Application Portfolio Discovery

Rapidly scan your entire IT environment with our agentless, machine-assisted discovery method achieving up to 98% accuracy in 2-3 weeks.

Service Overview

Every successful transformation begins with a thorough and accurate discovery. From experience, we know that traditional CMDBs or manually maintained spreadsheets and databases rarely capture a company's infrastructure in a timely or complete fashion. That's why arqitekta has developed a machine-assisted, agentless discovery method that rapidly scans the entire IT environment—without installing agents or disrupting operations.

This highly un-intrusive process inventories all servers, networks, and storage, mapping out logical relationships between systems. Within two to three weeks, we typically achieve up to 98% accuracy of the environment, providing you with a reliable foundation for planning.

By analyzing the communication between production systems and identifying the protocols in use, we can accurately infer application types and integrations, grouping servers into systems and systems into landscapes. This results in a comprehensive, up-to-date map of your application portfolio—giving you the actionable insights needed to guide every phase of transformation that follows.

Organizations that skip or under-invest in discovery typically face 30-50% cost overruns during migration and modernization programs. Inaccurate baselines lead to missed dependencies, unexpected outages, and scope creep that derails transformation timelines. Our discovery service eliminates these risks by establishing a single source of truth for your entire IT estate before any transformation work begins.

Why Traditional Approaches Fall Short

The CMDB Problem

Common Accuracy Gaps

Traditional CMDB Issues:
- 40-60% inaccurate at any given time
- Manual updates lag behind reality
- Inconsistent data entry standards
- No automated dependency tracking

Spreadsheet Limitations:
- Point-in-time snapshots only
- No cross-reference validation
- Version control challenges
- Lacks relationship context

Real-World Consequences

Migration Failures: Missed dependencies cause 35% of migration rollbacks
Budget Overruns: Inaccurate baselines lead to 30-50% cost escalation
Extended Timelines: Rework from bad data adds 3-6 months to projects
Compliance Gaps: Unknown systems create unaddressed regulatory exposure

Agent-Based Discovery Drawbacks

Requires software installation on every target system
Creates security vulnerabilities and attack surface expansion
Performance impact on production workloads
Lengthy rollout timelines across large estates
Ongoing agent maintenance and patching overhead

Our Advantage

Machine-Assisted: Automated accuracy validated by expert architects
Agentless: No footprint, no risk, no performance impact
Comprehensive: Full stack visibility from network to application layer
Fast: Weeks, not months—delivering actionable results in 2-3 weeks

Our Discovery Framework

Phase 1: Preparation & Scoping

Week 1, Days 1-3: Foundation Setting

Environment Scoping

Scope Definition Activities:
- Network segment identification
- IP range documentation
- VLAN and subnet mapping
- Firewall rule review for scan access

Credential Provisioning:
- Read-only service account creation
- SNMP community string configuration
- WMI/SSH credential setup
- API access token provisioning

Stakeholder Alignment:
- Discovery scope sign-off
- Communication plan distribution
- Escalation path definition
- Success criteria agreement

Technical Readiness

Jump server or scan appliance placement
Network connectivity validation
Credential testing across platform types
Scan schedule coordination with operations teams

Phase 2: Active Discovery

Week 1, Days 4-5 through Week 2: Automated Scanning

Infrastructure Scanning

Layer 1 - Network Discovery:
- ICMP sweep for host detection
- ARP table collection from switches
- SNMP polling for device inventory
- DNS zone transfer analysis

Layer 2 - System Profiling:
- Operating system fingerprinting
- Hardware specification collection
- Patch level and version capture
- Running service enumeration

Layer 3 - Application Mapping:
- Port and protocol analysis
- Process-to-port correlation
- Application version identification
- Configuration file extraction

Communication Pattern Analysis

Traffic Analysis:
- NetFlow/sFlow collection and parsing
- TCP connection state mapping
- Protocol identification (HTTP, SQL, SMB, NFS)
- Bandwidth utilization profiling

Dependency Inference:
- Client-server relationship mapping
- Database connection tracing
- Middleware interaction detection
- External service call identification

Phase 3: Data Enrichment & Validation

Week 2-3: Human-Guided Refinement

Automated Enrichment

Cross-reference with existing CMDB data
Application name inference from DNS, certificates, and banners
Business unit attribution via IP range ownership
Criticality classification from usage patterns

Expert Validation

Validation Activities:
- Anomaly review with application owners
- Unknown system investigation
- Dependency confirmation workshops
- Gap closure for unreachable segments

Quality Assurance:
- Confidence scoring per asset (High/Medium/Low)
- Coverage percentage tracking
- Exception documentation
- Data lineage recording

Phase 4: Analysis & Delivery

Week 3: Insight Generation

Portfolio Analysis

Application grouping by business capability
System landscape assembly
Technology stack categorization
End-of-life and end-of-support flagging

Deliverable Production

Report Components:
- Executive summary with key findings
- Detailed infrastructure inventory
- Application dependency matrices
- Network topology visualizations
- Risk and complexity heat maps
- Recommended next steps

Discovery Patterns

Pattern 1: Brownfield Enterprise

Best for: Established organizations with decades of accumulated IT

Characteristics:
- 500-10,000+ servers across multiple data centers
- Mix of physical and virtual infrastructure
- Multiple generations of technology
- Incomplete or outdated documentation

Approach:
- Phased scanning by data center or network zone
- Heavy focus on legacy system identification
- Mainframe and midrange inclusion strategies
- Historical dependency reconstruction

Typical Findings:
- 15-25% of servers running no active workload
- 30-40% of applications with undocumented dependencies
- 10-15% of infrastructure unknown to operations teams
- 20-30% of maintenance spend on end-of-life systems

Pattern 2: Greenfield Validation

Best for: Organizations that recently deployed new environments needing baseline documentation

Characteristics:
- Cloud-native or recently migrated infrastructure
- Modern tooling but incomplete asset tracking
- Rapid growth outpacing documentation
- Multi-cloud or hybrid deployments

Approach:
- Cloud API interrogation (AWS, Azure, GCP)
- Container and Kubernetes cluster mapping
- Serverless function inventory
- Infrastructure-as-code reconciliation

Typical Findings:
- 10-20% of cloud resources orphaned or untagged
- Shadow IT services deployed outside governance
- Cost optimization opportunities in right-sizing
- Security group and network policy inconsistencies

Pattern 3: Merger & Acquisition Due Diligence

Best for: Pre-close or post-close integration planning

Characteristics:
- Tight timeline (often 2-4 weeks)
- Limited access to target environment
- Confidentiality constraints
- Integration cost estimation required

Approach:
- Rapid assessment with constrained access
- Focus on infrastructure TCO estimation
- Redundancy and overlap identification
- Integration complexity scoring

Typical Findings:
- 20-35% infrastructure overlap with acquiring company
- License consolidation savings of 15-25%
- Integration complexity higher than initial estimates
- Hidden technical debt in acquired systems

Pattern 4: Compliance & Audit Readiness

Best for: Organizations preparing for regulatory audits or compliance certifications

Characteristics:
- Regulatory deadline driving urgency
- Need for complete system inventory
- Data flow documentation requirements
- Access path validation

Approach:
- Comprehensive system enumeration
- Data flow and storage location mapping
- Encryption and security control verification
- Access path and privilege documentation

Typical Findings:
- Unauthorized systems processing regulated data
- Unencrypted data flows between environments
- Orphaned access credentials on decommissioned systems
- Compliance gaps in disaster recovery configurations

Technology Expertise

Discovery Engine Capabilities

Network-Level Discovery

Protocols and Methods:
- SNMP v2c/v3 for device inventory
- NetFlow/sFlow/IPFIX for traffic analysis
- ARP and MAC table collection
- CDP/LLDP for topology mapping
- DNS zone analysis for service mapping

System-Level Profiling

Platform-Specific Methods:
- Windows: WMI, WinRM, PowerShell remoting
- Linux/Unix: SSH, SNMP, proc filesystem
- VMware: vSphere API interrogation
- Hyper-V: WMI and PowerShell
- AIX/Solaris: SSH with platform commands

Cloud Discovery

Cloud Provider APIs:
- AWS: EC2, RDS, Lambda, ECS, S3, VPC APIs
- Azure: Resource Manager, Virtual Machines, App Services
- GCP: Compute Engine, Cloud SQL, GKE, Cloud Functions
- Private Cloud: OpenStack, VMware Cloud Foundation

Supported Platforms

Operating Systems:
- Windows Server 2008 R2 through 2022
- Red Hat Enterprise Linux 6-9
- SUSE Linux Enterprise 12-15
- Ubuntu Server 16.04-24.04
- IBM AIX 7.1+, Oracle Solaris 11+

Hypervisors:
- VMware vSphere 6.5-8.0
- Microsoft Hyper-V 2016-2022
- KVM/QEMU, Citrix XenServer
- Nutanix AHV

Databases:
- Oracle 11g-21c
- Microsoft SQL Server 2012-2022
- PostgreSQL 10-16, MySQL 5.7-8.0
- MongoDB, Cassandra, Redis

Middleware:
- Oracle WebLogic 12c-14c
- IBM WebSphere 8.5-9.0
- Red Hat JBoss EAP 7.x
- Apache Tomcat, NGINX, HAProxy

Containers:
- Docker Engine, Kubernetes 1.24+
- OpenShift 4.x, Rancher, EKS, AKS, GKE

Analysis and Visualization Tools

Data Processing:
- Automated correlation engine
- Machine learning-based application grouping
- Protocol signature matching library
- Anomaly detection algorithms

Visualization:
- Interactive dependency graphs
- Network topology diagrams
- Heat maps for utilization and risk
- Exportable formats (Visio, Draw.io, PDF)

Industry Applications

Financial Services

Regulatory-Driven Discovery

Business Drivers

Basel III/IV infrastructure risk assessments
MiFID II data lineage requirements
PCI DSS cardholder data environment scoping
Operational resilience regulation (DORA) compliance

Discovery Focus

Financial Services Priorities:
- Trading system dependency chains
- Payment processing infrastructure mapping
- Regulatory reporting system identification
- Disaster recovery environment validation

Typical Scale:
- 5,000-50,000 servers across global data centers
- 500-2,000 applications in the portfolio
- 50-200 critical business applications
- Multi-vendor, multi-generation technology stack

Outcomes

Complete PCI DSS scope definition reducing audit effort by 40%
Operational resilience mapping for important business services
Trading system latency path documentation
Regulatory reporting system dependency validation

Healthcare

Patient Data Protection & Interoperability

Business Drivers

HIPAA compliance and PHI system identification
Clinical system interoperability mapping
EHR integration dependency documentation
Medical device network segmentation

Discovery Focus

Healthcare Priorities:
- Systems processing Protected Health Information
- Clinical workflow dependency chains
- Medical device network connectivity
- HL7/FHIR interface mapping

Compliance Requirements:
- PHI data flow documentation
- Access control validation
- Encryption verification
- Backup and recovery validation

Outcomes

Complete PHI system inventory for HIPAA compliance
Clinical system dependency maps for upgrade planning
Medical device isolation verification
Integration interface catalog for interoperability initiatives

Manufacturing

OT/IT Convergence & Supply Chain Visibility

Business Drivers

Industry 4.0 digital transformation planning
OT/IT network segmentation validation
Supply chain system integration mapping
ERP modernization preparation

Discovery Focus

Manufacturing Priorities:
- SCADA and PLC network mapping
- ERP system dependency documentation
- MES and quality system integration paths
- Supply chain connectivity to partners

Unique Considerations:
- Purdue model level classification
- Real-time system sensitivity
- Air-gapped network discovery methods
- Vendor-specific protocol handling

Outcomes

Complete OT/IT boundary documentation
ERP dependency maps supporting S/4HANA migration planning
Supply chain integration inventory
Cybersecurity segmentation validation per IEC 62443

Retail

Omnichannel Infrastructure Mapping

Business Drivers

PCI DSS cardholder data environment scoping
Omnichannel platform integration planning
Seasonal scaling capacity assessment
Store technology modernization

Discovery Focus

Retail Priorities:
- Point-of-sale system inventory across locations
- E-commerce platform dependency mapping
- Warehouse and logistics system integration
- Customer data platform connectivity

Scale Considerations:
- Hundreds to thousands of store locations
- Central and regional data centers
- Cloud and SaaS service dependencies
- Third-party payment and logistics integrations

Outcomes

PCI DSS scope reduction through accurate environment mapping
Store technology standardization roadmap inputs
E-commerce platform dependency documentation
Seasonal capacity planning baseline data

Implementation Challenges & Solutions

Technical Challenges

Network Access Constraints

Challenge:
- Firewall rules blocking scan traffic
- Network segmentation limiting visibility
- VPN and encrypted tunnel traversal
- Geographically distributed environments

Solutions:
- Distributed scan appliance deployment
- Firewall rule change requests with minimal scope
- Jump server placement in each network zone
- Phased scanning aligned with change windows

Legacy System Compatibility

Challenge:
- Older systems lacking modern management protocols
- Proprietary operating systems with limited tooling
- Mainframe and midrange access restrictions
- End-of-support systems with security lockdowns

Solutions:
- Protocol fallback strategies (Telnet, SNMP v1)
- Vendor-specific discovery adapters
- Manual supplementation for air-gapped systems
- Read-only mainframe console access methods

Cloud and Container Environments

Challenge:
- Ephemeral container instances
- Auto-scaling groups changing composition
- Serverless functions without persistent infrastructure
- Multi-account and multi-subscription sprawl

Solutions:
- Cloud API-based discovery (not network scanning)
- Point-in-time snapshots with change delta tracking
- Serverless function inventory via provider APIs
- Cross-account role assumption for unified visibility

Organizational Challenges

Stakeholder Resistance

Challenge:
- Concerns about scan impact on production
- Reluctance to share credentials
- Fear of exposing shadow IT
- Territorial data ownership disputes

Solutions:
- Non-intrusive scan methodology demonstration
- Credential vault with audited access controls
- Executive sponsorship and clear communication
- Discovery as enablement, not audit framing

Data Quality and Trust

Challenge:
- Skepticism about automated discovery accuracy
- Conflicting information from multiple sources
- Incomplete coverage due to access limitations
- Confidence scoring interpretation

Solutions:
- Transparent methodology documentation
- Validation workshops with application owners
- Clear confidence scoring with evidence trails
- Gap documentation with remediation recommendations

Deliverables

Discovery Report

Complete infrastructure inventory with confidence scores
Application portfolio catalog with business unit attribution
Dependency matrices showing system-to-system relationships
Network topology diagrams at logical and physical layers
Technology stack distribution analysis
End-of-life and end-of-support risk register

Analysis Dashboard

Real-time discovery progress during the engagement
Coverage statistics by network zone and platform type
Confidence scoring breakdown (High/Medium/Low)
Exception reporting for unreachable or unclassified systems

Transformation Readiness Assessment

Migration complexity scoring per application group
Risk identification with severity and likelihood ratings
Quick win opportunities for immediate optimization
Baseline metrics for measuring transformation progress

Data Export Packages

Machine-readable inventory exports (CSV, JSON, Excel)
CMDB import-ready data packages
Integration with migration planning tools (Cloudamize, AWS Migration Hub)
Visualization exports (Visio, Draw.io, PDF)

Success Metrics & KPIs

Discovery Accuracy

Coverage Metrics:
- Environment coverage: Target 95-98% of addressable assets
- Application identification rate: 90-95% automated classification
- Dependency mapping completeness: 85-95% of actual connections
- Confidence scoring: 80%+ assets at High confidence level

Quality Metrics:
- False positive rate: &lt;5% of identified relationships
- False negative rate: &lt;10% verified through validation
- Data freshness: All data &lt;2 weeks old at delivery
- Cross-reference accuracy: 95%+ match with known systems

Business Impact Metrics

Planning Acceleration:
- Migration planning time reduction: 40-60%
- Transformation risk reduction: 25-35%
- Scope accuracy improvement: 3-5x vs. manual methods
- Decision confidence increase: From low/medium to high

Cost Avoidance:
- Prevented migration rollbacks: $500K-2M per avoided incident
- Reduced rework from missed dependencies: 20-30% effort savings
- Compliance gap early identification: Avoided audit findings
- Shadow IT rationalization: 10-15% infrastructure cost reduction

Operational Metrics

Engagement Efficiency:
- Time to first results: &lt;5 business days
- Total engagement duration: 2-3 weeks
- Client IT team involvement: &lt;20 hours total
- Data delivery SLA: 100% of deliverables on schedule

Investment & ROI

Typical Investment

Engagement Pricing:
- Standard discovery (up to 2,000 assets): $75K-$120K
- Enterprise discovery (2,000-10,000 assets): $120K-$250K
- Large-scale discovery (10,000+ assets): $250K-$500K

Included in All Engagements:
- All discovery tooling and licensing
- Expert architect-led analysis
- Validation workshops
- Complete deliverable package
- 30-day post-delivery support

Pricing Model:
- Fixed price based on agreed scope
- No hidden costs or hourly overruns
- Travel and expenses included
- Optional follow-on retainer available

ROI Components

Immediate Value (Week 3):
- Accurate planning baseline eliminating guesswork
- Single source of truth for IT estate
- Identified quick wins for immediate action
- Risk register for proactive mitigation

Short-Term Value (3 months):
- 15-20% reduction in transformation planning risk
- Migration wave planning accuracy improvement
- License optimization opportunities identified
- Compliance gap remediation initiated

Medium-Term Value (6 months):
- 25-30% faster migration execution from accurate baselines
- Avoided costs from prevented dependency-related failures
- Shadow IT rationalization savings
- Improved vendor negotiation leverage from accurate data

Long-Term Value (12+ months):
- Foundation for continuous portfolio management
- Baseline for measuring transformation progress
- Repeatable discovery capability for future programs
- Organizational knowledge preservation

Payback Timeline

Typical ROI Trajectory:
- Investment recovery: 2-4 months (through avoided rework)
- 12-month ROI: 300-500% (transformation acceleration)
- Ongoing value: Reduced risk across all subsequent programs

Comparison with Alternatives:
- Manual audit: 3-6 months, 40-60% less accurate, 2-3x cost
- Agent-based tools: 2-4 months deployment, ongoing license fees
- CMDB refresh: 6-12 months, requires sustained process change
- Our approach: 2-3 weeks, 95-98% accurate, fixed cost

Next Steps

After discovery, clients typically proceed to:

Cloud Strategy & Workload Placement — Determine optimal hosting for each workload
Transformation Roadmapping — Sequence migration and modernization waves
Legacy System Cost Reduction — Target high-cost systems identified during discovery
Application Rationalization — Reduce portfolio complexity through consolidation and retirement

Application Portfolio Discovery