Scale your AI models with AARTIC AI’s human-in-the-loop data curation, annotation, and evaluation expertise, driving reliable, bias-aware generative AI performance and innovation.

Improve AI model accuracy with supervised fine-tuning, human-reviewed training data, and expert annotation workflows built around your business use cases.

Test model outputs for hallucinations, bias, unsafe responses, and edge-case failures with human-in-the-loop review.

Projects Delivered

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Fraud detection, risk assessment, trading algorithms, customer sentiment analysis, regulatory compliance, and more.

Diagnosis support, medical record analysis, workflow automation, imaging review, patient monitoring, virtual care assistance, and more.

Ticket routing, chatbot support, reply automation, sentiment analysis, knowledge search, agent insights, helpdesk and more.

Contract analysis, document review, case research, compliance monitoring, litigation support, risk checks, and more.

Threat detection, vulnerability analysis, incident response, fraud monitoring, security alert prioritization, and more.

Learning personalization, grading support, tutoring assistance, curriculum planning, engagement insights, and more.

Code generation, debugging, code review, documentation assistance, test case generation, refactoring , and more.

Crop monitoring, yield prediction, soil analysis, irrigation optimization, pest detection, farm automation, supply chain insights, and more.

Content generation, data analysis, workflow automation, research assistance, decision support, task planning, and more.

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Across SFT, RLHF preference data, prompt evaluation, model response review, and red teaming.

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From scoped evaluation pilots to ongoing production workflows for AI, fintech, LegalTech, and related teams.

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Trusted by AI teams building safer, more accurate, and better-aligned models.

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Inter-annotator agreement targets designed to support consistent, reliable data quality.

Human-in-the-Loop Workflows for Better AI Models

Train Smarter. Align Faster. Evaluate Safer.

PROCESS FOR DELIVERING AI WORKFLOW AUTOMATIONOur Proven Structured Process for Guaranteed Delivery

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Discovery

We review your model workflow, data type, task goal, risk areas, and quality expectations to confirm the right evaluation setup.

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Proposal

You receive a scoped pilot plan with defined deliverables, volume, timeline, reviewer profile, QA process, and acceptance criteria.

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Pilot

We build the rubric, calibrate reviewers, run the first batch, and deliver QA-reviewed outputs for your team to inspect.

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Review

Your team reviews the pilot results, flags issues, approves quality standards, and helps refine the rubric before scaling.

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Production

We move into recurring evaluation batches with a dedicated PM, QA lead, reviewer pool, weekly reporting, and structured delivery.

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Iterate

We monitor reviewer drift, update guidelines, track defect trends, retest edge cases, and improve the workflow as your model changes.

AARTIC AI gave us the structure we needed to review AI outputs with confidence. Their team is faster, more aligned, and our rubrics actually drive better reasoning.

James ThompsonAI LegalTech Startup

The rubric clarity and reviewer alignment we get with AARTIC AI has been a game changer. It’s now a core part of our eval process.

Michael B.Series-B Startup

What stood out was the quality control. The team did not just complete tasks. They calibrated reviewers, tracked defects, updated the rubric, and gave us QA reports we could actually use with our engineering team. It made our RLHF workflow faster and more reliable.

Ethan BrooksAI Startup

We needed evaluation data fast, but we could not afford noisy labels or a long vendor onboarding cycle. AARTIC AI helped us turn a loose rubric into a working review process within days. Their reviewers understood the task, flagged edge cases clearly, and gave our team usable signal from the first batch.

David RAI Infrastructure Startup