Inputs
Calls, meetings, prompted recordings, existing transcripts, model hypotheses, language requirements, and a target schema.
← All data programs
Speech data your model can use.
Turn recordings, transcripts, and production failures into consented, aligned language data with speaker context and review evidence intact.
- source
- speaker_014.wav
- segment
- 00:18.240 to 00:23.880
- speaker
- spk_02 / consent logged
- language
- buyer-defined taxonomy
- transcript
- normalized + time aligned
- review
- native-language second pass
A program defined by the handoff.
Flinket starts with the model decision and the acceptance rule, then builds the collection, annotation, review, and delivery plan around them.
Managed work
Consented collection, transcription, normalization, translation, time alignment, diarization, language labels, and evaluation curation.
Outputs
Accepted audio, segment timestamps, speaker IDs, structured labels, rights manifests, rejection reasons, and agreed data splits.
Quality
Audio validation, shared calibration data, native-language review, adjudication, label-specific agreement, and parser checks.
Delivery
Your schema, your approved platform, or an agreed export with a QA report, issue taxonomy, and versioned manifest.
Work grouped by the model job.
The program can start with buyer-owned audio or include new collection. Every workstream keeps source, consent, transformation, and review records connected.
Acquire representative speech
Recruit against a buyer-defined speaker matrix, capture consent, and validate device, environment, duration, and recording instructions.
Recover what was said
Transcribe, normalize, translate, align timestamps, identify speakers, and preserve code-switching or uncertainty where the guide requires it.
Describe meaning and delivery
Apply intent, entity, topic, sentiment, toxicity, pronunciation, acoustic-event, and conversation-turn labels from the approved ontology.
Measure production failures
Build evaluation slices around accents, noise, overlap, rare intents, unsafe responses, or other failures supplied by the model team.
Calibrate before volume.
The operating sequence stays visible from the first recording to the final loader check.
- 1.0
Define the decision
Name what the model must learn or measure, the allowed sources, and the acceptance rule.
Output: program specification - 2.0
Inspect the data
Review representative recordings, existing transcripts, and known failure cases before writing the guide.
Output: issue and coverage map - 3.0
Calibrate the team
Run a small batch with the intended speakers, annotators, and reviewers, then resolve disagreements.
Output: accepted sample and edge-case guide - 4.0
Produce with controls
Run file checks, route ambiguous language to review, and record corrections by task and label type.
Output: reviewed production data - 5.0
Validate the handoff
Test the export against the agreed schema or parser and deliver the evidence behind acceptance.
Output: dataset, QA report, and manifest
Quality is specific to the signal.
Transcription, speaker boundaries, intents, and acoustic events fail in different ways. They are checked and reported separately.
- File integrity, duration, sample rate, channels, clipping, silence, and required metadata.
- Gold-set calibration with written decisions for overlap, noise, names, normalization, and code-switching.
- Native-language or domain review when meaning depends on language, profession, or culture.
- Independent review and adjudication for ambiguous or high-impact segments.
- Export validation against the buyer's parser before final delivery.
Representative sample
A transcript row that carries its evidence.
This example shows the shape of a delivery. Fields and labels change to match the buyer's schema and acceptance method.
segment_idcall_014_seg_008stable source reference
time_range18.240 to 23.880 secondsboundary reviewed
transcriptnormalized buyer-approved textnative-language pass
speaker_rolebuyer-defined roleontology version attached
decisionacceptedautomated checks + reviewer
Questions before a pilot.
The useful answer depends on the language, source rights, signal quality, and model job.
Can you collect new data?
Yes, when the speaker profile, geography, consent terms, devices, prompts, and recording environment can be defined and supported for the program.
Which formats do you deliver?
Flinket maps to a buyer-approved schema or agreed export. A representative slice is tested against the buyer's parser before the final handoff.
How does review work?
Review is matched to the signal. Audio checks, native-language review, domain review, double-pass review, and adjudication are applied where the acceptance rule requires them.
Can work stay in our tools?
It can be scoped for a customer platform or approved processing environment when that environment supports the required contributor and review workflow.
Start with twenty representative recordings.
Receive a sample transcript and label set, a rejection taxonomy, and a pilot acceptance plan.