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Infrastructure for AI data operationsDeployed by direct engagement

Run complex AI data operations from one platform.

The operating layer for AI data programs: configure a project, staff it, run it under your own quality controls, and deliver what it produced.

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The shape of it

Everything an engagement needs is already standing. A new contract is configured and operated, not built.

Engagement
Configured, not built
Workforce
Internal teams, providers, or both
Quality
Controls that run inside the workflow
Record
One trace, from intake to delivery
Inputs you holdRequirementsDataModelsWorkforce
LARGWITThe operating layer
ProjectsConfigured, reviewed, approved
WorkforceRoles, qualifications, standing
WorkflowsStages, routing, redundancy
QualityChecks, sampling, escalation
DeliveryFormats, terms, traceability
OutputClient-ready deliveryStructured, traceable, and aligned to the terms the engagement was signed on.

Every new contract should become a new project, not a new internal software build.

An AI data business rarely loses margin on the work. It loses it in the weeks between signing a contract and running it: configuring tooling that almost fits, writing scripts to move data between systems, assembling a quality process by hand, and building the reporting a client will accept.

That cost repeats per engagement, and it is invisible in the proposal.

03The operating model

What a programme costs to stand up.

An organization can hold every hard input and still not run a programme until the machinery around them exists. That machinery is assembled once per engagement, by engineers, out of tools that were each built for something else.

Assembled per engagement
Project management
Annotation tooling
Spreadsheets
Workforce systems
Internal scripts
Quality workflows
Model infrastructure
Delivery and reporting

Plus the custom engineering that holds them together, and the operations built on top of that.

BECOMES
LARGWIT
  • One project configuration
  • One workforce, one set of roles
  • One quality process
  • One delivery path

The operating infrastructure already exists. A new engagement is something the organization configures and operates.

04Platform architecture

One operating environment across the AI data lifecycle.

Six operations, standing on one base. They share a project, a workforce and a record, which is what separates an operating environment from six tools pointed at the same job.

Platform detail
IntakeConfigurationProductionOversightDelivery
01Project operationsConfigure and manage complex engagements from intake through delivery.
02Workforce operationsContributors, reviewers, experts and managers across every project.
03Data workflowsTraining, evaluation, annotation, multimodal, research and expert data.
04Quality managementAutomated validation and human evaluation, configured per project.
05Operational intelligenceProgress, throughput, quality and delivery health, computed from production.
06Delivery and reportingStructured, traceable output aligned to what the engagement requires.
LARGWIT PLATFORMOne environment · one workforce · one record of what happened
05The project object

A project carries its own specification.

Every engagement brings different requirements, quality expectations, formats and delivery terms. A project holds all of it in one place, so standing one up is configuration rather than construction.

Which is why the second programme costs less to start than the first.

Project specificationheld as one object
requirementsThe statement of work, the rubric, the worked examples
task interfaceWhat a contributor is shown, and what they answer
data schemaThe formats it arrives in and the formats it leaves in
workflowThe stages work moves through, and in what order
quality rulesWhich checks apply, where they apply, and what they escalate
accessRoles, qualifications, and what a client is shown
deliveryThe formats and terms the output has to meet
Reviewed and approved by a person before the queue opens. Nothing reaches a workforce on its author’s say-so alone.
06Quality control

Quality is a control, not a report.

Checks run where the work happens, not after it. A problem caught at the stage that produced it is the only point at which it is still cheap to fix, and the only point at which the person who made it is still holding the context.

01Intake

Specification agreed before work is claimed

02Annotate

Automated validation on submission

03Review

Human evaluation against the rubric

04Sample

Sampling across live production

05Escalate

Disputes to a senior decision

06Audit

Traceable to what produced it

07Deliver

Released against the agreed terms

07Workforce and client visibility

Run your own workforce, and show your client only what they should see.

Who is on the work, what they are qualified for and where they stand is yours to manage. What a client sees is the state of their programme, and it stops there.

Inside your organizationoperations
One rosterContributors, reviewers and experts across every project you run.
Access by roleWhat a person can see follows the role they hold and the projects they are on.
QualificationsThe bar is set per project and enforced on the queue itself.
StandingActive, paused pending a review, or withdrawn, with the reason recorded.
Visibility boundaryWorkforce operations do not cross it
What the client seesreporting
ProgressHow much of the programme is done.
ThroughputThe rate it is being produced at.
QualityHow the work is performing against the bar.
Delivery statusWhat has shipped and what is next.
08Governance and control

Built for sensitive AI data operations.

Largwit is designed around controlled access, customer isolation, operational auditability, private data handling and production resilience.

Customer isolationEnvironments and access boundaries are designed to stay logically separated.
Identity and accessRole-based control and multi-factor authentication on sensitive operations.
Data handlingProject data and media governed to the organization's requirements.
AuditabilityImportant actions and operational events can be traced.
Data lifecycleControlled retention and deletion requirements are supported.
Monitoring and recoveryOperational monitoring and recovery controls support production reliability.
09How it sits in your stack

Operate around your data, models and infrastructure.

Largwit coordinates the operational layer around what you already run, rather than moving you into a model ecosystem.

Largwit · configurestaffrunmeasuredeliver
Stays yours
Your dataDatasets and source material
Your modelsEndpoints you run or license
Your requirementsThe specification work is held to
Your workforceInternal teams, providers, or both
One record of what happenedTraceable to the production behind it
10Start 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.

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