Enterprise ROI Calculator | CTGT Policy Engine
Enterprise ROI Calculator

Quantify Your Return on Certainty

Model the financial impact of deterministic AI governance. Calculate savings from reduced hallucination liability, infrastructure consolidation, and the open-source arbitrage, all grounded in benchmark data.

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Build Your Deployment Profile

Input your organization's AI deployment parameters. All calculations are derived from verified benchmark performance data.

Industry Vertical ?
Annual AI Query Volume ?
Current Model Tier ?
Current AI Stack ?
Avg. Tokens per Query ?
AI Engineering FTEs ?
3-Year Financial Impact
Infrastructure Savings $0
Inference Cost Reduction $0
Headcount Reallocation $0
Risk Mitigation Value $0
CTGT Platform Fee (3yr)* -$225K
Net 3-Year Value
$0
0% ROI
*Platform fee $50K-$100K/yr (midpoint shown). Use-case costs may be additional.
Cumulative Impact

Value Trajectory Over Time

Visualize the compounding financial advantage of deterministic AI governance versus conventional approaches.

Cumulative Cost Comparison
Total Cost of Ownership: Current Stack vs. CTGT Policy Engine
$0 $1M $2M $3M $4M Year 0 Year 1 Year 2 Year 3
Current Stack TCO
CTGT Policy Engine
Savings Zone
96.5%
Hallucination Prevention
3.3×
Accuracy Multiplier
80%
TCO Reduction
<6mo
Payback Period
Detailed Breakdown

Cost Component Analysis

Cost Component Current Stack (Annual) With CTGT (Annual) Annual Savings

Based on benchmark data, CTGT delivers 96.5% hallucination prevention on HaluEval (GPT-120B-OSS). Where RAG degrades performance (legal reasoning dropping from 69% to 39% on Gemini 3 Pro, entity resolution falling to 48%), CTGT maintains or improves accuracy while eliminating infrastructure overhead.

Methodology

Calculation Transparency

All projections are derived from verified benchmark performance and industry-standard cost structures. We believe in defensible, reproducible analysis.

Performance Data Sources
Accuracy metrics derived from CTGT benchmark suite: HaluEval (general hallucination), TruthfulQA (misconception resistance), and domain-specific evaluations in Finance, Law, and History. Models tested: GPT-120B-OSS, Gemini 2.5 Flash-Lite, Claude 4.5 Sonnet/Opus, Gemini 3 Pro Preview, GPT 5.2.
Benchmark Data: December 2025
Cost Model Assumptions
Infrastructure: Vector DB $60K-$120K/yr, Embedding compute $30K-$60K/yr. Talent: AI Engineer median $184,757/yr (loaded cost ~$240K). Inference: Frontier models ~$15-45/1M tokens, OSS ~$0.40/1M tokens. Risk: Average AI incident remediation $3.2M, frequency 2.3/quarter (industry baseline).
Market Data: Q4 2025
Risk Valuation Framework
Risk mitigation value calculated as: (Baseline Error Rate - CTGT Error Rate) × Query Volume × Error Severity Factor × Industry Multiplier. Severity factors: Financial Services 1.5× (regulatory + liability), Insurance 1.3× (claims leakage), Media 1.2× (brand + IP), CPG 1.0× (operational). Based on benchmark showing CTGT achieves 96% entity resolution vs. RAG 48%, and 87% legal accuracy vs. RAG 81%.
Framework: Enterprise AI Economics

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This calculator provides estimates based on industry benchmarks and publicly available performance data. Actual results will vary based on implementation specifics, existing infrastructure, and organizational factors. All benchmark metrics sourced from CTGT Policy Engine evaluation suite (December 2025). Consult with CTGT enterprise team for validated projections.