Quality you can inspect

A workflow built to earn trust at every handoff.

We turn complex AI data requirements into a controlled operation—combining role-matched Vietnamese specialists, rigorous qualification, multi-layer QA, secure handling and transparent delivery.

Explore the workflow ↓Why work with us

8controlled stages

✓ Qualification before production✓ QA throughout—not only at delivery✓ Pilot-first, production-ready design✓ Secure, flexible delivery

Our 8-step workflow

From requirements
to reliable delivery.

Every engagement is adapted to the task, data sensitivity, quality target and operating cadence—while the control points remain consistent.

Project requirement review

We study the model use case, source data, target users, volume, quality threshold, sensitivity and downstream workflow before proposing an operating plan.

ALIGN

Guideline setup

Project goals are converted into practical decision rules with positive examples, negative examples, edge cases and escalation paths.

DEFINE

Role-matched team training

Contributors and reviewers are trained on the task, domain language, tools, security rules and the reasoning behind each label.

TRAIN

Qualification test

Reviewers complete a representative test measured against agreed answers or senior-review decisions before production access is granted.

QUALIFY

Controlled production

Work begins in monitored batches. Throughput, questions, category-level errors and team performance are tracked while access remains limited to assigned personnel.

PRODUCE

Multi-layer quality control

Automated validations, QA or consensus review, senior checks and targeted audits are combined according to task risk and label complexity.

VERIFY

Feedback and correction loop

Review findings are converted into coaching, rule clarification, rework and—when needed—requalification. Updated decisions flow back to the active team.

IMPROVE

Validated delivery and reporting

Approved data is mapped to the required schema, checked for completeness and delivered with the project’s quality and issue history.

DELIVER

Why work with us

More than an annotation workforce.

We build managed review operations around your model, guidelines and risk profile. The result is a team that can interpret complex instructions, evaluate AI output, protect Vietnamese language quality and explain how each decision was made.

01 / PEOPLE

The right reviewer for the right task.

AI data work is not interchangeable labor. A sentiment task, safety evaluation, medical transcription and product relevance study require different judgment, vocabulary and experience. We map the role profile before recruitment, then select Vietnamese contributors whose language ability, professional background and market knowledge match the work.

Senior reviewers and project leads are assigned where the task requires interpretation—not only clicking labels. They can explain guideline intent, resolve edge cases and identify when a model output is linguistically correct but culturally wrong.

What this means for youLess rework, faster calibration and reviewers who understand the reason behind the label.

Role profileExperience screenLanguage & domain testTask calibrationQualified project team

02 / QUALIFICATION

Production access is earned, not assumed.

Every contributor receives project-specific training built around your instructions, representative examples and known edge cases. Practice work is reviewed with direct feedback before a formal qualification test.

Only people who meet the agreed threshold move into live production. Performance remains visible after qualification: repeated mistakes trigger coaching, additional review or removal from the active team. When guidance changes, the team is recalibrated before the new rule is applied at scale.

Controls you can inspectTraining material, qualification criteria, calibration findings and documented corrective actions.

LearnGuidelines and examples

PracticeRepresentative tasks

QualifyThreshold-based test

MonitorOngoing performance

03 / QUALITY

Quality is designed into the operation.

We convert project goals into decision rules, examples, exception handling and escalation paths. This gives reviewers a common reference and makes disagreements diagnosable rather than subjective.

During production, quality is protected through automated validation where appropriate, QA or consensus review, senior audits and targeted checks on high-risk categories. Findings feed back into team coaching and guideline updates, so the workflow improves while it runs.

What we reportAgreement, accuracy, audit results, issue patterns, rework and decisions made on ambiguous cases.

Automated checksReviewer validationSenior auditFeedback & correctionValidated delivery

04 / SCALE

Prove the model in a pilot, then scale it deliberately.

A pilot is not a smaller version of production—it is where we test guideline clarity, contributor fit, review coverage, throughput and delivery compatibility. The findings create a realistic operating baseline before volume increases.

Once accepted, we expand with trained team cohorts, repeated qualification and the appropriate reviewer-to-producer ratio. This lets throughput grow without treating quality as a fixed assumption. Vietnam-based operations provide cost efficiency, while managed QA reduces the hidden cost of rework and unreliable data.

Commercial confidenceStart with a controlled scope, validate results, then scale team size and review depth around real evidence.

PilotTest fit and quality

→

CalibrateFix gaps and forecast

→

ScaleExpand qualified cohorts

Quality control

Quality is a system of prevention, detection and correction.

Final inspection alone cannot protect a large AI dataset. We place controls before production, inside active batches and before release, then use every finding to strengthen the next cycle.

The review depth is adjusted by risk. Clear, repetitive tasks may rely on validation rules and sampling; subjective or safety-sensitive work may require consensus, senior review and higher audit coverage.

What the client can seeQualification results, agreement or accuracy signals, audit findings, recurring error categories, rework status and corrective actions.

PREVENT

Training & qualification

Guidelines, examples, practice batches and threshold-based access.

DETECT

Review & audit

Automated checks, QA review, gold sets, consensus and senior audits.

CORRECT

Feedback & remediation

Root-cause analysis, coaching, rule updates, rework and requalification.

Data security

Access is limited by project, role and need.

Security begins during scoping, when we identify data sensitivity, permitted tools, transfer methods, storage expectations and who should be allowed to work on the project.

Assigned contributors receive only the access needed for their role. Project content is not placed in public channels, and client-controlled platforms can be used when data must remain inside the client environment.

NDALeast-privilege accessSecure transferNo public sharingControlled workspacesGDPR-aligned handling

Specific retention, deletion, certification or regulatory requirements are confirmed during scoping and documented for the applicable project.

Intake

Agree approved transfer channels, sensitivity and handling rules.

Access

Limit data to authorized project members and approved tools.

Production

Use controlled workspaces, clear policies and monitored permissions.

Delivery & closure

Transfer through the approved channel and follow agreed retention rules.

Built around your operation

Use your tools.
Receive your format.

We adapt to the client environment instead of forcing every project into one delivery system.

01 / TOOLING

We choose the tool around the task and governance model.

Computer-vision work may require polygons, segmentation or frame-level tracking in CVAT, Label Studio or SuperAnnotate. Language evaluation may be faster in structured spreadsheets. Sensitive programs can stay inside a client-controlled platform.

Before production, we confirm the interface, required fields, user permissions, review workflow and how decisions will be exported.

Supported environments

Label StudioSuperAnnotateCVATGoogle SheetsExcelClient internal platform

Tool choice is validated during the pilot—not after production begins.

02 / DELIVERY

The output is mapped to your downstream workflow.

We agree the schema before scale: field names, label structure, identifiers, timestamps, metadata, file naming and version rules. A sample export is reviewed early so format problems do not appear at final delivery.

Deliveries can be released in controlled batches, directly through a platform or through an approved secure transfer channel.

Common delivery formats

CSVJSONXLSXGoogle SheetsPlatform deliveryCustom schema

Custom formats and client templates are supported when defined during scoping.

03 / SCALE

Capacity grows through qualified cohorts.

Scaling is not achieved by adding untested people to the task. New cohorts repeat the same training and qualification process, while reviewer capacity and audit coverage grow alongside production volume.

Throughput targets are based on pilot evidence, task complexity and actual quality performance. This protects delivery forecasts from unrealistic assumptions.

PilotValidate task fit, quality and tooling.

CalibrateRefine rules, staffing ratios and throughput.

ScaleAdd qualified cohorts with matched QA coverage.

Operational accountability

Support does not stop at delivery.

We remain accountable for understanding reported issues, correcting work within the agreed scope and carrying lessons into the next delivery cycle.

✓

Quality remediationIssues are investigated, corrected and fed back into the workflow.

✓

Transparent reportingSee throughput, review findings, issues and corrective actions.

✓

Post-delivery supportWe remain available for questions, fixes and next-stage planning.

A workflow you can test before you scale.

Share a sample task, target quality level and delivery requirements. We’ll design a focused pilot with clear acceptance criteria.

Discuss your pilot ↗