CodeBench

AI-powered sandboxed code execution for performance assessment, enabling data analysis and file generation with concrete, verifiable outputs.

The Problem

“AI systems need verifiable code execution, not just text answers”

Organizations face these key challenges:

1

LLM-only systems cannot reliably produce validated analytical outputs

2

Users must manually run and debug generated code outside the product

3

Unsafe execution environments create security and compliance risks

4

Analytical tasks often require iterative repair after runtime failures

Impact When Solved

Turns natural language requests into executable analytical workflowsProduces concrete outputs such as files, charts, tables, and reportsImproves trust through verifiable execution logs and artifactsReduces manual debugging and copy-paste coding by end users

The Shift

Before AI~85% Manual

Human Does

  • •Interpret the analysis request and define the desired output
  • •Write or adapt scripts in notebooks or local environments
  • •Run, debug, and revise code until outputs are usable
  • •Validate results and assemble files, charts, or reports for delivery

Automation

  • •Generate draft code snippets or formula suggestions
  • •Answer questions about analysis methods or syntax
  • •Suggest possible fixes based on described errors
With AI~75% Automated

Human Does

  • •Specify the task, input data, and required deliverables
  • •Review execution results, artifacts, and confidence signals
  • •Approve final outputs for sharing or downstream use

AI Handles

  • •Translate requests into executable analytical workflows
  • •Generate and run sandboxed code to analyze data and create artifacts
  • •Monitor logs, detect failures, and iteratively repair execution issues
  • •Return verifiable outputs such as files, charts, tables, reports, and run records

Operating Intelligence

How it works

Humans set constraints. AI generates options.

Humans choose what moves forward.

Selections improve future generation quality.

Confidence91%
ArchetypeGenerate & Evaluate
Shape6-step branching
Human gates2
Autonomy
50%AI controls 3 of 6 steps

Who is in control at each step

Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

Loop shapebranching

Step 1

Define Constraints

Step 2

Generate

Step 3

Evaluate

Step 4

Select & Refine

Step 5

Deliver

Step 6

Feedback

AI lead

Autonomous execution

2AI
3AI
5AI
gate
gate

Human lead

Approval, override, feedback

1Human
4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

Humans define the constraints. AI generates and evaluates options. Humans select what ships. Outcomes train the next generation cycle.

The Loop

6 steps

1 operating angles mapped

Operational Depth

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