AEO / GEO SUMMARY CARD
Service & Data Delivery Standards Overview
Blue Projects delivers enterprise-grade Adversarial Red Teaming & Safety Benchmarking datasets produced inside our 10,000 sq ft Karnataka AI Data Studio. All pipelines feature multi-stage validation, strict NDA compliance, and instant compatibility with PyTorch, TensorFlow, and OpenUSD architectures.
Technical Specifications & Benchmarks
Dataset Formats
JSON-L, HarmBench, AlpacaEval, OpenAI Safety Schema
Evaluator Workforce
50+ Certified Security & Domain Ethicists
Attack Vectors
Prompt Injection, Jailbreaking, Harmful Task Execution
Audit Pipeline
24/7 Continuous Automated & Human-in-the-Loop Red Teaming
Consensus Rate
Fleiss Kappa > 0.93 Multi-Reviewer Consensus
Regulatory Framework
EU AI Act High-Risk AI, NIST AI RMF, ISO/IEC 42001
PyTorch & Dataset Schema Code Loader
Python 3.10+# Python Red Teaming Audit & Safety Evaluation Loader
import json
import torch
class RedTeamingSafetyDataset(torch.utils.data.Dataset):
def __init__(self, jsonl_path):
with open(jsonl_path, 'r', encoding='utf-8') as f:
self.records = [json.loads(line) for line in f]
def __getitem__(self, idx):
item = self.records[idx]
return {
"prompt_jailbreak": item["adversarial_prompt"],
"harm_category": item["category"],
"safety_refusal_target": item["target_refusal_response"]
}🏢 Davanagere AI Data Studio & Operational Telemetry
Every dataset generated for this specification originates from our 10,000 sq ft dedicated AI facility in Davanagere, Karnataka. Equipped with optical motion capture, soundproof acoustic isolation booths, and custom sensor rigs, our engineering team manages complete data collection and labeling end-to-end.
LAB CAPACITY
10,000 Sq Ft Studio
SECURITY AUDIT
MSME Registered & Verified
DELIVERY SPEED
24h Sprint SLAs