Schema & normalization
Map records to agreed fields, naming conventions, formats, and validation rules.
Curation & preparation
Turn sourced information into usable examples.
Raw material is rarely ready for an AI pipeline. We help define the transformations, validation steps, and handoff requirements that make collected data useful.
01 / Operational readiness
Preparation without losing the signal.
Every step should preserve the features that make the underlying example relevant to the model objective.
Deduplication & filtering
Identify redundant, off-spec, corrupted, or low-signal examples before delivery.
Annotation & enrichment
Scope labels, context fields, outcome markers, and human review where needed.
Privacy-focused handling
Assess minimization, redaction, de-identification, and necessary restrictions per engagement.
Sampling & QA
Document acceptance tests, review representative samples, and track reasons for rejection.
Delivery packaging
Provide agreed schema, metadata, manifests, field definitions, and batch documentation.
02 / What a delivery spec can include
A useful handoff is more than a folder of files.
We help buyers articulate what “ready” means for their training or evaluation process. That allows data providers and processing specialists to aim at concrete acceptance criteria.
Data unitConversation, task, file, clip, document, session, or trajectory.
SchemaExpected fields, types, formats, references, and relationships.
QA targetsRequired completeness, consistency, agreement, and exception handling.
Rights metadataPermitted uses, source reference, limitations, and agreed retention rules.
DeliveryBatch cadence, secure transfer method, documentation, and acceptance process.
Start a conversation
Discuss a data needKnow the dataset. Define the acceptance bar.
We can discuss a pilot that tests source quality, transformation effort, and fit before committing to broader volume.