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LLMs alone are not enough

LLMs have intristic limitations that make them unreliable.

Unpredictable Results

LLMs produce different outputs for the same prompt, complicating debugging and collaboration.

Surface-Level Understanding

AI can generate code that looks right but fails in practice, lacking true comprehension.

Missing Context

LLMs struggle with broader project context, creating friction in complex codebases.

Hidden Bugs

Tokenization quirks can introduce subtle issues that evade immediate detection.

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Our Solution: AI + Guardrails

Benchify combines AI’s speed with deterministic safeguards to ensure code is reliable, secure, and maintainable.

Static Analysis

Automated scanning catches errors early running code.

Program Synthesis

Generate correct-by-construction code from specifications.

Formal Methods

Mathematical verification proves critical code sections work as intended.

Compiler Techniques

Advanced methods optimize and correct common AI errors automatically.

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Tools for Confidence

Deploy with confidence whether you’re human or AI.

Review

Deep code analysis through execution testing.

Repair

Fix AI-generated code problems automatically.