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PlatformCapability level, not mechanism

The operating layer for AI data programs.

One environment across the lifecycle of an AI data engagement: configuring the project, staffing it, running the work, managing quality, watching production, and delivering the result.

Request a demoSupported workloads
Six operations

Each is configured per project. All of them share one workforce and one record.

Project
Configured, reviewed, approved
Workforce
Roles, qualifications, standing
Workflows
The shape the work takes
Quality
Controls inside the workflow
Intelligence
Computed from production
Delivery
Traceable to what produced it
01Project operations

Configure and manage engagements from intake through delivery.

A project in Largwit carries its own specification: the task interface contributors see, the requirements document the work is held to, the operational settings that govern how work moves, and the delivery expectations it has to meet.

Project configurations are reviewed and approved by people before a project opens to a workforce.

Standing a project upfour steps, one approval
  1. 01Requirements and delivery terms
  2. 02Task interface and quality expectations
  3. 03Operational settings
  4. 04Review and approval by somebody other than the author
Queue opennothing opens on its author's say-so
02Workforce operations

Coordinate the people who do the work.

Contributors, reviewers, domain experts, project managers and operations staff work in one environment, with access determined by role and project participation.

Your own team, a network you operate, or providers you already work with, coordinated through the same control layer and the same oversight.

Workforce controlsper person, per project
accessWhat a person can see and do follows the role they hold.
assignmentParticipation is granted per project, not per organization.
qualificationThe bar for a project is defined, and staffed against.
availabilityWorking hours and capacity, recorded by the people themselves.
standingActive, paused pending review, or withdrawn, with the reason recorded.
Bringing somebody on: invited, or joined through your own onboarding; given a role; qualified against the project's bar; available in their own hours. Then the queue may offer them work.
03Data workflows

Support the shape the work actually takes.

AI data programs differ in what a task is, what evidence supports a judgement, and what a finished item looks like. The workspace a contributor sees follows the project rather than the other way round.

Structured annotationDefined fields, controlled vocabularies and validation appropriate to the task.
Evaluation programsRating, comparison, ranking and critique against project-defined criteria.
Multimodal workText, image, audio and video handled within one operation and one record.
Document-grounded workJudgements tied to source material, so a conclusion can be checked against it.
Research programsInvestigative work where the finding, and the evidence for it, are the deliverable.
Expert dataDomain programs staffed against qualifications the customer defines.
ONE WORKSPACEthe interface follows the project · one record beneath all of it
04Quality controls

Quality controls configured per engagement.

Configurable combinations of automated validation, human review, project-specific evaluation, sampling, escalation and audit controls operate throughout the data lifecycle.

Quality expectations differ by contract. The controls are project configuration rather than a fixed process every project is forced through.

Quality operations
Controlscombined per project
validationStructured and programmatic issues caught before they spread.
reviewProject-specific judgement where expertise is essential.
samplingReview effort directed where the project needs it.
escalationDifficult decisions routed to more senior judgement.
auditTraceability across significant actions and delivered outputs.
05Operational intelligence

Know where production actually stands.

Progress, throughput, quality and delivery health are reported per project and across the organization, computed from production activity rather than entered by hand.

Operational signals surface conditions worth a person’s attention. They are observations for operators to judge, not automatic actions taken against anybody.

Reported per projectcomputed, never entered
progressCompletion against the volume the project is scoped to.
throughputProduction rate, from real activity.
acceptanceHow much work passes review the first time.
reworkWhere work is going back, and how often.
forecastExpected completion at the current pace.
signalsConditions worth a person's attention, raised for judgement.
06Delivery and reporting

Produce outputs a customer can accept and defend.

Deliveries are produced in structured formats aligned with what the engagement requires, and remain traceable to the production that generated them.

Authorized stakeholders can be given controlled visibility into progress and quality without exposing internal workforce operations.

From finished work to delivered batch
  1. 01Work completes under the project's controls
  2. 02Delivery requested in the required format
  3. 03Batch produced and recorded
Deliveredtraceable to the production behind it
07Start here

Bring us the programme you are about to build.

Largwit is deployed through direct engagement. Tell us what you are running and we will show you the platform against it, rather than against a generic demo.

Request a demoSupported workloads