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ZipherCode LOGIC — AI Reasoning & Reliability
ZipherCode · AI Reasoning & Reliability

LOGIC

Improves AI Reasoning & Reliability

A neural reasoning layer designed to improve how existing AI models discover, learn, process, validate, and connect information — significantly reducing hallucinations and increasing the reliability of AI-generated information.

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Hallucination reduction — same LLM
94 %

Reduction in hallucinations — on the same LLM, with no model retraining required.

Baseline 18 hallucinations / 100 tests
With LOGIC 1 hallucination / 100 tests
Same LLM · No retraining · Software only 94% Reduction
ZipherCode LOGIC — What It Is
What It Is

A reasoning layer that makes
AI dramatically more reliable

LOGIC enhances the neural reasoning layer within existing LLM architectures — improving how models process, validate, and connect information without requiring model retraining or infrastructure changes.

01

Improves how models process information

Existing LLMs process information through attention mechanisms that can conflate, misweight, or lose context across long sequences. LOGIC adds a structured processing layer that ensures information is handled with greater fidelity — reducing the noise that leads to unreliable outputs.

02

Validates outputs before they are returned

Hallucinations occur when a model generates plausible but incorrect information without self-correction. LOGIC introduces a validation pass that checks generated outputs against source context — flagging and correcting inconsistencies before they reach the user.

03

Connects information across reasoning chains

Complex queries require models to chain multiple facts, inferences, and sources coherently. LOGIC structures the reasoning chain — ensuring that intermediate conclusions are grounded, that contradictions are surfaced, and that the final output reflects a coherent path from evidence to answer.

Without LOGIC

Standard LLM operation

18 hallucinations per 100 outputs
Unvalidated reasoning chains
Information gaps filled with plausible fiction
High variance in output reliability
With LOGIC

Same LLM — dramatically more reliable

94% reduction in hallucinations
Validated, grounded reasoning chains
Contradictions surfaced and corrected
Consistent, trustworthy AI outputs
Key Insight

LOGIC works within existing LLM architectures — no retraining, no model replacement, no infrastructure overhaul. It's a software layer that makes any model significantly more reliable from day one.

ZipherCode LOGIC — Benchmarks
Performance Benchmarks

The same LLM.
94% fewer hallucinations.

Measured against a standard LLM baseline, LOGIC delivers a dramatic reduction in hallucination rate — without modifying the underlying model, retraining, or changing any infrastructure.

Technology Baseline With ZipherCode Result
LOGIC LLM without LOGIC Same LLM + LOGIC
Baseline 18 / 100 tests
With LOGIC 1 / 100 tests
94% reduction
Hallucinations per 100 outputs — baseline vs LOGIC
Without LOGIC · Baseline 18 hallucinations per 100 outputs
■ hallucination   □ correct output (40 shown)
With LOGIC · Verified 1 hallucination per 100 outputs
■ hallucination   □ correct output (40 shown)
Results measured on identical LLM configurations across a standardized hallucination test suite. Hallucination rate defined as factually incorrect or fabricated outputs per 100 standardized prompts. All tests conducted with LOGIC reasoning layer active vs. standard LLM inference as baseline. Performance may vary by model architecture, prompt type, and domain.
ZipherCode LOGIC — Market & Commercial
Market & Commercial

Built for every organization
running AI at scale

LOGIC is a software layer that integrates with existing LLM infrastructure — making it applicable to any organization deploying AI models in production environments where reliability matters.

Target Licensees

Organizations where AI reliability is critical

  • LLM developers
  • AI platforms
  • Hyperscalers
  • Enterprise AI companies
  • Search and autonomous systems
Commercial Models

Flexible structures for every deployment type

  • Technology licensing
  • Platform integration
  • API licensing
  • Enterprise agreements
94% Reduction in hallucinations
on the same LLM
$0 Model retraining or
infrastructure cost
5+ Licensee categories across
the AI ecosystem
Licensing Inquiries

Ready to make your AI
dramatically more reliable?

We're working with LLM developers, AI platforms, and enterprise AI companies. If LOGIC fits your stack — let's talk.

Get in Touch →