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Solutions · Talent platforms and networksLicensed and run inside your environment

You already have the people. Largwit is what runs the work.

AI data work is one of the few demand streams that pays for exactly what a talent network already holds: verified people, real credentials, and specialist knowledge a general workforce cannot supply.

Turning that demand into delivered work is an operations problem rather than a matching one. Largwit is the operating layer for it.

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The division of labour
You have
A qualified pool and client engagements
Largwit runs
Programs, quality, delivery, the record
Your client sees
Progress and quality, never your pool
Deployment
Licensed and run in your environment
01Operating the work

Connecting somebody to work is not the same as operating the work.

A platform that introduces people to opportunities has solved discovery. AI data programs need everything that comes after it: a task interface built to the client’s specification, a qualification bar enforced rather than advertised, quality controls that catch a problem while it is still cheap, throughput a client can be held to, and a record of how every delivered item was produced.

None of that is a feature you bolt onto a marketplace. It is a production system, and building one is a software company’s worth of work in a business that is not a software company.

What you would build
Task interfaces, per client specification
Qualification and assignment logic
Quality controls
Progress and delivery reporting
Audit trail and traceability

An engineering roadmap you now own, in a business that is not a software company.

OR
WITH LARGWIT
  • A qualified talent pool
  • Client engagements
  • Largwit as the operating layer

Programs you can deliver, on infrastructure that already exists.

02What gets a program signed

The questions that decide whether a program gets signed.

An organization buying AI data work is not buying access to people. It is buying a standard it can hold somebody to, a rate it can plan against, and an answer when something in the delivery turns out to be wrong.

Those three answers are what the operating layer produces, and they are the difference between a pilot and a renewal.

A buyer's questionsanswered by the operating layer
What is your quality bar?Configured per engagement and applied to the work as it moves, then reported as a production number while the program runs.
How fast can you go?Throughput computed from real activity, with expected completion at the current pace rather than an estimate.
Who did this, and when?Significant actions recorded, so a question about a delivered item is answered from the record rather than from memory.
Can we see progress?Controlled visibility for authorized stakeholders, without exposing how the work was staffed or routed.
What happens if it is wrong?Problems surfaced while the program is still running, which is the only point at which they are still cheap.
03The machinery

The machinery between a qualified person and a delivered dataset.

Six things stand between somebody who could do the work and a dataset a client will accept. All six are configuration on the same platform, and none of them touches your pool from the outside.

Program setupEach client engagement configured as a project: task interface, requirements, quality bar and delivery format, approved before anyone works on it.
QualificationThe bar for a program defined and enforced on the queue itself, so somebody who does not hold it is never offered the work.
Work distributionWork claimed, held, returned and completed under the time limits and capacity rules the program sets.
Quality controlsAutomated validation and human review combined to the standard each client is held to, reported while the program is still running.
Client visibilityProgress, throughput and quality shown to the client without exposing your pool or how work was routed through it.
Delivery and auditStructured output in the required format, traceable to the production that produced it.
LARGWITone environment · one workforce · one record of what happened
04The scarce input

Verified people are the scarce input.

The AI data programs worth having are the ones a general workforce cannot do: evaluation that needs a working clinician, code review that needs an engineer, document work that needs somebody who reads the source for a living.

A network that already knows who its people are, and can prove it, holds the input that is actually hard to get. Largwit turns that into a qualification a program can be staffed against rather than a claim on a profile.

Supported programs
From your pool to delivered work
  1. 01A client engagement arrives with its specification
  2. 02Configured as a program and approved
  3. 03Staffed from your pool against its qualification bar
  4. 04Run under the program's quality controls
  5. 05Progress and quality visible to the client
Deliveredand accountable for
05Once

The build happens once.

The first client engagement is where the operating layer earns its place. Every one after it is configuration: a different specification, a different bar, a different delivery format, on infrastructure that already exists.

That is the difference between taking on AI data work as a line of business and taking on an engineering programme with each contract signed.

06Start 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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