Inputs
Images, video, documents, available sensor data, model predictions, calibration files, existing labels, failure slices, and target schema.
← All data programs
Vision data that survives training.
Turn images, long video, documents, and aligned sensor media into versioned labels that handle occlusion, identity, time, and hard cases before your researchers inherit them.
- media
- sequence_041 / frames 188 to 236
- object
- track_007 / buyer class
- state
- occluded / identity retained
- event
- enter_zone / reviewed boundary
- alignment
- caption + frame interval
- export
- loader check passed
A program defined by the model job.
Flinket translates the model decision into an ontology, edge-case guide, production workflow, and export that the buyer can inspect.
Managed work
Classification, geometry, segmentation, tracking, temporal events, document structure, captioning, cross-modal alignment, and scoped 3D labels.
Outputs
Versioned labels, stable tracks, intervals, captions, media manifests, ontology records, edge-case inventory, rejections, and agreed splits.
Quality
Gold-set calibration, geometry and schema checks, identity review, boundary review, domain escalation, adjudication, and acceptance by error class.
Delivery
Native-tool project or buyer schema, plus a loader-tested sample, QA report, issue log, and versioned final handoff.
Labels organized around perception.
Annotation techniques are selected for the capability being trained or measured. The page does not treat every polygon, track, or caption as the same job.
Locate and describe objects
Classification, boxes, polygons, keypoints, semantic, instance, or panoptic segmentation with buyer-defined attributes and class rules.
Preserve identity through time
Object tracks, states, relationships, temporal events, and action intervals with explicit decisions for occlusion, truncation, and re-entry.
Align modalities
Image or video captions, question-answer pairs, audio or text alignment, sensor synchronization review, and document reading order.
Find what the model misses
Model-assisted pre-labeling, human correction, hard-case mining, error categorization, and held-out evaluation curation.
Lock the ontology before scale.
The five-stage workflow makes label decisions and delivery evidence visible before production expands.
- 1.0
Define the decision
Name the model job, source rights, ontology, tool, export, and acceptance method.
Output: program specification - 2.0
Inspect the data
Review easy, hard, ambiguous, rare, and broken examples with existing model failures.
Output: scene and error map - 3.0
Calibrate the guide
Annotate a representative batch, adjudicate boundary cases, and version the ontology before scaling.
Output: accepted sample and edge-case guide - 4.0
Produce with controls
Run automated validation, review low-confidence work, and monitor acceptance by class, scene, and error category.
Output: reviewed production labels - 5.0
Validate the handoff
Load a representative export in the buyer's parser or tool, then deliver the dataset and its review trail.
Output: labels, QA report, and manifest
Review the errors that change training.
A valid file can still contain broken identity, geometry, timing, or modality alignment. The program reports those failures directly.
- Class, attribute, geometry, timestamp, track-continuity, and file-reference validation.
- Written decisions for occlusion, truncation, small objects, uncertain boundaries, and class overlap.
- Cross-modal and sensor-timestamp checks before annotation and before export.
- Qualified review for domain-sensitive scenes and adjudication for disputed cases.
- Acceptance reported by class, scene, reviewer, and error category where the workflow supports it.
Representative sample
A track that explains what changed.
This example shows delivery structure only. The buyer's ontology, media, tool, and acceptance rule determine the real fields.
track_idtrack_007stable within sequence
frame_range188 to 236entry and exit reviewed
visibilitypartial occlusionidentity retained by guide
evententer_zonetemporal boundary reviewed
decisionacceptedschema + continuity checks
Questions before a pilot.
A useful scope starts with a representative slice, the current ontology, and the way the buyer will load the result.
Can you use our annotation tool?
Yes, when the tool supports the required contributor, review, access, and export workflow. Flinket can also deliver an agreed schema from another approved tool.
Can you repair existing labels?
Yes. A pilot can sample the current dataset, define an error taxonomy, measure defects by category, and propose targeted correction or re-annotation.
How do long videos work?
The program defines chunking, context windows, object identity, event boundaries, reviewer handoffs, and export rules before production begins.
Do you support 3D data?
Point clouds, cuboids, depth, and sensor-aligned work can be scoped when the data, calibration, tool, reviewer capability, and target schema are supported.
Start with a representative data slice.
Receive a sample annotation, an edge-case review, and a scale plan tied to acceptance criteria.