Setsumei / on-demand codebase intelligence

AI generates more changes. Understand what they could affect.

Setsumei maps how your code connects and prioritizes potential risks. Explore the results in a local scan brief, or let a coding agent query the analysis through MCP before planning an edit.

ON-DEMAND ANALYSIS · RANKED FINDINGS · LOCAL BRIEF · MCP ACCESS
SETSUMEI / SCAN BRIEFSYNTHETIC DEMO DATA
Setsumei scan brief showing a prioritized review queue with synthetic demonstration data

SETSUMEI SCAN BRIEF · DEMONSTRATION DATA

The review bottleneck

More change, same need to understand the system.

Teams already combine source reading, IDE navigation, static analysis and expert knowledge. As proposed changes multiply, assembling that context for the next question takes experienced attention.

CONTEXT ACROSS TOOLSContext takes work to assemble.
HUMAN CHANGEAGENT CHANGEDEPENDENCY CHANGE
FINITE REVIEW CAPACITYReconstruct contextnavigate · ask · inspect
OUTCOMEMore context gathering
WITH SETSUMEIReuse a structural starting point.
ANALYZED CODERELATIONSHIPSVISIBLE UNKNOWNS
REVIEWER WORKFocus investigationinspect · test · decide
OUTCOMEMore focused review
Inside the scan brief

See what deserves a closer look and why.

A finding in a widely used component may deserve earlier attention than the same pattern in isolated code. The brief brings priority, relationships and supporting source into one place to investigate.

Setsumei demonstration: repository brief with a prioritized review queue and coverage context
Current scan brief · synthetic demonstration data
Enlarge the example brief

Walk through the example →

Useful beyond the first scan

Reuse the same context for the next question.

The next task does not have to begin with reconstructing every relationship.

Use the analysis to get oriented, prioritize potential issues, document architecture or plan a refactor. Engineers explore the report and local brief; compatible coding agents query dependencies, findings, source and freshness through MCP.

The core static analysis is deterministic and builds the relationships and candidate findings without an LLM or model API key. An optional AI-assisted investigation step calls a language model to examine selected candidates. Refresh after relevant changes so the context matches the code you are working on.

Explore Setsumei →See MCP workflows →

Two products / different review questions

Understand the codebase. Test the agent's behavior.

Setsumei provides reusable codebase analysis for engineers and coding agents. RunQC separately tests AI-agent behavior through a permitted endpoint. Choose the product for the question you need to answer.

Setsumei and RunQC →
RUNQC / AGENT QUALITY CONTROL

RunQC exercises a permitted AI-agent endpoint across behavioral, security, reliability, and domain-specific checks, then returns the interactions and results for human review.

Explore RunQC ↗
ANALYZED CODEBASE Setsumei
STRUCTURE structure + findings
AGENT@ENDPOINT RunQC
BEHAVIOR test interactions
INSPECTABLE · RETAINED · HUMAN-OWNED
TEAM REVIEW → NEXT ACTION
Discuss an evaluation

Bring one codebase question.

Discuss a local evaluation of the scan, browser brief and workflow your team needs, from understanding unfamiliar code to planning the next change.

Working with client codebases? See the services-firm use case.

Setsumei demonstration

Current Setsumei GUI with demonstration data. Press Escape or Close to return. At actual size, scroll to inspect.

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