Flinket

Human data operations for AI

Four data programs. One accountable operation.

Send a data brief

From a model requirement to a dataset your team can inspect.

Flinket plans, collects, annotates, evaluates, and delivers human data for teams building language, vision, multimodal, and physical AI systems.

One operating partner owns the specification, contributor workflow, production controls, review trail, and final handoff.

Send a data brief

Four data programs. One quality system.

Each program names the source, the artifact, the reviewer, and the evidence that ships with it.

Speech and languageCollection + annotation

Conversations that preserve language, speaker, and context.

Source
Consented speech, prompted dialogue, domain audio, and buyer-owned recordings.
Artifacts
Transcripts, diarization, timestamps, intent, emotion, code-switching, and linguistic review.
Quality
Audio checks, language calibration, reviewer agreement, and rejection reasons.
Speech program
Vision and multimodalAnnotation + enrichment

Images and video structured around what the model must see.

Source
Images, video, documents, sensor-aligned media, and buyer-owned archives.
Artifacts
Detection, segmentation, tracking, temporal events, captions, and cross-modal alignment.
Quality
Gold examples, edge-case routing, duplicate checks, and field-level provenance.
Vision program
Expert and post-trainingAuthoring + evaluation

Human judgment turned into tasks a model can learn from and be graded against.

Source
Domain workflows, model outputs, expert demonstrations, and buyer evaluation criteria.
Artifacts
Demonstrations, preference pairs, task sets, rubrics, reference answers, and red-team cases.
Quality
Credential checks where required, calibration, adjudication, and agreement reporting.
Expert program
Physical AICapture + temporal data

Human demonstrations and robot data with observation, action, and uncertainty kept separate.

Source
Egocentric video, industrial video, robot streams, teleoperation, and scoped field capture.
Artifacts
Task phases, visible interactions, pose, contact proxies, outcomes, failures, and recovery windows.
Quality
Synchronization checks, explicit nulls, inference status, reviewer trail, and loader validation.
Physical AI program

Proof lives in the delivery record.

A representative program record shows how evidence travels with the dataset. This is sample structure, not client data.

Field Value Evidence
program egocentric manipulation approved scope
task_phase reach → grasp reviewed boundary
visible_event hand closes around object observed
contact_state probable contact inferred, not telemetry
outcome partial success rubric + second pass
lineage source, contributor, reviewer, export manifest attached
01

Approved schema and annotation guide

02

QA-reviewed dataset and exception queue

03

Dataset card and provenance manifest

04

Format check against the agreed loader

Calibrate first. Scale after the evidence holds.

Volume enters only after the buyer and Flinket agree on what counts, what fails, and what must stay unknown.

Define an acceptance test
  1. 1.0

    Scope the decision

    Define the model objective, source data, rights, taxonomy, delivery format, and acceptance evidence.

    Output: program specification
  2. 2.0

    Build the supply

    Recruit and train the right contributors or reviewers for the language, domain, geography, or task.

    Output: approved operating pool
  3. 3.0

    Calibrate the work

    Run a representative batch, expose disagreement, refine instructions, and lock gold examples.

    Output: calibrated guide and sample
  4. 4.0

    Produce with controls

    Route uncertain and high-risk decisions to review while tracking source, worker, rubric, and disposition.

    Output: reviewed production dataset
  5. 5.0

    Validate the handoff

    Run schema, duplicate, consistency, leakage, and loader checks before the final export.

    Output: dataset, QA report, and manifest

Trust is an operating record, not a badge wall.

Controls are set for the program and documented in the statement of work. Flinket does not claim certifications it has not earned.

Rights before capture

Consent, allowed use, geography, retention, and deletion are defined before contributors create data.

Known field origin

Supplied, observed, inferred, reviewed, and missing fields remain distinct in the schema.

Reviewable lineage

Source references, contributor records, rubric versions, reviewer decisions, and export history can travel with the delivery.

Buyer-approved access

Named personnel, least privilege, approved tools, and time-bounded access are scoped where the workflow supports them.

Uncertainty stays visible

Missing telemetry remains null. Low-confidence decisions enter an exception queue instead of becoming false facts.

Your environment when required

Work can be scoped for a customer cloud or approved processing environment when the selected tools permit it.

A San Francisco office for accountable data programs.

Contracting entityFlinket Services Private Limited
Registered office548 Market St PMB9492, San Francisco, CA 94104
Program contactsupport@flinket.com

Start with the artifact you need.

No discovery maze. Send the data source, the model objective, and the acceptance question.

Model and research teams

Training sets, preference data, evaluation suites, and benchmark tasks.

Bring a failure mode or capability target. Flinket returns a scoped task design and sample artifact.

Robotics and physical AI teams

Egocentric capture, temporal annotation, robot-data QA, and format-ready episodes.

Bring the task, environment, sensors, and policy objective. Flinket separates observation from action and inference.

Enterprise AI teams

Domain data programs that stay aligned to your workflow, access model, and IP terms.

Bring the workflow and the people who know what right looks like. Flinket turns both into a production system.

Send one real data problem.

Tell us what the model must learn, what source data exists, and where the work must happen. We will reply with the questions needed to scope a sample.

support@flinket.com

Your brief is sent directly to Flinket.