Is this a data annotation company or BPO service?

Annotation and human feedback are part of what we deliver, but we approach them as an engineering discipline — defined taxonomies, versioned guidelines, quality systems, evaluation infrastructure, measurable outcomes, and a feedback loop into your AI team. If you only need commodity, high-volume labeling, a pure-play annotation provider may be the better fit. If you need the data to train, evaluate, and improve production AI, that's where XIVTech adds value.

Can you evaluate AI agents, not just LLM outputs?

Yes. We evaluate the complete agent task trajectory — planning, retrieval, tool selection, tool-call accuracy, task completion, escalation decisions, and error recovery — not just the final response.

Do you provide human feedback and preference data?

Yes. Preference rankings, response comparisons, ideal-response demonstrations, rubric-based evaluations, expert critiques, safety evaluations, and other structured feedback for model training and optimization.

Can you work with sensitive or regulated data?

Yes. Security and data-handling requirements are defined during discovery, before production data is introduced — controlled access, encryption, PII minimization and redaction, auditability, and defined retention and deletion policies as needed.

Can we start with a small pilot?

Yes. We recommend starting with 500–1,000 representative data points, which lets us validate taxonomy, quality requirements, delivery model, and economics before scaling.

How is this different from XIVTech's Data & AI/ML engineering service?

That service builds the data pipelines, platforms, and ML systems that power AI applications. AI Data Engineering focuses on the data those systems learn from and the evaluation systems used to measure them. The two are frequently engaged together.

AI Data Engineering

The Data & Evaluation Layer for Production AI in Auckland

Your AI is only as good as the data that trains it, the evaluations that measure it, and the feedback that improves it.XIVTech engineers that layer — pipelines, evaluation systems, and human feedback loops that turn raw data into production-ready datasets and model outputs into measurable, improvable performance.

Trusted by ambitious AI teams

DittoCLIKA.ZYCUS

Why AI Data Is an Engineering Problem

Models change. Prompts change. Agents gain new capabilities. Data and evaluation need to keep up — not sit still as a one-time labeling exercise.

We treat data and evaluation as an engineered system: versioned, tested, measured, continuously improved. And we're not owned by, or feeding, a foundation-model lab — your data and results stay yours.

What We Deliver

Training Data & Annotation Pipelines

Turn raw, unstructured data into production-ready datasets built around clear taxonomies, annotation guidelines, calibration, and measurable quality standards.

Text

Classification · Entity extraction · Intent · Sentiment · Relevance · Document annotation

Image & Video

Classification · Bounding boxes · Object detection · Segmentation · Keypoints · Tracking

Audio

Transcription · Speaker identification · Classification · Conversational annotation

Multimodal

Cross-modal classification · Image-text · Audio-text · Video-text annotation

LLM & Agent Evaluation Systems

Evaluation areas: Factual accuracy · Groundedness · Instruction following · Hallucination · Citation quality · Safety · Task completion · Retrieval quality · Tool-call accuracy · Escalation · Error recovery

The output is more than a score — it's a failure taxonomy, an evaluation dataset, and an actionable view of where the system is breaking down.

Human Feedback & Preference Data

Structured feedback for model training, evaluation, and optimization: preference rankings, response comparisons, ideal-response demonstrations, rubric-based scoring, expert critiques, safety evaluations, and red-team datasets — supporting SFT, preference optimization, RLHF, and continuous evaluation.

Data Quality & Evaluation Infrastructure

Quality isn't a QA step at the end of the process — it's part of the system, designed in from the beginning.

Guidelines → Calibration → Annotation → Review → Adjudication → Measurement

Gold-standard datasets · Annotator calibration · Inter-annotator agreement · Automated consistency checks · Multi-pass review · Sampling and audits · Expert adjudication · Continuous guideline refinement

Specialized AI Data Services

Go deeper on the data and evaluation layer that matters most to your AI system.

Training Data & Annotation Pipelines

Structured, quality-controlled training datasets and annotation workflows.

LLM & Agent Evaluation Systems

Repeatable evaluation systems for LLM, RAG and agent quality.

Human Feedback & Preference Data

Structured feedback and preference-data workflows for AI improvement.

Data Quality & Evaluation Infrastructure

Repeatable quality, review, adjudication and evaluation pipelines.

Built the Same Way We Build Everything Else

Data and evaluation engagements follow the same engineering discipline we apply across XIVTech. Optimize isn't the end of the cycle — it's the start of the next one. Results feed straight back into Discovery: prepare, annotate, evaluate, diagnose, improve, re-evaluate. That loop is what separates a data system from a one-time labeling project.

01
Discovery
Define data requirements, taxonomy, evaluation criteria, quality thresholds, security requirements, and success metrics before production work begins.
02
Pilot
Run a controlled 500–1,000 data point pilot to validate the taxonomy, guidelines, task complexity, quality targets, and turnaround time.
03
Build
Use AI-assisted processing where it scales; domain specialists and reviewers focus on ambiguous, high-value, and edge cases.
04
Validate
Apply defined quality gates through reviewer, QA, and expert adjudication layers, tracked continuously — not only at delivery.
05
Optimize
Turn evaluation results into failure patterns, improved datasets, refined guidelines, and recommendations for your model team.

Built on Real AI Data Tooling

We work with established open-source and commercial technologies and select the right stack for your data type, evaluation requirements, security model, and existing environment.

Evaluation & Feedback

LLM-as-judge frameworks · Custom evaluation harnesses · Rubric engines · Preference-ranking systems

Annotation Infrastructure

Label Studio · Argilla · Custom annotation interfaces

Data Engineering

Python · Apache Airflow · dbt · Apache Spark

ML & Analysis

PyTorch · pandas

Security & Delivery

Role-based access controls · Encrypted environments · Audit trails · PII detection and redaction

Built for Sensitive & Regulated Data

AI data can contain customer information, financial records, healthcare data, proprietary content, and confidential model information. Security requirements are defined during discovery — before production data is introduced.

Role-based access
Encryption
Data minimization
PII detection & redaction
Audit trails
Controlled team access
Retention & deletion policies

Where required, we design delivery processes around your organization's security, privacy, and compliance requirements.

Engagement Models

Project-Based

A defined dataset, annotation program, or evaluation initiative with clear scope, milestones, and delivery criteria. Best for: Pilots · New models · Dataset creation · Evaluation projects

Embedded Data Engineering Pod

A dedicated XIVTech team working continuously against your data and evaluation backlog, integrated with your existing engineering cadence. Best for: AI teams with recurring data and evaluation requirements

Managed AI Data Operations

XIVTech manages the operational layer end-to-end — data preparation, annotation, evaluation, QA, reporting, continuous improvement. Best for: Teams that want to scale AI data operations without diverting ML engineers from core model and product work

FAQ

The Data & Evaluation Layer for Production AI in Auckland

Built the Same Way We Build Everything Else

The Data & Evaluation Layer for Production AI in Auckland

Trusted by ambitious AI teams

Why AI Data Is an Engineering Problem

What We Deliver

Training Data & Annotation Pipelines

Text

Image & Video

Audio

Multimodal

LLM & Agent Evaluation Systems

Human Feedback & Preference Data

Data Quality & Evaluation Infrastructure

Guidelines → Calibration → Annotation → Review → Adjudication → Measurement

Specialized AI Data Services

Training Data & Annotation Pipelines

LLM & Agent Evaluation Systems

Human Feedback & Preference Data

Data Quality & Evaluation Infrastructure

Built the Same Way We Build Everything Else

Discovery

Pilot

Build

Validate

Optimize

Built on Real AI Data Tooling

Evaluation & Feedback

Annotation Infrastructure

Data Engineering

ML & Analysis

Security & Delivery

Built for Sensitive & Regulated Data

Engagement Models

Project-Based

Embedded Data Engineering Pod

Managed AI Data Operations

FAQ

Is this a data annotation company or BPO service?

Can you evaluate AI agents, not just LLM outputs?

Do you provide human feedback and preference data?

Can you work with sensitive or regulated data?

Can we start with a small pilot?

How is this different from XIVTech's Data & AI/ML engineering service?

The Data & Evaluation Layer for Production AI in Auckland

Trusted by ambitious AI teams

Why AI Data Is an Engineering Problem

What We Deliver

Training Data & Annotation Pipelines

Text

Image & Video

Audio

Multimodal

LLM & Agent Evaluation Systems

Human Feedback & Preference Data

Data Quality & Evaluation Infrastructure

Guidelines → Calibration → Annotation → Review → Adjudication → Measurement

Specialized AI Data Services

Training Data & Annotation Pipelines

LLM & Agent Evaluation Systems

Human Feedback & Preference Data

Data Quality & Evaluation Infrastructure

Built the Same Way We Build Everything Else

Discovery

Pilot

Build

Validate

Optimize

Built on Real AI Data Tooling

Evaluation & Feedback

Annotation Infrastructure

Data Engineering

ML & Analysis

Security & Delivery

Built for Sensitive & Regulated Data

Engagement Models

Project-Based

Embedded Data Engineering Pod

Managed AI Data Operations

FAQ

Is this a data annotation company or BPO service?

Can you evaluate AI agents, not just LLM outputs?