Context-Aware AI Diligence

Technical due diligence that reads the codebase in context.

CodeDD doesn't just scan — it interprets. Multi-agent AI validates security findings, reviews code file by file, maps architecture and engineering signals, and synthesizes an evidence-backed report your deal team can defend in the data room.

Executive Health IndicatorsLive analysis

87/ 100

Strong — context-validated, minor remediation flagged

Architecture91
T3 · 5 modules mapped · 2 bottlenecks
Engineering health82
14 devs · 43% innovation · 1 domain at risk
Code quality84
Est. 4.5 days remediation · 28% debt ratio
Security & supply chain78
0 critical · 1 high CVE · 3 license flags

Every score links to underlying findings — not a black-box rating.

Why CodeDD

Context-aware analysisMulti-agent validationIC-ready synthesis

Built for

VC & PETech AdvisorsTech FoundersPortfolio CTOs

Context-aware by design

AI that understands what it's looking at — and why it matters to the deal

Every assessment runs with repository, portfolio, and domain context. Findings are validated, scored, and narrated — not exported as raw scanner output. Start with what differentiates CodeDD; security baselines are covered too.

ArchitectureReads the system in context

Architecture & Scalability Assessment

AI maps 300+ technology patterns, classifies a scalability archetype (T1–T5), and profiles each repo's estate role — so you judge fit for the growth thesis, not just what's in the tree.

Scalability archetype (T1–T5) with scored dimensions
Stack, deployment & data-flow pattern detection
Portfolio estate map with choke-point highlighting

Scalability IndexAbility of the platform to handle accelerated growth

T3/5ARCH. TIERMID-SCALE SOA

Operational index: 74/100Safe growth headroom: 1.5×

Worker queue single-consumerShared DB connection pool

2 bottlenecks identified — drill down to module map & estate roles

Stack & module mapDetected from codebase — not self-reported

5MODULES

Estate roles: Core Business, PlatformTech signals: 4 detected

PostgreSQLRedisDockerGitHub Actions

Click any module to see coupling edges and data-flow dependencies

AI-Native ScoreSix-pillar assessment rolled up portfolio-wide

72/ 100AI AUGMENTED

Repos with AI signal: 8/12OpenAI API integrations: 6 reposCustom model endpoints: 2 reposVector / RAG pipelines: 1 repo

Product embedding: API wrappersMLOps: not detectedNo-AI-signal: 4 repos

AI depth concentrated in API integrations — drill down per repo for IP classification

AISeparates signal from AI hype

AI-Native Assessment

Distinguish wrapper apps from genuine AI IP — scored across six weighted pillars and rolled up portfolio-wide for the questions IC actually asks about AI depth and readiness.

AI-Native Score (0–100) across six pillars
Product vs augmented vs exploratory classification
ML IP depth map — core assets vs integrations

TeamGit history, interpreted for deals

Development Activity & Engineering Health

Commit history read in deal context — AI classifies intent, surfaces bus-factor risk, and splits innovation from maintenance. The engineering signal a financial model cannot produce.

AI-classified commits (feature, fix, refactor, upkeep)
Bus-factor & key-person dependency KPI
Innovation rate and velocity trends

Innovation RateAI-classified commits — last quarter

43.1%INNO / 56.9% MAINTFAIR BALANCE

Classified commits: 2,847Velocity vs prior Q: -43%Active developers: 14

Team is shipping features, not just fixing bugs — see commit breakdown by intent

Key Person DependencyPortfolio file ownership (all repos)

37%MEDIUM RISK

Bus factor: 1/4 domains at risk

Auth 62%Payments 41%

Single developer owns majority of auth module — high exit risk

Code Health ScoreOverall technical quality and maintainability

72/ 100GOOD

Est. remediation: 4.5 daysTechnical debt ratio: 28%

Click to see breakdown by module and debt category — per-file AI review behind each score

Key Person DependencyOwnership concentration from git history

37%MEDIUM RISK

Bus factor: 1/4 domains at risk

Frontend 100%API 15%

Single developer owns majority of frontend — drill down to file-level ownership

QualityPer-file AI review at scale

Code Quality & Technical Debt

Every file reviewed for maintainability, complexity, smells, and debt — synthesized into a seven-dimension quality model and a Code Health Score with remediation cost in engineering days.

7-dimension quality model (0–100 per repo)
Per-file AI review with drill-down, no source exposure
Technical-debt cost estimate in engineering days

SecurityFindings validated, not just flagged

AI-Validated Application Security (SAST)

Static analysis is the starting point — a multi-agent pipeline then gathers evidence, runs taint analysis, and confirms before a finding reaches your report. Fewer false positives, defensible severity.

Multi-agent validation with evidence gathering
OWASP Top 10 mapping & business-impact triage
Line-anchored findings with remediation guidance

Validated Security FindingsMulti-agent pipeline — evidence before report

0Critical

1High

4Medium

9Low

A03 InjectionA01 Broken AccessA07 Auth Failures

middleware/auth.py:142SQL injection path — validated by multi-agent pipeline

Validated: 3/5 flagged issues

3 of 5 flagged issues confirmed after evidence gathering — 2 invalidated as false positives

SecurityDependencies reconciled to reality

Software Composition Analysis (SCA) & Supply Chain

Manifests reconciled against actual imports — not just declared packages. Full SBOM, CVE exposure with CVSS, license policy flags, and transitive supply-chain risk in one inventory.

Manifest + import reconciliation across ecosystems
CVE scanning with CVSS, CWE & patch guidance
OpenSSF Scorecard & license compliance rollup

CodeDD Advisor

Every view includes a context-aware AI copilot — it knows which audit, portfolio tab, or metric you're looking at and answers with evidence, not generic guidance.

See detailed features list

Ask the codebase anything — in context.

See a live assessment on a real repository: validated findings, AI-synthesized executive summary, and the context-aware advisor answering questions on the metrics in front of you.