Confidently Serve AI-Generated Code at Enterprise Scale

The Trust Layer for AI-Generated Code

Deterministic methods to analyze AI-generated code, with explainable AI for monitoring, diagnostics, security, and compliance.

See the Product
AI Generated Code Infrastructure as Code Runtime Metrics, Alerts & Logs Deterministic Discovery Digital Twin Predictive Monitoring Reduced Downtime Hypothesis Validation Root Cause Analysis Realtime Security New Exploit Identification

The Gap Between Speed and Trust

Enterprises face a critical gap between development speed and code trustworthiness.

Speed Pressure

  • Enterprises seek to deliver new digital experiences at unprecedented speed to be competitive
  • There is pressure to leverage LLMs for accelerated code generation
  • Humans can not keep up with comprehending and assessing AI-generated code

Security & Quality Concerns

  • AI-generated code changes after initial development are frequently not accurate
  • AI-generated code may contain hidden vulnerabilities
  • Compliance and auditability gaps create regulatory risk

Deterministic Analysis for AI-Generated Code

The Confiserve solution: deterministic analysis of AI-generated code, with explainable AI across four pillars — monitoring, diagnostics, security, and compliance.

Digital Twin Monitoring

Dynamic twins for every user experience, tracking runtime anomalies.

Intelligent Diagnostics

Multi-hypothesis analysis with evidence-based validation.

Security Analysis

Contextual analysis of findings from existing enterprise security tools.

Audit & Compliance

Full trail of all AI-generated code modifications.

Proactive Monitoring of User Experiences

Dynamic code change tracking and monitoring with runtime metrics, alerts, and logs.

01

Inventory

Catalogs the enterprise's entire portfolio of user experiences and constructs a dynamic digital twin for each.

02

Monitor

Actively monitors runtime environments for both operational and security anomalies in real time.

03

Alert

Issues high-fidelity alerts for emerging problematic trends before they escalate into production failures.

Enables impact assessment and higher accuracy for subsequent LLM-driven changes and reduces downtime.

Evidence-Driven Root Cause Analysis

From alert to resolution — evidence-driven root cause analysis.

01

Detect

Anomaly identified in runtime environment.

02

Hypothesize

Generate multiple diagnostic hypotheses.

03

Validate

Test each against real-time & historical evidence.

04

Resolve

Actionable insights for rapid corrective action.

Enables fast root cause analysis and reduces mean time to recovery.

Contextual Security Analysis

Reduce false positives, identify precise mitigation actions, and shorten the time for breach analysis.

01

SAST Integration

Ingest findings from existing SAST tools and add contextual analysis to reduce false positives.

02

DAST Integration

Correlate DAST results with code context to identify precise mitigation actions.

03

Continuous Monitoring

Continuously analyze new findings as security tools detect emerging threats.

04

Pen Test Integration

Enrich pen test reports with code-level context to shorten breach analysis time.

Integrate with current security infrastructure and perform contextual analysis to identify vulnerabilities and explainable evidence of exploits.

Complete Transparency

Complete transparency for every AI-generated code modification.

Modification Tracking

Every AI-generated code change is recorded with full context — who, what, when, and why.

Regulatory Compliance

Meets audit requirements for regulated industries with structured, exportable evidence trails.

Development Transparency

Stakeholders gain clear visibility into how AI contributes to the codebase over time.

Ensures transparency and enables compliance.

VeoDiagram — Evidence-Backed Mind Maps for End-to-End Flows

VeoDiagram is the first product implementation of the Confiserve vision. It analyzes code across multiple repositories and builds mind maps for every end-to-end user flow — with every node backed by evidence from the source code itself.

Understand what happens when users invoke a digital experience — in minutes, not days.

Humans and AI agents both struggle to reason about modern systems where one user action touches dozens of services across multiple repositories. VeoDiagram turns that complexity into a navigable map.

Each node is a verifiable claim — a service, a database operation, an API call — linked back to the exact lines of code that produced it. Cut mean time to resolution during debugging and give your AI assistants the context they need to be accurate.

Built for
Humans & AI agents debugging complex systems
Scope
Multi-repository, end-to-end flows
Trust
Every node backed by source evidence
Outcome
Lower mean time to resolution
Step 01 · Point at your code

Start with any local folder or GitHub repository

Open VeoDiagram and point it at the codebase you want to understand. No GitHub account required for local analysis — the tool meets your code where it lives.

  • Analyze local folders or remote GitHub repositories
  • Visualize architecture as interactive diagrams
  • Export results as SVG for docs and presentations
VeoDiagram dashboard — visualize your code architecture
Step 02 · Inventory the codebase

An early scan shows you what you're working with

Before deep analysis, VeoDiagram inventories the project — counting source files, mapping languages, and surfacing operational and security signals so you know what to expect.

  • Catalog every source file across the repository
  • Break down by language and framework footprint
  • Flag operational and security context up front
VeoDiagram early project analysis with file counts and language breakdown
Step 03 · Deep analysis in parallel

A swarm of specialized analysis agents goes to work

Multiple agents analyze the codebase in parallel — each one specialized for a different facet of the system. Together they assemble the deterministic picture that downstream AI assistants and humans both rely on.

  • Schema Analyzer maps database structure
  • Call Graph Analyzer traces code execution paths
  • Import Graph Analyzer builds module dependency maps
  • E2E Flow Analyzer stitches it all into user-visible flows
VeoDiagram swarm of analysis agents working in parallel
Step 04 · The payoff

End-to-end mind maps where every node points back to the code

Trace any user-facing flow — like "Permanent Delete" — from request entry point through services, queues, and database operations. Click any node and the evidence panel shows the exact file, function, and line that justifies it.

  • Visualize complete user journeys across repositories
  • Inspect each node's evidence — file paths, operations, line numbers
  • Copy evidence directly into incident tickets or AI prompts
  • Cut mean time to resolution during debugging
VeoDiagram end-to-end mind map with code evidence panel
Equips humans and AI agents with grounded, evidence-backed maps — the foundation for faster debugging and higher-accuracy AI-generated changes.

Enterprise Value Delivered

Enterprise value delivered through deterministic code analysis.

Faster Root Cause Analysis

Rapidly diagnose and fix production failures with evidence-driven multi-hypothesis analysis.

Impact Analysis

Understand the ripple effects of code changes before they reach production through digital twin simulation.

Better LLM Outputs

Higher accuracy when using LLMs to generate new features, informed by comprehensive code understanding.

Continuous Security

Amplify the value of existing security tools with contextual analysis of SAST, DAST, and pen test findings.

Meet the Founder

The vision and leadership behind Confiserve.

Mudit Tyagi, Founder of Confiserve

Mudit Tyagi

Founder & CEO

Building the trust layer for AI-generated code — bringing deterministic analysis, continuous monitoring, and full auditability to enterprises adopting AI-driven development.

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Why We're Building Confiserve

A note from the founder — on change windows, lost sleep, and what AI finally makes possible.

“At every startup where I helped take an early product out of the lab and into customer environments, I tracked every single change window. During each one, tens — sometimes hundreds — of people would get on a call to make sure every application kept working after the change. No one slept well. Stress was the default mode.”

The Pain

Change windows ran on adrenaline and headcount

Every release was a high-wire act. Hundreds of people watching dashboards, waiting to find out what broke. Recovery meant late nights, paged engineers, and a slow manual scramble to correlate signals across services that no one fully understood end to end.

The Shift

AI now reasons across code, configs, logs, and metrics

What was once impossible is now within reach. Modern AI performs contextual analysis across code, configurations, runtime logs, and metrics together — making it possible to understand a priori exactly how every digital experience an enterprise delivers actually works.

The Vision

Agents that monitor, hypothesize, validate, and report

We're building agents that monitor every element required for end-to-end delivery of a digital experience, hypothesize why a problem is occurring, validate the hypothesis against runtime data in a contextually relevant way, suggest solutions, and report on whether the solution actually worked.

“Let AI do what it's good at. Let humans sleep better — and focus on the creative work.”
— Mudit Tyagi, Founder & CEO

Move Fast.
Stay Secure.

Confiserve enables enterprises to embrace AI-driven development with confidence — backed by deterministic security analysis, continuous monitoring, and full auditability.

C O N F I S E R V E