quality, provenance, privacy, security, evaluation boundaries and model-risk considerations are incorporated into the workflow.
The dataset is designed against requirements first and then packaged for the approved model or fine-tuning environment.
Documentation, versioning, limitations, quality evidence and change triggers help internal teams understand and evolve the dataset.
Answers to common enterprise questions about scope, fine-tuning data, quality, governance, pricing, timing and required client inputs.
Describe the target behaviour, current failure pattern and any data constraints. A useful first conversation focuses on the training objective and evidence needed to build a defensible dataset specification.
Required fields are marked with an asterisk.
