Security & Trust
Case Studies
Talk to an AI Data Expert
The operating model

Reliable data is manufactured, not collected.

Anyone can hand you labels. The difference is the operation around them. Every OmniTech program runs the same disciplined lifecycle — so quality is engineered in from the first pilot to steady-state production.

AI data lifecycle

From objective to continuous improvement.

Eleven stages, one accountable team. The loop never really ends — measurement from production feeds straight back into design.

Discover

Understand the model objective, task, domain, risk, complexity and volume. Agree what “good” means before anything starts.

Design

Develop and refine the taxonomy, rubrics, instructions and edge-case rules together with your research team.

Pilot

Run a small cohort to establish a baseline and surface ambiguity in the guidelines early.

Calibrate

Resolve disagreements and ambiguities; tighten the rubric until judgments converge.

Certify

Qualification testing and controlled onboarding — only certified contributors go into production.

Produce

Managed, monitored task execution with live quality signals, not a fire-and-forget queue.

Review

Layered quality assurance — sampling, gold tasks and independent second passes on flagged work.

Adjudicate

Senior reviewers resolve disagreement and critical errors, setting the precedent for the next batch.

Deliver

Validated output with quality metadata — so you know not just what the label is, but how sure we are.

Measure

Report acceptance, agreement, rework and throughput against the SLA. No black boxes.

Improve

Error patterns and disagreements feed back into guidelines, training and worker coaching — every cycle.

Measure → Improve → Design — the loop that compounds quality
Quality framework

Quality as a measured system.

We treat quality like an engineering discipline: multiple layers of review, senior adjudication, and honest reporting of the signals that predict downstream model behavior.

Production
Certified contributors, gold tasks embedded
Quality Review
Sampling & hidden-check scoring
Secondary Review
Independent second pass on flagged work
Adjudication
Seniors resolve disagreement & critical errors
Client Acceptance → Feedback Loop
Findings update guidelines & coaching

Signals we track & report

First-pass acceptance
Accepted without rework
per batch
Gold-task accuracy
Performance on hidden checks
per contributor
Inter-annotator agreement
Consistency across the team
per task
Adjudication & critical-error rate
Escalations and severe misses
monitored
Rework & guideline-deviation rate
Where and why work is returned
tracked
Throughput & turnaround
Against the agreed SLA
SLA

Targets are set per program with your team. We report the real numbers from your work — we don’t publish invented percentages.

Workforce qualification

How a contributor becomes production-ready.

Our real product is the system that turns applicants into a qualified, calibrated, monitored workforce — and keeps them there.

01
Source
Targeted, domain-specific recruiting.
02
Screen
Language, domain & integrity checks.
03
Assess
Task-specific skill assessment.
04
Train
Guidelines, examples, edge cases.
05
Certify
Pass qualification to go live.
06
Calibrate
Align to gold & peers.
07
Deploy
Assign to matched work.
08
Monitor
Track & coach performance.
09
Re-certify
Requalify as tasks evolve.

Specialist roles we qualify & manage

AI Data AnnotatorsAI / LLM EvaluatorsCoding ExpertsMathematics & STEM ExpertsLanguage SpecialistsQA ReviewersTrust & Safety AnalystsCalibration LeadsAdjudicatorsProgram Managers
Technology & tooling

Tool-agnostic. Client-controlled when required.

We’re honest about this: our edge is the managed operation, not a proprietary platform. That means we fit your stack instead of forcing ours.

Client platform

Teams work directly in your customer-approved tools and environments.

Approved third-party tools

Support for established annotation and evaluation platforms your program requires.

Managed workflow

We run the workforce, qualification, calibration, QA and reporting around whatever tool you use.

Private delivery

Client VDI / VPC and controlled endpoints where required and contracted.

What we don’t claim: we don’t market a proprietary annotation platform we don’t have. If a program needs specific tooling we can’t support, we’ll tell you — and often we’ll run beautifully inside the environment you already trust.

See it on your work

Run a pilot and watch the model work.

The fastest way to judge an operating model is to run one. Let’s scope a small, measured pilot on your real task.