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☁️ CO₂ ~ 1.2g ⚡ Energy ~ 0.006 kWh 📈 Coherence: 0.87 🎯 Accuracy: 90.5%
RCE: Deterministic response based on verified semantic relations. Full audit trail available.
Relational Cognitive Engine

A coherence-as-a-service layer that significantly reduces AI hallucinations through relational graph optimization.

🎯 Reduced Hallucinations

90.5% average accuracy across diagnostic benchmarks. Deterministic reasoning grounds every output in verified relations.

🔍 Full Transparency

Complete audit trails show exactly how conclusions were reached, meeting EU AI Act requirements.

⚡ Energy Efficient

3-5× less computation than traditional approaches. Lower costs and reduced carbon footprint.

🔌 Easy Integration

Plugs into existing AI pipelines. Compatible with GPT-4, Claude, Llama, and any LLM.

Who Are We

From peer-reviewed research to production-ready AI reliability solution

📚 Origin: Academic Research

October 2025: Publication of peer-reviewed research paper "RCE-LLM: A Relational Coherence Engine for Consistent and Energy-Efficient Language Modeling"

Author: Ismail Sialyen, Independent Researcher

DOI: 10.5281/zenodo.17360372

Read the full paper: https://doi.org/10.5281/zenodo.17360372

🔬 The Research Foundation

The paper introduced a fundamental shift in language modeling: replacing probabilistic token prediction with deterministic coherence optimization over explicit relational graphs.

Key Innovation: Instead of maximizing token likelihood, RCE maximizes semantic coherence across five dimensions: units consistency, temporal ordering, arithmetic validity, coreference resolution, and factual grounding.

Validated Results:

  • 91.7% accuracy on units consistency tasks
  • 95.2% accuracy on compositional arithmetic
  • 90.5% average across all diagnostic benchmarks
  • 3-5× computational efficiency vs traditional attention

🚀 From Paper to Product

Following the publication, we recognized the urgent market need for reliable AI in regulated industries. Enterprise deployments face 25-40% hallucination rates, and the EU AI Act (August 2025) creates mandatory compliance requirements.

Our Mission: Transform the theoretical framework into a production-grade API that any company can integrate into their AI pipeline to significantly enhance reliability and traceability.

The Team

Ismail Sialyen

Founder & Chief Scientist

Independent researcher with groundbreaking work in contextual coherence theory. Author of the peer-reviewed RCE-LLM paper. Deep expertise in AI/ML research, graph theory, and optimization.

AI Engineering Team

Core Development

AI Engineer: Implementation, system integration, performance optimization

Data Scientist: Coherence metrics development, evaluation frameworks, benchmarking

Infrastructure & Security

AI Infrastructure & Security Engineer

Cloud deployment, security architecture, compliance infrastructure, scalable production systems

Commercial & IP

Commercial Engineer

Specializing in IT patents, intellectual property strategy, technology commercialization, and licensing

IKAMI Research

Research Organization

Dedicated to advancing deterministic AI reasoning and explainable language models for enterprise applications.

AI Pipeline Integration

RCE adds a verification layer to your existing AI stack. The earlier you integrate, the better the results.

🎯 Critical Insight: Integration Timing Matters

Native RCE (Best): Build your AI pipeline with RCE from the start. Maximum hallucination reduction and full traceability.

Early Integration (Better): Add RCE before LLM generation. Guides the model toward coherent outputs.

Post-Processing (Good): Verify LLM outputs with RCE. Catches errors but can't prevent them.

The earlier RCE is integrated into your pipeline, the better the hallucination reduction and the more complete the audit trail.

Integration Modes
1

RCE-Native (Recommended)

Best performance: Design your AI system around RCE from day one. Build relational graphs first, then use coherence scores to guide generation. Maximum reliability and full explainability.

→ Hallucination reduction: 15-20% on formal reasoning tasks

2

RCE-Rerank (Early Integration)

LLM generates multiple candidates → RCE scores each via coherence functional → Return highest scoring option. Better than post-processing because RCE influences the final output selection.

→ Hallucination reduction: 10-15% improvement over baseline

3

RCE-Verify (Post-Processing)

Your LLM generates output → RCE validates → Returns verified response with coherence score. Easiest to implement but offers least improvement since errors are detected after generation.

→ Hallucination reduction: 8-12% improvement

4

RCE-RAG (Enhanced Retrieval)

Vector DB retrieval → RCE extracts and validates relations from documents → Coherence-guided context selection → LLM generates with verified context. Improves both retrieval and generation quality.

→ Factual grounding: 85.6% accuracy on F5 benchmark

Compatible With

LLMs

GPT-4, Claude, Llama, Mistral, Gemini - works with any text generation model

Vector DBs

Pinecone, Weaviate, Chroma, FAISS, Qdrant - standard RAG stacks supported

Frameworks

LangChain, LlamaIndex, Haystack, spaCy - drop-in integration

Deployment

REST API, Python SDK, on-premise, or cloud - your choice

Industry Solutions

Pre-validated coherence modules for regulated sectors

🏥 Healthcare

Challenge: LLMs hallucinate drug interactions and medical advice

Solution: RCE validates against medical ontologies (SNOMED, RxNorm, WHO guidelines) using factual entailment module

Validated Results: 85.6% accuracy on factual grounding benchmarks

⚖️ Legal

Challenge: Inconsistent contract interpretation and temporal reasoning errors

Solution: RCE ensures temporal consistency and entity resolution

Validated Results: 89.4% accuracy on temporal reasoning, 90.8% on coreference

💰 Finance

Challenge: Quantitative errors and unit inconsistencies in financial analysis

Solution: RCE validates dimensional consistency and arithmetic validity

Validated Results: 91.7% accuracy on units consistency, 95.2% on compositional arithmetic

🏢 Enterprise AI Platforms

Challenge: General AI reliability issues and EU AI Act compliance requirements

Solution: RCE provides explainable outputs with complete audit trails

Result: Meets EU AI Act Article 13 transparency requirements, full traceability

📡 Telecommunications

Challenge: Network troubleshooting, incident root cause analysis, complex system diagnostics with temporal dependencies

Solution: RCE ensures temporal consistency and causal reasoning for network events and fault analysis

Validated Results: 89.4% accuracy on temporal reasoning, enabling precise incident timeline reconstruction

Simple Pricing

Start free, scale as you grow

Free

€0/mo
  • 1,000 verifications/month
  • Basic coherence checks
  • Community support
  • Public API access

Pro

€49/mo
  • 50,000 verifications/month
  • All coherence modules
  • Full audit trails
  • Email support (24h)
  • 99.5% uptime SLA

Pro Plus

€89/mo
  • 150,000 verifications/month
  • Priority support (4h)
  • Custom domains
  • Advanced analytics
  • SSO/SAML
  • 99.9% uptime SLA

Pro Max

€149/mo
  • 500,000 verifications/month
  • Dedicated support (1h)
  • White-label options
  • Custom integrations
  • 99.95% uptime SLA
  • Quarterly reviews

Enterprise (On-Demand)

Need more? Custom solutions for large-scale deployments

  • Unlimited verifications
  • On-premise deployment
  • Custom coherence modules
  • 24/7 phone support
  • Dedicated infrastructure
Academic Research

Peer-reviewed foundation published October 2025

RCE-LLM: A Relational Coherence Engine for Consistent and Energy-Efficient Language Modeling

Author: Ismail Sialyen (Independent Researcher)

Published: October 15, 2025

DOI: 10.5281/zenodo.17360372

Core innovation: Replaces probabilistic token prediction with deterministic coherence optimization over explicit semantic graphs.

Read the full paper →

Validated Benchmark Results

F1: Units Consistency

91.7% accuracy

Dimensional analysis and unit consistency validation

F2: Temporal Reasoning

89.4% accuracy

Temporal ordering and chronological constraints

F3: Arithmetic

95.2% accuracy

Multi-step compositional arithmetic problems

F4: Coreference

90.8% accuracy

Entity tracking and discourse coherence

F5: Factual Grounding

85.6% accuracy

Factual entailment and evidence grounding

Average Performance

90.5% accuracy

Across all diagnostic benchmarks

Computational Efficiency

Complexity Reduction: O(n²) → O(|R|d) where |R| ≪ n²

Practical Impact: 3-5× less computation than dense attention mechanisms

Energy Savings: Validated through complexity analysis, enabling deployment on standard CPUs

Frequently Asked Questions
What is RCE (Relational Cognitive Engine)?
RCE is a deterministic AI reasoning system that reduces hallucinations by validating outputs through relational graph optimization. Instead of predicting the next token probabilistically, RCE evaluates semantic coherence across five dimensions: units consistency, temporal ordering, arithmetic validity, coreference resolution, and factual grounding.
How does RCE differ from traditional LLMs?
Traditional LLMs (like GPT-4, Claude) maximize local token-to-token dependencies using attention mechanisms. This works well for fluency but lacks global semantic consistency checks. RCE builds explicit relational graphs and optimizes for coherence across the entire semantic structure, resulting in deterministic, verifiable outputs instead of probabilistic guessing.
What are the actual performance numbers?
Our peer-reviewed research paper validated RCE on five diagnostic benchmarks: 91.7% on units consistency, 89.4% on temporal reasoning, 95.2% on compositional arithmetic, 90.8% on coreference resolution, 85.6% on factual grounding, with 90.5% average accuracy across all tasks. These are significantly better than baseline LLM (73.5%) and LLM+RAG (77.4%) approaches.
Can RCE work with my existing AI pipeline?
Yes! RCE is designed for easy integration. You can use it in three modes: (1) RCE-Verify: post-processing verification of existing LLM outputs, (2) RCE-Rerank: coherence-based selection among multiple candidates, or (3) RCE-Native: building your AI system around RCE from the start. The earlier you integrate RCE, the better the hallucination reduction and traceability.
Which LLMs and frameworks are compatible?
RCE works with any LLM (GPT-4, Claude, Llama, Mistral, Gemini), vector databases (Pinecone, Weaviate, Chroma, FAISS), and frameworks (LangChain, LlamaIndex, Haystack). It's model-agnostic and integrates via simple REST API or Python SDK.
Why is early integration better than post-processing?
When RCE is integrated early in the pipeline (before generation), coherence scores can guide the generation process itself. Post-processing can only accept or reject completed outputs, but early integration helps the system produce more coherent responses from the start. Native RCE (building on it from day one) provides maximum benefit: 15-20% hallucination reduction on formal reasoning tasks vs 8-12% for post-processing verification.
Does RCE meet EU AI Act requirements?
Yes. RCE provides complete audit trails showing exactly how conclusions were reached, meeting EU AI Act Article 13 transparency requirements. Every decision is traceable through the relational graph structure, with explicit evidence citations and coherence scores. This makes RCE inherently compliant with explainability mandates that take effect in August 2025.
How much does RCE cost to run compared to traditional LLMs?
RCE is 3-5× more computationally efficient than traditional attention mechanisms. The complexity reduction from O(n²) to O(|R|d) where |R| ≪ n² means lower inference costs and energy consumption. RCE can run on standard CPUs without requiring expensive GPU infrastructure for many applications.
What industries benefit most from RCE?
Regulated industries with high reliability requirements: Healthcare (clinical decision support, drug interactions), Legal (contract analysis, compliance checking), Finance (risk assessment, regulatory compliance), and Enterprise AI platforms adding reliability layers to existing LLM products. Any application where hallucinations create liability or regulatory risk.
How do I get started?
Start with our Free plan (1,000 verifications/month) to test RCE with your use case. For production deployment, choose Pro (€49/mo), Pro Plus (€89/mo), or Pro Max (€149/mo). Enterprise customers can request custom on-demand solutions with on-premise deployment, custom coherence modules, and dedicated support. Contact us to discuss your needs.
Can I read the research paper?
Yes! The peer-reviewed paper is publicly available at DOI: 10.5281/zenodo.17360372. It provides the complete theoretical foundation, mathematical framework, experimental validation, and complexity analysis. The paper was published October 15, 2025, and establishes the academic basis for RCE's deterministic coherence optimization approach.
Get Started

Request API access or schedule a demo

Contact

Email

is.sialyen@gmail.com

Response: <24 hours

Research

Ismail Sialyen, Founder

IKAMI Research

DOI: 10.5281/zenodo.17360372

© 2025 IKAMI Research — ⟨RCE⟩ Relational Cognitive Engine — i-Depot Protected