Agentic Services

Find annotation expertise that matches the task

The project begins with 500 de-identified customer-support examples that need intent labels, answer-quality judgments, and short rationales. The data lead specifies the domain knowledge required and prepares a 50-case calibration sample containing both routine and ambiguous cases. Scale AI and Surge AI are assessed on their proposed expertise and review process, not simply on the number of workers they can supply.

The buyer commissions a managed service rather than assuming access to named individual freelancers. Agree reviewer qualifications, turnaround, disagreement handling, and what counts as an accepted example. Calibration is part of the paid scope; production begins only once the instructions are understandable and the parties agree how unclear cases will be escalated.

Sources: Scale AI: expert data · Surge AI: expert workforce

Give people and software distinct responsibilities

Scale AI is the proposed primary annotation service and Surge AI the independent review service. This is a division to negotiate, not a claim that either company automatically integrates with the other. Factory AI writes the manifest checks, conversion scripts, and export tests in the buyer’s repository. Label Studio gives the buyer a place to inspect returned labels and resolve review findings.

Providers may require their own production tools. In that case, the contract specifies portable exports that the buyer can import into Label Studio; the scenario does not require external annotators to work inside the buyer’s instance. Stable record IDs and a versioned schema connect the outputs without inventing a shared provider workflow.

Sources: Factory: coding Droids · Label Studio: annotation and evaluation

Set the budget for a reviewed, usable dataset

The $30,000 ceiling includes calibration, annotation, independent review, pipeline work, and required tooling. Quotes must separate those costs and explain rework charges. The four-week target remains a requested schedule until the providers accept it. Hiring more annotators is useful only when the review process can keep up with their output.

Calibrate the experts before releasing the batch

Muse starts the authorized Scale AI and Surge AI assignments with the sample, rubric version, data-access limits, and acceptance criteria. The providers return their interpretations of ambiguous examples before the team commits the remainder of the work. The buyer records each clarification so the same issue does not produce conflicting instructions in separate conversations.

The calibration sample is part of the 500-case scope, not an additional hidden batch. When instructions change, identify which already-labeled records need rework. An agreement on a revised rubric is not proof that every example now meets it; the subsequent review still has to check the actual labels.

Build an import path the buyer can inspect

Factory AI works in an authorized repository to validate unique IDs, required fields, permitted labels, and export encoding. Its changes go through code review and run against fixture data before production exports are imported into Label Studio. Annotation rationale and provider batch identifiers remain attached to the records.

A failed schema check returns a specific error list to the responsible provider. Code can check that a rationale exists; it cannot establish that the rationale is sound. That judgment belongs to the annotation experts and reviewers, with the buyer deciding how unresolved disagreements affect acceptance.

Sources: Factory: coding Droids · Label Studio: annotation and evaluation

Review independently and resolve disagreements

Surge AI reviews the contracted coverage without silently copying the original label as its own judgment. Label Studio holds the buyer’s comparison and adjudication record, while Scale AI receives actionable correction requests. Muse tracks whether a disagreement was resolved, excluded with a reason, or left open. Production data is not released simply because a provider reports the assignment complete.

Receive a dataset with an inspectable review history

The handover joins each example to its annotation, rationale, rubric version, review status, and any correction. Factory AI’s validation report establishes that the files can be consumed by the intended pipeline. The data lead samples accepted records in Label Studio and checks the review evidence from Scale AI and Surge AI before signing off.

The useful outcome is a batch that can be traced and corrected. A clean export does not demonstrate better model performance, and this engagement does not include a model-training result. The buyer can use the accepted examples in a later training or evaluation run with its own safeguards.

Accept the contracted work against explicit checks

Deliver 500 accepted examples. Track exclusions separately and replace rejected cases within the agreed scope so the accepted count does not quietly shrink. Confirm the independent review coverage, adjudication history, and reproducible schema validation. A batch with missing rationale or unexplained count differences goes back for correction before it is recorded as accepted.

Calculate the net value of coordination time saved

For one 500-example batch, the pilot estimate reduces buyer coordination from 32 to 21 hours. Eleven hours at an assumed $100/hour represents $1,100 of recovered team capacity; subtract a $200 cost allowance for incremental coordination tooling to estimate $900 in net value for the batch. Annotation and review fees remain in the pilot budget in both approaches.

Measure briefing, export reconciliation, and review coordination against the 32-hour baseline. Calculate net time value at $100 per hour after coordination costs, and reconcile expert invoices separately. The estimate values recovered team capacity; it does not reduce the agreed annotation fee.