Enterprise AI Analysis
DeepTRACE: Auditing AI Systems for Reliability Across Citations and Evidence
DeepTRACE introduces a novel sociotechnical framework to audit Generative Search Engines (GSEs) and Deep Research Agents (DR), addressing critical issues like overconfidence, weak sourcing, and citation inaccuracies. This analysis reveals the current limitations and pathways to trustworthy AI-driven information systems, emphasizing the need for balanced, factually supported, and transparent AI responses.
Key Insights for Enterprise Adoption
DeepTRACE's findings highlight critical areas for improving AI system reliability and trustworthiness in enterprise applications, from research automation to information synthesis.
0%
Max Unsupported Statements (DR)
0%
Max One-Sided Answers (DR)
0%
Min Citation Accuracy (DR)
0%
Max Source Necessity (GPT-5 DR)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Comprehensive Audit Methodology
The DeepTRACE framework is built upon eight measurable dimensions derived from real-world user feedback, spanning answer text, sources, and citations. It employs granular statement-level analysis, confidence scoring, and matrix-based evaluation of source grounding and citation integrity. This approach provides an end-to-end assessment of how AI systems reason and attribute evidence, moving beyond isolated component evaluation to a holistic sociotechnical perspective.
Enterprise Process Flow
Decompose Answer Text
→
Score Confidence
→
Scrape Source Content
→
Generate Citation Matrix
→
Generate Factual Support Matrix
→
Calculate 8 Metrics
GSE Findings: Overconfidence & Weak Sourcing
Our evaluation shows that public Generative Search Engines frequently produce one-sided, highly confident responses, especially for debate queries. Perplexity, for example, exhibited over 83% one-sided answers and very high confidence (90%+), yet its citation accuracy was as low as 49%. These systems often list numerous sources, but a significant fraction remains uncited (e.g., BingChat 36.2%) or unsupported by the generated statements (You.com and Perplexity 23-47%), creating a false impression of validation and undermining user trust.
83.4%
Highest One-Sided Answers (Perplexity)
49.0%
Lowest Citation Accuracy (Perplexity)
DR Agents: Mixed Outcomes and Path to Reliability
Deep Research agents showed mixed outcomes. While they generally reduced overconfidence (<20%) and some, like GPT-5(DR), achieved high citation thoroughness (87.5%) and low unsupported statements (12.5%), they still exhibited high rates of one-sidedness (54.7-94.8%). Other DR agents, such as Perplexity(DR) and YouChat(DR), struggled with very high unsupported statement rates (up to 97.5%) and low citation accuracy. This highlights that simply providing more sources or longer answers does not guarantee reliability; careful calibration, as seen in GPT-5(DR), is key to achieving trustworthy AI.
94.8%
Highest One-Sided Answers (Copilot)
12.5%
Lowest Unsupported Statements (GPT-5)
87.5%
Highest Citation Thoroughness (GPT-5)
Calculate Your Potential AI ROI
Estimate the efficiency gains and cost savings your enterprise could achieve by integrating advanced AI solutions, based on industry benchmarks.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your Enterprise AI Implementation Roadmap
A structured approach to integrating DeepTRACE-informed AI systems into your organization for maximum reliability and impact.
Phase 1: DeepTRACE Assessment & Gap Analysis
Conduct an initial audit of existing AI systems using DeepTRACE metrics to identify current reliability, sourcing, and citation weaknesses. Define specific performance benchmarks aligned with your enterprise needs.
Phase 2: Custom Model Calibration & Integration
Work with AI experts to fine-tune or develop custom Deep Research Agents, focusing on improving identified metrics like citation accuracy, factual support, and balanced perspective-taking. Integrate these calibrated models into your existing workflows.
Phase 3: Continuous Monitoring & Feedback Loop
Implement ongoing DeepTRACE monitoring to track system performance over time. Establish feedback mechanisms for users to report issues, ensuring continuous improvement and adaptation to evolving information landscapes and ethical considerations.
Ready to Build Trustworthy AI?
Connect with our AI specialists to explore how DeepTRACE's insights can transform your enterprise's AI reliability and ensure responsible, effective deployment.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat