Data Annotation & AI Readiness
85% of Failed AI Projects Blame Data Quality.
Only 12% of Companies are Ready.
Your model isn’t underperforming because the architecture is wrong — it’s underperforming because the data feeding it isn’t AI-ready. Gartner found 85% of failed AI projects cite poor data quality as the root cause, and only 12% of organizations have data good enough to support AI applications.
In-House AI Hiring Costs $300K+
and 6 Months You don’t Have.
Senior AI/ML total comp now runs $220K–$550K, before recruiting fees, before ramp time, before the risk that your AI strategy shifts mid-search. A Hurix engineering pod trades that fixed bet for a predictable monthly cost.
85% of failed AI projects cite data quality*
12% of orgs have AI-ready data*
60% of unready AI projects abandoned by 2026*
85% of failed AI projects blame data quality
The problem
Why do your AI models underperform in production?
Usually not the model. Inconsistent labels, no human-in-the-loop review, and data that was never validated for the use case — AI faithfully reproduces those flaws at scale instead of correcting for them.
How it works
Three capabilities. One reason models keep underperforming.
Built to answer the exact questions your team is already searching for.
ANNOTATE
High-quality human-in-the-loop annotation
Expert reviewers in the loop, not fully automated labeling that quietly compounds errors into your model’s training set.
→ “lack of human in the loop data annotation process”
LABEL
Multilingual data labeling at scale
Consistent labeling standards across every language and market your model needs to perform in — not just the one it was trained on.
→ “multilingual data labeling for global AI models”
GOVERN
Secure, compliant data operations
Data annotation built to enterprise security and compliance standards from day one — not retrofitted after a review flags it.
→ “vendor for secure compliant data annotation services”
The research
What Gartner’s data says about AI failure
Independent 2025–2026 industry research — not a Hurix case study, just the numbers behind why data readiness matters right now.
Gartner, 2025–2026, as reported by Folio3 AI and Connected Paths
85%
Of failed AI projects cite poor data quality as a root cause
12%
Of organizations have data of sufficient quality to support AI applications
60%
Of AI projects lacking AI-ready data predicted to be abandoned through 2026
Every model built on unready data is a failure waiting to be diagnosed.
Bring your current data pipeline and model performance numbers. We’ll show you exactly where the gap is — not a generic demo. Takes 30 minutes.
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