Annotation
Advanced post-training data operations for SFT, RLHF, verifier-backed learning, and agentic systems.
We provide advanced AI data operations for the post-training stack: SFT, preference optimization, verifier-backed learning, tool-use training, GUI training, multimodal labeling, and benchmark construction. Our focus is the layer where curated demonstrations, preference data, verifiable rewards, and full agent trajectories drive measurable capability gains.
What We Deliver
We support training and evaluation programs that require more than one-pass annotation. Teams use our outputs for instruction tuning, DPO and RLHF, verifier-driven reinforcement learning, tool-use evaluation, agent release gating, regression dashboards, policy review, and UI-agent training.
Operating Snapshot
0
labeled or reviewed units per month
0
tool-use trajectories reviewed monthly
0.0%
first-pass QA accuracy
0.0%
client delivery SLA adherence
Capabilities
Advanced labeling built for realistic agent, coding, GUI, and multimodal training loops.
Instruction / SFT labeling
What we label
High-quality demonstrations, gold completions, structured outputs
Training use
Instruction tuning, task grounding
Preference labeling
What we label
Chosen/rejected pairs, ranked responses, pairwise judgments
Training use
RLHF, DPO, reward-model training
Verifier-backed labeling
What we label
Executable tasks with deterministic pass/fail
Training use
Verifier-driven RL, regression testing
Tool-use / function-call labeling
What we label
Tool selection, argument validation, step-by-step call chains
Training use
Agentic AI, MCP agents, orchestration systems
Trajectory labeling
What we label
Full execution traces with state changes, retries, and final outcomes
Training use
Long-horizon planning and recovery training
GUI / computer-use labeling
What we label
Clicks, keystrokes, screenshots, action-to-state alignment
Training use
Browser-use and desktop-use agents
Multimodal labeling
What we label
Image, video, audio, document, and cross-modal annotations
Training use
VLMs, grounding, retrieval, multimodal reasoning
Sub-agent environment labeling
What we label
Role-based workflows in cloned or simulated applications
Training use
Departmental copilots, workflow specialists
Service Lines
Flexible delivery across dataset creation, evaluation design, and specialized environments.
JSONL, Parquet, conversation trajectories, action logs, tool schemas, and evaluation splits.
Hidden verifiers, challenge tasks, failure taxonomies, and release-readiness suites.
Multi-turn tool-use tasks, API chains, stateful simulations, and response recovery traces.
VM setup, gold action recordings, screenshot sequences, and execution evaluators.
Bounding, tagging, QA, structured extraction, document understanding, and cross-modal tasks.
SFT corpora, preference sets, verifier-backed tasks, red-team and policy review datasets.
Application clones, enterprise sandboxes, workflow replicas, and sub-agent task environments.
Labeling Scope
Coverage across agentic systems, coding environments, GUI workflows, and enterprise multimodal data.
We support agents that must plan, clarify, sequence tools, recover from errors, and complete multi-step workflows.
Market Relevance
Public benchmark and market signals show that strong post-training performance increasingly depends on curated demonstrations, preference data, verifiable rewards, and realistic agent trajectories.
01
Hard terminal and coding tasks remain far from ceiling for frontier agents.
02
Curated skills can materially improve agent performance when evaluated under paired measurement.
03
Desktop-use agents still show a significant gap relative to human performance.
04
SFT to DPO/RLHF to verifier-backed RL increasingly requires specialist data operations.
| Metric | Representative Value |
|---|---|
| Monthly advanced data throughput | 42,000 labeled or reviewed units |
| Tool-use trajectories reviewed per month | 8,500 |
| GUI episodes recorded and QA-checked per month | 1,200 |
| Benchmark tasks authored or refreshed per quarter | 95 |
| First-pass QA accuracy | 94.1% |
| Post-review acceptance rate | 89.3% |
| Client delivery SLA adherence | 97.6% |
| Rework rate after adjudication | 8.7% |
| Gold-task accuracy across active programs | 96.4% |
| Inter-annotator agreement band on subjective tasks | 0.78-0.86 |
Delivered — Harbor-native hard tasks, hidden verifiers, oracle solutions, evaluation repos
Pass@1 on a coding/terminal fine-tune track improved from 31% to 44% after adding verifier-backed SFT and preference data.
Delivered — Skill packs paired with with-Skills / without-Skills evaluation
With-Skills resolution improved from 36% to 54% on enterprise workflow tasks.
Delivered — Multi-turn tool-use trajectories grounded in schema-valid function calls and DB-backed responses
Function-call argument accuracy improved from 81% to 93% and clarification compliance improved from 62% to 88%.
Delivered — VM-based GUI action trajectories with execution-based evaluators
Held-out computer-use task completion improved from 9% to 18%, with a 27% reduction in invalid UI actions.
Delivered — Simulated enterprise applications for specialist-role agents
Workflow compliance improved from 68% to 89%, with escalation errors reduced by 34%.
Quality Consistency
Quality consistency is the core differentiator in advanced AI data work. Leading programs are measured by agreement, gold-set accuracy, adjudication quality, and error escape rate, not by speed alone.
Every production artifact moves through a multi-layer quality path:
First-pass authoring or annotation
Structured peer review
Domain lead or senior QA review
Gold-task and sampling audit
Final packaging and delivery validation
For agentic and benchmark projects, we add schema validation, source grounding, hidden validation sets, adjudication, and risk-based sampling.
Projects run on versioned instructions and task-specific rulebooks, with guideline quality treated as a measurable production variable.
Reward Design
We do not incentivize pure throughput. Contributors are rewarded on quality, stability, complexity handled, and process discipline.
01
Monthly bonus unlock at >= 96% quality score
02
Throughput multiplier only if quality floor is met
03
Higher coefficients for tool-use, GUI, verifier-backed, and multimodal work
04
Quarterly recognition for lowest defect escape rate and strongest reviewer agreement
05
Additional incentives for guideline authorship, mentorship, and adjudication support
This structure helps avoid the most common failure mode in annotation operations: fast output with unstable quality.
Tell us about your SFT, RLHF, verifier-backed, or agentic data needs — we'll map the right workflow and quality controls.
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