📊 Benchmarks

Performance Comparison

Internal benchmarks: RCT SignedAI targets 99.7% accuracy vs 85% for single LLM, with up to 60% estimated cost savings, full audit trails, and cryptographic signing. Figures below are self-reported and not yet independently audited.

Accuracy (benchmark)

0.0%

Hallucination Rate (target)

Cost Savings (estimate)

<0ms

Warm Recall (p95, target)

Interactive Dashboard

Switch between Radar and Bar views to compare RCT SignedAI against Single LLM across key dimensions.

Performance Metrics

Accuracy (internal benchmark)

RCT SignedAI99.7%

Single LLM85%

Hallucination Rate (target)

RCT SignedAI0.3%

Single LLM15%

Warm Recall Latency (p95, target)

RCT SignedAI<50ms

Single LLM~300ms

Cost Efficiency (internal estimate)

RCT SignedAI3.74× less

Single LLMBaseline

Audit Trail

RCT SignedAIFull

Single LLMNone

Cryptographic Signing

RCT SignedAIYes

Single LLMNo

FDIA Protocol Score (internal benchmark)

RCT SignedAI0.92

Single LLM~0.65

L4 Virtuoso Benchmark (internal)

RCT SignedAI389/390

Single LLMN/A

Uptime SLA (design target)

RCT SignedAI99.98%

Single LLMNo SLA

Feature Comparison

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FeatureRCTSingle LLM

Multi-LLM Orchestration

Cross-Verification

Dynamic Model Routing

Persistent Memory (RCTDB)

Cryptographic Signatures

Complete Audit Trails

Intent Understanding (FDIA)

Self-Evolution (Genome)

Basic Text Generation

Single Model API

Platform Comparison

How RCT Ecosystem stacks up against LangChain and AutoGPT across enterprise-critical capabilities.

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Capability

RCT Ecosystem

LangChain

AutoGPT

Hallucination Rate (target)

0.3%

~12–15%

~10–20%

Accuracy Rate (benchmark)

99.7%

~85%

~80%

Cryptographic Audit Trail

✓ Full

✗ None

Multi-LLM Consensus

✓ 9 HexaCore LLMs

~ Manual wiring

✗ Single model

Persistent Memory

✓ RCTDB v2.0

~ Plugin-based

~ Limited

Warm Recall (p95, target)

<50ms

~350–600ms

~500ms–2s

Cost per Query (estimate)

3.74× vs all-Claude baseline

Baseline

+20–40% overhead

Enterprise Compliance

✓ Full audit+sign

✗ DIY only

✗ None

Intent-Centric Processing

✓ FDIA equation

✗ Prompt-centric

✗ Goal-decomp only

Self-Evolution (Learning)

✓ 7-Genome system

✗ Static chains

~ Experimental

* Data based on internal benchmarks (2025). LangChain and AutoGPT figures based on publicly available research.

Test Infrastructure v5.4.5

First 0-failure milestone across the enterprise (private) test suite — March 21, 2026. The public open-source SDK maintains its own separate, smaller verified suite — see TESTING_CANONICAL.md in the delentia-os repo.

4,849

Passed (enterprise, private)

Failed

6,738+

Total Tests (enterprise, private)

0.92

FDIA Accuracy (internal benchmark)

FDIA accuracy 0.92 (internal benchmark) vs industry average ~0.65. Test pyramid: Unit → Integration → Contract → Component → API → E2E → Performance → Security (8 levels). Zero failures across 6,738+ tests (enterprise, private snapshot) represents the first clean run in project history — not independently audited.

Related Research

SignedAI: Multi-LLM Consensus to Prevent Hallucination at Scale HexaCore 7-Model AI Infrastructure: Cost Reduction at Scale Delta Engine: 74% Lossless Memory Compression 4,849 Tests, 0 Failures: How RCT Verifies Everything

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