📊 Dashboard

—

Total Projects

—

COBOL Programs

—

JCL Jobs

—

High Risk Modules

Recent Analyses

Project Name	Programs	High Risk	Avg Score	Analysed	Actions
📂 No projects yet. Use Upload & Analyse in the sidebar to start.

📤 Upload & Analyse

Upload Mainframe Source ZIP

🏦

For regulated industries only: Mainframe Discovery is designed exclusively for Banking, Insurance, and Public-Sector mainframe environments. Only ZIP archives containing Mainframe source code (COBOL, JCL, BMS, Copybooks, DB2 SQL) are accepted. Game files, desktop applications, web projects, or general-purpose source code are not supported.

📦

Drop your Mainframe ZIP here

Analysed: COBOL (.cob/.cbl) · JCL (.jcl) · BMS (.bms/.map) · Copybooks (.cpy) · DB2 SQL (.sql)

Supported File Types

COBOL
.cbl .cob .cobol

JCL Jobs
.jcl .jclproc

BMS Maps
.map .bms

Copybooks
.cpy .copy

DB2 SQL
.sql .ddl

📊 Graph Exports
After analysis: download a PNG image for quick sharing, or a SVG vector file for detailed interactive viewing (zoom in, hover for tooltips, open in browser or draw.io).

📁 Projects

All Projects

Project Name	Programs	JCL Jobs	Avg Score	Status	Analysed	Actions
Loading…

📚 Pattern Dictionary

Pattern Dictionary defines business meanings for COBOL field names, dataset names, and program prefixes used during analysis.

Patterns

📚

Click Refresh to load patterns.

📬

Custom Pattern Dictionary — Managed Service

Tailor the analysis engine to your organisation's specific COBOL field names, dataset conventions, and domain vocabulary. Send us your pattern list and we will load it into your account within one business day.

1️⃣

Prepare your JSON file

Use the format shown below. One entry per pattern — add as many as you need.

2️⃣

Send it to our team

Email your file to [email protected] with subject line: Pattern Import – [your username].

3️⃣

Patterns go live

We confirm by email once your patterns are active. They will immediately apply to all future analyses.

📄 JSON File Format

[
  {
    "key":           "WKN",
    "category":      "FIELD_TYPE",
    "domain":        "SECURITIES",
    "meaning":       "German Securities Identification Number (Wertpapierkennnummer)",
    "migrationHint": "Map to String(6) — pad with leading zeros if numeric"
  },
  {
    "key":           "ISIN",
    "category":      "FIELD_TYPE",
    "domain":        "SECURITIES",
    "meaning":       "International Securities Identification Number",
    "migrationHint": "Map to String(12) — ISO 6166 format"
  },
  {
    "key":           "GD100A",
    "category":      "FIELD_TYPE",
    "domain":        "SECURITIES",
    "meaning":       "Security status code: 1 = active, 2 = not yet active, 3 = inactive",
    "migrationHint": "Map to enum SecurityStatus { ACTIVE, PENDING, INACTIVE }"
  }
]

🗂 Field Reference

Field	Required	Allowed values / notes
key	✅ Yes	Exact COBOL field / dataset / program prefix name. Uppercase recommended.
category	✅ Yes	`FIELD_TYPE` `COLUMN` `TABLE` `PROGRAM_PREFIX` `DATASET` `JCL_STEP` `STATUS_VALUE`
domain	Optional	Free text, e.g. SECURITIES, INSURANCE, CORE-BANKING. Helps filter patterns later.
meaning	✅ Yes	Human-readable business description. Appears in analysis reports.
migrationHint	Optional	Java / Spring Boot migration note, e.g. target type, enum mapping, validation rule.

✉️ Send Patterns to [email protected]

⚡ Self-service pattern management is coming soon — we typically respond within one business day.

👥 User Management

Users

Username	Full Name	Email	Role	Status	Last Login	Actions
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🔑 Enterprise Licenses

Manage your Enterprise license keys for on-premise deployments.

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⚙️ My Profile

Account Information

ℹ️ About Mainframe Discovery Tool

🖥️

Mainframe Discovery Tool

Name: Mainframe Discovery Professional
Brand: ilumeny
Price: 990 EUR
Availability: InStock

Version 1.0.0-MVP · Java 17 · Spring Boot 3.2 · PostgreSQL 15

An Advanced platform that automatically extracts, analyses, and documents business rules, dependencies, and risk profiles from legacy Mainframe source code — producing business-ready specifications that bridge the gap between COBOL subject-matter experts and modern Java/Spring Boot development teams.

🎯 Business Problem

Legacy Mainframe systems contain decades of business logic embedded in COBOL programs, JCL batch jobs, BMS maps, and DB2 schemas. This knowledge is often held only in source code — no current documentation exists.

Migration projects fail or stall because:

Business rules are invisible in raw COBOL source
Dependency chains are undocumented and complex
Risk assessment per module is manual and slow
Handover from Mainframe experts to Java teams is error-prone

✅ What This Tool Does

The Mainframe Discovery Tool automates the complete analysis pipeline from raw ZIP archive to production-ready specification documents:

Parses all Mainframe file types from a single ZIP upload
Generates a 10-section business spec per module ()
Calculates a risk score (0–100) per COBOL program
Renders a full dependency graph (PNG + interactive SVG)
Exports PDF and Excel reports ready for stakeholder review

⚙️ Analysis Pipeline

📦

ZIP Upload

COBOL · JCL · BMS
Copybooks · SQL

→

🔍

Parsing

Source extraction
Dependency mapping

→

🤖

AI Analysis

Business rules
Field mappings

→

📊

Risk Scoring

0–100 per module
HIGH / MED / LOW

→

📄

Export

PDF · Excel
PNG · SVG

📋 10-Section Specification

1	Overview	Program name, type, purpose, UI, data source
2	Business Use Case	Actor, description, goal, trigger
3	Input Data	COBOL fields, working storage, validation rules
4	Database Access	DB2 tables, SQL statements, key columns
5	Business Rules	Numbered rules derived from source code
6	Data Flow	Input → Processing → Output pipeline
7	Field Mapping	Screen/JCL → Working Storage → DB Column → Java DTO
8	Error Cases	SQLCODE handling, MQ error codes, VSAM status
9	Technical Dependencies	External systems, JCL jobs, copybooks, queues
10	Ready for Implementation	Developer checklist for Java/Spring Boot migration

📁 Supported Mainframe File Types

🟦	COBOL	.cbl .cob	Batch programs, online services, validators
🟩	JCL	.jcl	Job Control Language — batch job chains
🟧	BMS Map	.map .bms	CICS 3270 terminal screen definitions
⬜	Copybook	.cpy	Shared data structure definitions
🟫	DB2 Schema	.sql .ddl	Table definitions, foreign keys, indexes
🟪	VSAM	.cbl (FD)	Virtual Storage Access Method file I/O
🔴	MQ	.cbl (MQ)	IBM Message Queue GET/PUT operations
🔵	CICS	.cbl (CICS)	Online transactions with EXEC CICS commands

📤 Export Formats

📑

PDF Report

Multi-page document with cover page, call graph visualisation, program risk table, 10-section business specs, DB2 schema analysis, dead code report, and migration recommendations.

📊

Excel Workbook

8 structured sheets: Programs (risk-coloured), Business Rules, Overview & Use Case, Input & Field Mapping, DB Access & Error Cases, Technical Dependencies, Implementation Checklist, Recommendations.

🖼

PNG Dependency Graph

High-resolution raster image. Colour-coded by node type and risk level. Includes risk score bars and a full legend. Ready for presentations and documents.

⬡

SVG Dependency Graph

Infinitely scalable vector graph. Hover tooltips show full module metadata (risk score, lines, CALLs, copybooks, connections). Editable in Inkscape, draw.io, or Figma.

⚠️ Risk Scoring Model

Each COBOL module receives a risk score from 0 to 100, calculated from a weighted combination of structural complexity indicators:

Factor	Weight
Outgoing CALL count (complexity)	25%
Incoming callers (coupling)	20%
Source code size (lines)	20%
Copybook usage (data coupling)	15%
SQL operation count (DB dependency)	10%
Program type (ENTRY = higher risk)	10%

HIGH 70–100 MEDIUM 40–69 LOW 0–39

🏗️ Technology Stack

Backend	Java 17 · Spring Boot 3.2 · Spring Security + JWT
Database	PostgreSQL 15 (H2 for test profile)
ORM	Spring Data JPA · Hibernate 6
PDF Export	Apache PDFBox 3.x (multi-page, bookmarks)
Excel Export	Apache POI 5.x (8 sheets, risk-coloured)
Graph Export	Java2D (PNG) · SVG XML builder (interactive SVG)
AI Analysis	Azure OpenAI in Europe (EU data centres)
Security	JWT (JJWT 0.12) · BCrypt · Role-based access control
API Docs	SpringDoc OpenAPI 2.3 · Swagger UI
Container	Docker · Docker Compose · Tomcat embedded
Frontend	Vanilla HTML5 / CSS3 / ES6 (no framework, responsive)

🔌 REST API Overview

Method	Endpoint	Description	Access
POST	/api/auth/login	Authenticate and receive JWT token	PUBLIC
GET	/api/auth/me	Get current user info	AUTH
GET	/api/analysis/projects	List all analysis projects	AUTH
POST	/api/analysis/upload	Upload ZIP and start analysis	AUTH
GET	/api/analysis/projects/{id}/export/pdf	Download PDF report	AUTH
GET	/api/analysis/projects/{id}/export/excel	Download Excel workbook	AUTH
GET	/api/analysis/projects/{id}/export/graph	Download PNG dependency graph	AUTH
GET	/api/analysis/projects/{id}/export/graph/svg	Download SVG dependency graph	AUTH
GET	/api/patterns	Read pattern dictionary entries	ADMIN
POST	/api/patterns	Create pattern dictionary entry	ADMIN
GET	/api/admin/users	List all users	ADMIN
POST	/api/admin/users	Create new user	ADMIN
PUT	/api/admin/users/{id}	Update user (role / enabled)	ADMIN
DELETE	/api/admin/users/{id}	Delete user	ADMIN

ℹ️ AI Analysis Note: When an Azure OpenAI key is configured (AZURE_OPENAI_API_KEY), each Mainframe module is analysed by Azure OpenAI in Europe, generating detailed business rules, field mappings, SQL statements, and error handling descriptions. Without a key, a robust static analysis fallback is used — all 10 specification sections are still populated from source code structure.

⬇️ Download

Download the on-premise application package, documentation, and your license key.

📦 Application Package

Complete Docker Compose setup with environment template, Dockerfile, startup scripts, and configuration examples.

📁 mainframe-discovery-v1.0.zip

📁 Includes: docker-compose.yml, .env.template, Dockerfile

🐳 Docker Image

Pull the latest Docker image directly from our private registry using your customer credentials.

                docker pull registry.ilumeny.com/

                  mainframe-discovery:latest

📚 Documentation

Complete PDF guides for installation, configuration, and operation.

⚙️ System Requirements

▸OS: Linux (Ubuntu 22.04+, RHEL 8+) or Windows Server 2019+

▸CPU: 2 vCPU minimum (4 recommended)

▸RAM: 4 GB minimum (8 GB recommended)

▸Disk: 20 GB free space

▸Docker: 24.0+ or Kubernetes 1.28+

▸Network: HTTPS outbound for license validation

▸PostgreSQL: 14+ (bundled in Docker Compose)

Need help with installation?

Your dedicated account manager is available for onboarding support.

Contact Support →

🚀 Setup & Run Guide

🐳

Complete Installation Guide

Docker · Java 17 · Spring Boot 3.2 · PostgreSQL 15 · Linux · Windows · macOS · Cloud

The Mainframe Discovery Tool is distributed as a Docker image and requires PostgreSQL as its database. All deployment methods use Docker Compose for simplicity. Minimum: 2 GB RAM · 2 vCPUs · 10 GB disk.

⚡ Quick Start (Any Platform)

🐧Linux / Hetzner

🪟Windows + WSL 2

🍎macOS M1/M2/Intel

☁️Azure / AWS / GCP

bash

$ git clone https://github.com/your-org/mainframe-discovery.git
$ cd mainframe-discovery/mainframe_discovery
$ cp .env.example .env # Edit with your passwords
$ docker compose up -d --build
✔ Started MainframeDiscoveryApplication in 4.2 seconds
✔ http://localhost:8088/ → ready

🔐 Security Notice

⚠️ Default Credentials Removed for Security For security reasons, default credentials are no longer shown in public documentation.

Enterprise & On-Premise Customers:
Your dedicated account manager will provide secure initial credentials during onboarding.

Cloud Trial Users:
Create your account via the registration form. You will set your own password.

🔒 Security Best Practice: Always use strong, unique passwords (min 16 characters with symbols, numbers, upper/lowercase). Enable 2FA for admin accounts.

📂 Service URLs

🌐 Web UI :8088/

🔌 Swagger API Docs :8088/swagger-ui/index.html

🗄️ pgAdmin (debug) :5050 (--profile debug)

⚙️ Environment Variables (.env)

Variable	Default	Description
ADMIN_USERNAME	your_admin_user	Choose a secure administrator username (avoid "admin")
ADMIN_PASSWORD	your_secure_password	Strong password: min 16 chars, symbols, numbers — NEVER use default values!
JWT_SECRET	generate_with_openssl_rand	Generate: `openssl rand -base64 64` — minimum 32 characters
ANTHROPIC_API_KEY	(optional)	Azure OpenAI API key — leave empty for static analysis fallback
SPRING_DATASOURCE_URL	jdbc:postgresql://postgres:5432/mainframe_discovery	PostgreSQL JDBC connection URL
SPRING_DATASOURCE_USERNAME	your_db_user	PostgreSQL database username (avoid generic names)
SPRING_DATASOURCE_PASSWORD	your_db_password	PostgreSQL database password — use strong unique password!

🐧 Linux Installation (Ubuntu/Debian)

# Step 1 – Install Docker
$ sudo apt-get update && sudo apt-get install -y ca-certificates curl gnupg
# Step 2 – Add Docker repo + install
$ sudo apt-get install -y docker-ce docker-ce-cli containerd.io docker-compose-plugin
# Step 3 – Add user to docker group
$ sudo usermod -aG docker $USER && newgrp docker
# Step 4 – Start application
$ docker compose up -d --build
✔ Application ready at :8088

💡 Wait for the log line: "Started MainframeDiscoveryApplication" before accessing the UI.

🐳 Common Docker Commands

Check container status

docker compose ps

Follow live logs

docker compose logs -f app

Restart after config change

docker compose restart app

Stop all services

docker compose down

Full reset (removes all data!)

docker compose down -v

☁️ Cloud Deployments

🔷 Azure App Service RECOMMENDED

App Service Plan B2 + PostgreSQL Flexible Server. Auto-scaling, managed SSL, custom domains.

🟠 AWS ECS Fargate RECOMMENDED

ECS Fargate + RDS PostgreSQL. Serverless containers, secrets via AWS Secrets Manager.

🟡 GCP Cloud Run RECOMMENDED

Cloud Run + Cloud SQL. Scales to zero, automatic HTTPS on *.run.app domain.

⚙️ systemd Service (Production Linux)

Ensure the application starts automatically on server reboot:

# /etc/systemd/system/mainframe-discovery.service
[Unit]
Description=Mainframe Discovery Tool
Requires=docker.service
After=docker.service
[Service]
WorkingDirectory=/opt/mainframe-discovery/mainframe_discovery
ExecStart=/usr/bin/docker compose up
Restart=on-failure
[Install]
WantedBy=multi-user.target

$ sudo systemctl daemon-reload
$ sudo systemctl enable mainframe-discovery
$ sudo systemctl start mainframe-discovery

🔧 Troubleshooting

Problem	Likely Cause	Solution
ERR_CONNECTION_REFUSED on :8088	Container not running	`docker compose ps` → if not running: `docker compose up -d`
HTTP 403 on API calls	Missing or expired JWT	Login via `POST /api/auth/login` and include `Authorization: Bearer <token>`
DB connection refused	PostgreSQL not healthy	`docker compose ps postgres` → `docker compose logs postgres`
App starts then exits	Missing env var / wrong DB URL	`docker compose logs app` — verify `.env` file and DB credentials
ZIP upload fails with 400	Invalid ZIP or >100 MB	Ensure file is a valid `.zip` archive and under 100 MB
Slow AI analysis	Azure OpenAI API call per module	Normal for 50+ programs (2–5 min). Remove `AZURE_OPENAI_API_KEY` for fast static mode
Out of memory (OOM)	Insufficient container RAM	Increase Docker memory to 4 GB minimum (Docker Desktop → Settings → Resources)

📋 Platform Summary

Platform	Recommended Approach	Setup Time
🐧 Linux	Docker Compose (direct)	10–15 min
🪟 Windows	Docker Desktop + WSL 2	20–30 min
🍎 macOS	Docker Desktop (native arm64)	10–15 min
🔷 Azure	App Service + PostgreSQL Flexible	30–45 min
🟠 AWS	ECS Fargate + RDS	45–60 min
🟡 GCP	Cloud Run + Cloud SQL	30–45 min

📖 Pattern Dictionary Guide

📚

Domain Pattern Dictionary

The Pattern Dictionary maps cryptic Mainframe identifiers — COBOL field names, DB2 table names, JCL step names, program prefixes — to plain business language. Without patterns, the tool reports raw technical names. With patterns, it generates human-readable specifications that stakeholders and developers can immediately understand.

🧩 What Is a Pattern?

A Pattern maps a Mainframe identifier to its business meaning. The tool uses this during analysis to generate readable reports instead of raw COBOL names.

Mainframe Key	Category	Business Meaning
KONTO-SALDO	COLUMN	Account balance — current ledger balance
WP-BUCHUNG	PROGRAM_PREFIX	Securities transaction booking
KUNDEN-ID	COLUMN	Customer identifier — unique CIF number

📂 Pattern Categories

COLUMN DB2 column names, COBOL field names

HIGH

TABLE DB2 table names, VSAM file names

HIGH

PROGRAM_PREFIX COBOL program name prefixes (first 4–6 chars)

HIGH

STATUS_VALUE 88-level condition names, return codes

MED

JCL_STEP JCL step names (EXEC PGM=)

MED

DATASET JCL dataset names (DSN=)

LOW

FIELD_TYPE COBOL PIC clause descriptions

LOW

🔌 API Reference — All Endpoints All require ADMIN JWT token

Method	Endpoint	Description	Use Case
GET	/api/patterns	Retrieve all patterns	Export / backup full dictionary
POST	/api/patterns	Create a single new pattern	Add one missing identifier
PUT	/api/patterns/{id}	Update an existing pattern	Correct meaning or migration hint
DELETE	/api/patterns/{id}	Delete a pattern	Remove obsolete entries
GET	/api/patterns/search?q=	Full-text search across all fields	Avoid duplicate entries
GET	/api/patterns/lookup?input=&category=	Look up meaning for a specific key	Verify mapping before analysis
GET	/api/patterns/domain/{domain}	Filter patterns by domain	Review completeness per domain
GET	/api/patterns/category/{category}	Filter patterns by category	Review all column or table mappings
POST	/api/patterns/batch	Batch import — JSON array of patterns	Initial domain setup / restore backup
POST	/api/patterns/domain-package/wertpapier	Install built-in Securities domain	Hundreds of pre-defined patterns
POST	/api/patterns/domain-package/versicherung	Install built-in Insurance domain	Hundreds of pre-defined patterns

✅ Best Practices

📛 Naming Conventions

Use title-case with hyphens for domains: Credit-Banking, Trade-Finance. For PROGRAM_PREFIX use the first 4–6 characters that identify the functional area.

✍️ Writing Good Meanings

Lead with the business concept: "Credit limit — maximum approved credit line". Avoid abbreviations. For status values describe the business state, not the technical flag.

🔧 Migration Hints

Always reference the Java class and field: "Map to BigDecimal creditLimit in CreditAccount entity. Add @Min(0)". Reference Spring annotations: @Service, @Entity, @Transactional.

🔄 Maintenance Workflow

Export regularly via GET /api/patterns → save JSON → commit to version control. Use one JSON file per domain. After each analysis, review unrecognised identifiers and add missing patterns.

📦 Batch Import Example

Most efficient way to set up a new domain — import hundreds of patterns in one API call:

POST /api/patterns/batch
Authorization: Bearer <token>
Content-Type: application/json
[
{
"key": "KRED-LIMIT",
"category": "COLUMN",
"domain": "Credit-Banking",
"meaning": "Credit limit — max approved credit line",
"migrationHint": "BigDecimal creditLimit, @Min(0)"
},
{ "key": "ZKRED", "category": "PROGRAM_PREFIX", ... }
]
// Response:
{ "imported": 16, "skipped": 0, "errors": [] }

💡 Duplicate keys within the same category are automatically skipped — safe to re-import.

📦 Built-In Domain Packages

📈 Wertpapier (Securities)

Securities trading, depot management, ISIN, WP_BESTAND tables.

WPBUCH · WPSALDO · WPKURS · WPDEPOT · CICSWP01

WP_BESTAND · WP_KURS · WP_DEPOT · WP_FEHLERLOG

🛡️ Versicherung (Insurance)

Policy management, premiums, claims processing, insured persons.

VSPOLIS · VSBEITRAG · VSSCHAD · VSKLIEN

VS_POLIS · VS_KLIEN · VS_SCHADEN · VS_BEITRAG

✅ Built-in packages add patterns without overwriting existing ones. Safe to install even if you have custom patterns for the same domain.

☑️ Domain Completeness Checklist

Before running a project analysis, verify you have patterns for all high-priority categories:

PROGRAM_PREFIX First 4–6 chars of every program family

REQUIRED

TABLE All DB2 tables referenced in SQL

REQUIRED

COLUMN Key fields — IDs, amounts, dates, status

REQUIRED

STATUS_VALUE All 88-level conditions driving business logic

MEDIUM

JCL_STEP All step names in batch job JCL

MEDIUM

DATASET Frequently referenced DSN names

OPTIONAL

💡 Manage patterns directly via the admin page or via the REST API.

💳 Billing FAQ

Common questions about subscriptions, cancellations, and payments.

❓ How do I cancel my subscription?

Go to Billing → Open Billing Portal (Stripe). In the portal, click "Cancel subscription". Your subscription will not renew after the current billing period.

✓ Access continues until the end of your billing period

You will not be charged again, but you keep full access until the date shown in the Billing Portal. You can reactivate at any time before that date.

❓ What happens to my data after cancellation?

Your projects, analyses, and PDF reports are not deleted when you cancel. They remain for 90 days after the subscription ends.

After cancellation you can still view and download existing reports, but cannot start new analyses. If you re-subscribe within 90 days, all data is immediately accessible.

❓ Can I reactivate after cancellation?

Yes. Go to Billing and click Subscribe for any plan. All your previous projects and data are immediately accessible. If your billing period has not ended yet, you can also reactivate in the Stripe Billing Portal without entering payment details again.

❓ What happens if my payment fails?

Stripe automatically retries failed payments up to 3 times over approximately 8 days. You will receive email notifications from Stripe at each attempt.

Day 1 Payment fails → Upload & Analyse temporarily restricted

Days 1–8 Stripe retries. Update payment method via Billing Portal to resolve.

After 8 days Subscription canceled. Subscribe again to restore access.

❓ Can I get a refund?

Monthly subscriptions are non-refundable once a billing period has started. You retain access for the full period you paid for. For exceptional circumstances contact [email protected] within 7 days of the charge.

❓ How does cancellation work for Enterprise?

Enterprise is managed under a signed annual contract. Cancellation terms are defined in your contract (typically 30 days written notice before renewal). Contact your account manager or [email protected]. Since Enterprise runs on your own infrastructure, your data remains on your servers after contract end.

Still have questions about your subscription?

Contact Billing Support →

🔌 API Reference

Click any endpoint to expand it. Your session token is sent automatically. Base URL: https://app.ilumeny.com

💳 Billing & Subscription

Current Plan

Available Plans

Free Trial

14 days — no credit card required

€0/14 days

Up to 3 concurrent projects
50 programs per analysis
PDF + Excel export
PNG + SVG graph export
Static analysis (no AI)
AI-powered business rules
Pattern Dictionary API
White-label PDF reports

Professional

For migration teams & consultancies

€990/month

Unlimited projects & programs
AI-powered analysis (Azure Europe)
DSGVO / GDPR compliant by design
Dead Code Detector
GDPR Scanner — PII auto-detection
Risk Scoring (0–100 per program)
PDF + Excel + PNG + SVG export
White-label PDF reports
Pattern Dictionary (managed)
REST API access for CI/CD

Enterprise

For large enterprises & on-premise

From €2.490/month

Annual commitment: €29.880/year incl. VAT
✓ Flexible payment terms available

Everything in Professional
On-premise deployment (Docker)
SSO / SAML integration
Azure Germany (data residency)
SLA with guaranteed response time
Dedicated account manager
Custom domain pattern packages
Team training & onboarding

*Billed annually (€29.880/year). Quarterly/Semi-annual payment options available.

Illuminate what lives inside
your legacy Mainframe

Everything you need to understand your Mainframe

Detect dead code
before it costs you

Know your GDPR exposure
before the regulator does

From ZIP upload to business specification

Upload your source archive

Automated parsing & analysis

Risk scoring & dependency graph

Download reports

Transparent pricing for every team

Built for enterprise trust

Ready to illuminate
your Mainframe?

📊 Dashboard

📤 Upload & Analyse

📁 Projects

📚 Pattern Dictionary

Custom Pattern Dictionary — Managed Service

👥 User Management

🔑 Enterprise Licenses

Enterprise Subscription Required

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⚙️ My Profile

ℹ️ About Mainframe Discovery Tool

Mainframe Discovery Tool

⬇️ Download

🚀 Setup & Run Guide

Complete Installation Guide

📖 Pattern Dictionary Guide

Domain Pattern Dictionary

💳 Billing FAQ

🔌 API Reference

💳 Billing & Subscription

Free Trial

Professional

Enterprise

Illuminate what lives insideyour legacy Mainframe

Everything you need to understand your Mainframe

Detect dead codebefore it costs you

Know your GDPR exposurebefore the regulator does

From ZIP upload to business specification

Upload your source archive

Automated parsing & analysis

Risk scoring & dependency graph

Download reports

Transparent pricing for every team

Built for enterprise trust

Ready to illuminateyour Mainframe?

Illuminate what livesinside your legacy systems

Welcome back

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Choose a new password

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📊 Dashboard

📤 Upload & Analyse

⚠️ AI Analysis Consent

Analysis Mode:

📁 Projects

📚 Pattern Dictionary

Custom Pattern Dictionary — Managed Service

👥 User Management

🔑 Enterprise Licenses

Enterprise Subscription Required

No Licenses Found

⚙️ My Profile

ℹ️ About Mainframe Discovery Tool

Mainframe Discovery Tool

⬇️ Download

🚀 Setup & Run Guide

Complete Installation Guide

📖 Pattern Dictionary Guide

Domain Pattern Dictionary

💳 Billing FAQ

🔌 API Reference

💳 Billing & Subscription

Free Trial

Professional

Enterprise

Illuminate what lives inside
your legacy Mainframe

Detect dead code
before it costs you

Know your GDPR exposure
before the regulator does

Ready to illuminate
your Mainframe?

Illuminate what lives
inside your legacy systems