See the Blast Radius Before You Ship
Outages caused by dependency impacts nobody saw coming are preventable. ChangeSafe maps direct and transitive impact across services before a change ships.
Enterprise AI
AI in SDLC
Most teams discover the true impact of a code change only after it's in production. ChangeSafe scans your repositories and creates a live architectural map, so you can understand dependencies, predict the blast radius, and ship with confidence. See the demo at flentas.com/resources/events/on-demand/changesafeai.
The Reality
Do You Know What That Change Will Break?
Most teams discover the true impact of a code change only after it's in production.
Architecture diagrams are stale
The picture on the wiki describes the system as it was two years and forty engineers ago.
Blast radius is found in production
A small change ships, and only the incident reveals which services depended on it.
Tracing dependencies burns hours
Engineers grep across repositories by hand to answer a question the codebase should answer itself.
Knowledge leaves with the engineer
The only person who understood the integration layer resigned, and nothing was written down.
PR reviews can't see the whole graph
Reviewers approve changes within one repo without seeing the callers in another.
AI coding agents work blind
Copilots and agents crawl raw files, burn tokens and hallucinate dependencies that don't exist.
Key Benefits
Codebases Outlive the People Who Wrote Them
Architecture diagrams go stale within months, documentation lags behind what's actually running, and the dependencies that matter most live inside the codebase itself — not in any design document anyone kept up to date.
Stop Losing Hours to Manual Tracing
Engineers lose hours a week manually tracing dependencies that a graph can answer in seconds. Search a table like orders_db.payments and see all consuming services in seconds.
Catch Drift Before a Human Opens the PR
Production incidents from resource consumers nobody knew were connected are avoidable. ChangeSafe maps every diff onto the graph and scores risk as High, Medium, or Low before review.
Keep Dependency Knowledge When the Engineer Leaves
Tribal dependency knowledge leaves the company the moment the engineer who held it does. ChangeSafe keeps one live, verified graph everyone — and every AI tool — can query.
Proof Points
Measured Against Real Codebases, Real Dependencies and Real Engineering Workflows
<1 hrFor a first analysis, even on multi-million-line codebases
7xFewer tokens per AI query versus a raw-file crawl
4Languages supported today: Java, TypeScript, C#, and Python
100%Self-hosted — code never leaves your network
How It Works
Four Ways In, One Verified Graph
The UI, the AI chat, PR review, and your own MCP agents all render from the same parsed graph. No second source of truth, no hallucinated dependencies.
- 1
Impact Analysis: See the Blast Radius Before You Commit
Search a table like orders_db.payments and see all consuming services in seconds, not a week of tracing. Works across language boundaries — direct and transitive callers, by service and by call distance. Filters by call depth or annotation, and exports as CSV, JSON, PNG, or SVG.
- 2
AI Chat: Ask the Graph, Not the Model
The assistant calls the same tools as the UI — impact analysis, path finding, transaction graph — so answers come from your actual parsed code. Runs on Claude, via Anthropic or your own AWS Bedrock account, with an OpenAI fallback. Tool calls are logged for cost tracking and auditability.
- 3
AI Pull-Request Risk Review: Risk-Scored Before Review
On every PR, ChangeSafe maps the diff onto the graph, aggregates downstream impact, and flags architecture drift before a human reviewer opens it. A four-stage pipeline — diff mapping, impact aggregation, drift detection, and risk scoring — with human confirmation built in. Merge is blocked until critical dependencies are confirmed.
- 4
MCP Server: Plug Your Agents Straight Into the Graph
Claude Code, GitHub Copilot, and Cursor connect to the same verified graph instead of crawling raw files, using scoped, audited access keys issued per client, per team — up to 7x fewer tokens per query than a raw-file crawl.
Technology Stack
Technologies & Tools We Use
Languages Analyzed
Java, TypeScript, C#, and Python today, with framework-aware parsing for Spring, Hibernate, .NET, Django, FastAPI, Flask, SQLAlchemy, Alembic, and Celery.
External Integrations Mapped
Every AWS, Azure, and GCP service your code calls, detected straight from source.
AI Model Layer
Runs on Claude via Anthropic or your own AWS Bedrock account, with an OpenAI fallback for the AI chat and PR risk review.
Agent & IDE Integrations
Cursor, Claude Code, GitHub Copilot, and other MCP-aware agents query the graph directly through the ChangeSafe MCP server.
Deployment
Self-hosted and air-gap ready. Deploy in your environment in an afternoon; the license validates offline, so source code never leaves your network.
Export & Handoff
Exports as CSV, JSON, PNG, or SVG for review, documentation, or handoff to another team.
Case Studies
View all client success storiesWhere AI in SDLC Makes a Difference
Modernization and Migration
The actual dependency map plus a full cloud-service inventory on the first analysis run, instead of weeks of interviews and whiteboards.
Modernization
<1 hrTo a Full Dependency Map on First Scan
On a first analysis run, even on multi-million-line codebases, teams get the actual dependency map plus a full cloud-service inventory — instead of weeks of interviews and whiteboards.
De-Risking Changes
See every downstream dependent before refactoring a core class, and let PR review flag drift before a human opens it.
Incident Response
Lower MTTRTransaction Tracing Shows the Failure Path
When something breaks, transaction tracing shows exactly how a request moved through the system — cutting the time spent guessing which service actually failed.
AI Coding Agents
Cursor and Claude Code query the graph as context, so generated code fits the architecture that's actually there.
Onboarding
Day OneNew Engineers See the Real Architecture
New engineers navigate the real, live architecture on day one instead of booking a knowledge-transfer session with the one person who remembers how it works.
Developer Onboarding
New engineers see the whole system on day one instead of booking a knowledge-transfer session with the one person who remembers it.
“We'd had three outages in a quarter from changes nobody realized touched a shared service. We ran ChangeSafe against our monorepo and had a live dependency graph in under an hour — self-hosted, so security signed off without a fight. The PR risk review alone would have caught two of those three incidents before they shipped.”
Director of EngineeringEnterprise SaaS Platform, India
Pricing
Start With a Scoped Pilot, or Deploy Full Year One
Run ChangeSafe against one or two of your real repositories to validate analysis quality, AI accuracy, and team fit before a full-year commitment.
- Full architecture map for the piloted repositories
- Impact analysis and AI chat access
- PR risk review on live pull requests
- Self-hosted in your environment
Self-hosted across your codebase, with every capability from interactive architecture maps to MCP-connected AI agents.
- All repositories, all supported languages
- AI chat, PR risk review, and MCP server access
- Air-gap ready, offline license validation
- Scoped, audited access keys per team
What's Next
Where This Fits in Your Journey
One engagement is one stage. Here is what usually comes before and after, so the next step is always clear.
Agentic AI Solutions
Build and run production agents with governance and cost under control.
Explore Agentic AI SolutionsAI in SDLC
Know what a change will break before it ships.
Application and Database Modernization
Clear the application and database debt that migration alone does not solve.
Explore Application and Database ModernizationDevOps and Automation
Make every release a routine, low-risk event.
Explore DevOps and AutomationGet Started
Stop Guessing What Your Code Does
Get a navigable map of your architecture, running on your own infrastructure. Tell us about your repositories, your deployment constraints, and what you're trying to ship, and we'll come back within one business day with a scoped demo.
- AWS Advanced Consulting Partner
- AWS Managed Service Provider
- 96.5% Client Retention