From Monoliths to Living Architecture

Safe Modernization with ACCelerAIte

2025 ACCelerAIte Research

Abstract

Most enterprises can't stop the world and rebuild their systems from scratch. Lift-and-shift cloud migrations only replicate existing problems in new environments. Big-bang rewrites collapse under risk, budget overruns, and changing requirements.

This whitepaper introduces AccelerAIte, an AI-driven Enterprise Architecture Mining (EAM) platform that builds a Living Architecture Map from Code, Configurations, Costs, Change Controls, Compliance, Capacity, Custody, Catalogs, Caching, Credentials, Clusters, Contracts, and Connections.

By continuously mining codebases, observability data (Splunk, Datadog, Prometheus), organizational evidence (tickets, docs, ADRs), and network surfaces, AccelerAIte delivers actionable modernization paths.

Instead of rewriting or replicating, enterprises can deconstruct monoliths safely through staged transition states — proxy → dual-write → cutover — each backed by measurable KPIs and a tamper-evident Evidence Ledger.

1. The Modernization Trap

  • Lift and shift → Infrastructure moves but architectural debt persists.
  • Big-bang rewrites → All risk front-loaded, failure rates high.
  • Stale diagrams → Architecture docs drift from reality within months.
  • Patchwork monoliths → Systems aren't single blocks; they're bundles of AORs entangled by decades of change.
  • Unknown edges → Exposed endpoints, APIs, and ingress routes often remain undocumented.

2. Enterprise Architecture Mining (EAM)

AccelerAIte mines your enterprise stack across all C-word layers:

  • Code Mining → dependency graphs, churn analysis, ownership patterns.
  • Configuration Mining → infrastructure configs, pipelines, toggles, feature flags.
  • Cost Mining → cloud spend analysis, over-provisioning detection, waste identification.
  • Change Control Mining → tickets, PRs, approvals, incidents, deployment patterns.
  • Compliance Mining → audit logs, policies, regulatory signals, security posture.
  • Capacity Mining → throughput analysis, utilization patterns, scalability limits.
  • Custody Mining → ownership mapping, team alignments, CODEOWNERS analysis.
  • Catalog Mining → APIs, schemas, data lineage, service registries.
  • Cache Mining → hit rate patterns, invalidation flows, performance layers.
  • Credential Mining → auth flows, secrets sprawl, identity mapping.
  • Cluster Mining → deployments, pods, scaling groups, orchestration.
  • Contract Mining → SLAs, API agreements, service boundaries, vendor dependencies.
  • Connection Mining → network topology, hidden ingress/egress routes, undocumented APIs.

Together, these form the Living Architecture Map.

3. Edge Detection: Why It Matters

Hidden edges often derail modernization:

  • Orphaned /v1/* APIs still serving traffic.
  • Proxy bypasses skipping authentication.
  • Shadow egress flows leaking data.
  • CDN endpoints delivering stale content.

AccelerAIte detects these edges using:

  • Infra & Config scans (Kubernetes Ingress, Nginx/Envoy, IaC).
  • Cloud & DNS inventories (ELBs, gateways, subdomains, cert SANs).
  • Runtime evidence (logs, WAF traces, OTel spans).

Findings are added to the Living Architecture Map and classified by risk, owner, and modernization impact.

4. The Living Architecture Map

A continuously updated, unified model of the system as it is:

  • Code → repo/module dependencies.
  • Configurations → infrastructure settings and deployment patterns.
  • Costs → resource utilization and optimization opportunities.
  • Change Controls → approval workflows and deployment gates.
  • Compliance → audit trails and policy enforcement.
  • Capacity → performance metrics and scaling thresholds.
  • Custody → ownership alignment and team responsibilities.
  • Catalogs → service registries and API documentation.
  • Caching → performance layers and invalidation patterns.
  • Credentials → authentication flows and secrets management.
  • Clusters → container orchestration and deployment topology.
  • Contracts → SLAs and service boundaries.
  • Connections → risk-classified endpoints and ingress/egress paths.

Sample Living Architecture Map: E-commerce Platform

📦 USER-SERVICE

Code: 47 modules, 18 repos, 127 commits/week, high coupling to auth-service (0.8), owned by @platform-team
Configurations: 3 environments (dev/staging/prod), 12 feature flags active, Kubernetes deployment, auto-scaling enabled 2-10 pods
Costs: $1,247/month AWS spend, 73% CPU utilization, over-provisioned by ~$400/month, RDS instance underutilized
Change Controls: 23 PRs pending, requires 2 approvals, 87% test coverage, last deployment: 2 days ago, 1 failed deployment this week
Compliance: PCI-DSS compliant, logs shipped to Splunk, 14 security policies active, vulnerability scan: 2 medium issues
Capacity: P95 latency: 245ms, handling 2,847 req/min peak, 99.2% uptime, error rate: 0.8%, 4 pods running
Custody: Owner: platform-team, On-call: @sarah.chen, CODEOWNERS: platform-team, last update: @mike.rodriguez
Catalogs: 7 REST endpoints documented in Swagger, registered in Kong Gateway, schema v2.1 in schema registry
Caching: Redis cluster, 89% hit rate, 2.4GB memory usage, TTL: 1h for user profiles, cache invalidation via events
Credentials: OAuth 2.0 + JWT, secrets in AWS Secrets Manager, 3 service accounts, cert expires in 67 days
Clusters: EKS cluster "prod-east", namespace: user-service, 4 pods across 2 nodes, HPA configured
Contracts: SLA: 99.5% uptime, 200ms P95 latency, depends on auth-service (99.9% SLA), payment-service integration
Connections: Ingress: ALB → /users/*, Egress: auth-service:443, payment-service:8080, analytics-kafka:9092

🔐 AUTH-SERVICE

Code: 23 modules, 8 repos, 73 commits/week, critical dependency for 12 services, owned by @security-team
Configurations: Multi-region deployment, circuit breaker enabled, rate limiting: 1000 req/min, Redis session store
Costs: $2,156/month, 91% CPU during peak, well-optimized, reserved instances saving 40%
Change Controls: 8 PRs pending, requires security team approval, 94% test coverage, deployment freeze during Black Friday
Compliance: SOC2 Type II, GDPR compliant, audit logs encrypted, pen-test: last month, zero critical vulnerabilities
Capacity: P95: 89ms, handling 8,934 auth/min peak, 99.97% uptime, error rate: 0.1%, 8 pods running
Custody: Owner: security-team, On-call: @alex.kim, Critical service, escalation path defined
Catalogs: OAuth 2.0 provider, 4 endpoints, OpenAPI 3.0 spec, integrated with 12 downstream services
Caching: Redis Cluster, 96% hit rate for sessions, 1.8GB usage, distributed across 3 nodes
Credentials: HSM-backed keys, auto-rotation enabled, 15 service principals, OIDC federation
Clusters: Multi-AZ deployment, 8 pods across 3 nodes, pod disruption budget: min 4 available
Contracts: SLA: 99.95% uptime, 100ms P95 latency, dependency for 12 services, JWT token validity: 1h
Connections: Ingress: ALB → /auth/*, internal mesh, egress: LDAP:636, SMS gateway:443

⚠️ PAYMENT-SERVICE (MODERNIZATION TARGET)

Code: Legacy monolith, 156 modules, 1 massive repo, 312 commits/week, tight coupling detected, owned by @payments-team
Configurations: Manual deployment, single instance, no auto-scaling, config hardcoded, environment drift detected
Costs: $4,892/month, oversized EC2 instances, 23% utilization, estimated $2,800/month waste
Change Controls: Manual testing, no CI/CD, deployment takes 4 hours, last deployment: 3 weeks ago
Compliance: PCI-DSS audit findings: 7 high-risk items, manual log collection, encryption gaps identified
Capacity: P95: 1,247ms (SLA breach), 94.2% uptime, error rate: 3.1%, single point of failure
Custody: Owner: payments-team, On-call: @legacy-support, knowledge concentrated in 2 developers
Catalogs: SOAP APIs, limited documentation, 23 undocumented endpoints discovered, schema drift
Caching: In-memory cache only, no distributed caching, cache invalidation issues, 45% hit rate
Credentials: Hardcoded API keys detected, manual cert renewal, service account sprawl: 12 accounts
Clusters: Single EC2 instance, no orchestration, no health checks, manual scaling
Contracts: SLA: 99% uptime (missing), P95: 500ms (violated), affects checkout flow, vendor lock-in
Connections: Direct DB access, 15 hidden endpoints, bypass proxy, unencrypted internal traffic

🚀 AccelerAIte Modernization Recommendation

Phase 1: Strangler proxy for checkout endpoints → Target: P95 < 300ms, 99.5% uptime

Phase 2: Dual-write payment data → Target: Data consistency 99.9%

Phase 3: Full cutover to microservices → Target: $2,800/month cost savings, P95 < 200ms

Evidence Ledger: Links to Jira PAY-1023, Splunk traces, PCI audit findings, cost analysis

5. Evidence-Backed Transition States

Modernization is delivered as safe slices:

  • Strangler Proxy → intercept traffic gradually.
  • Branch by Abstraction → replace behind an interface.
  • Dual-write / Data Twin → validate parity before cutover.
  • Shadow Traffic + Canary → test new paths in production safely.

Each step references the Evidence Ledger, recording:

  • Context & drivers
  • Alternatives considered
  • Observed KPIs (latency, error budgets, cost)
  • Linked docs and tickets

6. Why AccelerAIte

  • We C Everything → Code, Configurations, Costs, Change Controls, Compliance, Capacity, Custody, Catalogs, Caching, Credentials, Clusters, Contracts, Connections.
  • Edge Detection → no hidden endpoints derail migrations.
  • Evidence-backed → all decisions logged and auditable.
  • Safe modernization → staged transition states, not risky rewrites.
  • Clean outside, AI inside → human-readable configs, AI-powered analysis.
  • Integration-first → GitHub, Jira, Splunk, Datadog, Prometheus, AWS/GCP/Azure.

Deep Dive: How AccelerAIte Mines Each "C"

AccelerAIte deploys AI-powered mining adapters that learn from YOUR documentation, configurations, and designs. These adapters quickly understand your unique environment and toolchain—no matter how legacy or modern.

Code Mining

What we mine: Dependencies, coupling, hotspots, ownership, technical debt patterns, security vulnerabilities (SAST/DAST findings)

Technology ecosystem:

  • Version Control: Git (GitHub, GitLab, Bitbucket), Perforce (Helix Core), IBM ClearCase, Bazaar, Mercurial, SVN, TFS/Azure DevOps
  • Legacy systems: PVCS, SourceSafe, RCS, SCCS
  • SAST (Static Analysis): SonarQube, Veracode, Checkmarx, Fortify, CodeClimate, Semgrep, CodeQL
  • DAST (Dynamic Analysis): OWASP ZAP, Burp Suite, Rapid7 AppSpider, Veracode DAST, Checkmarx DAST

Example: AI mining adapters can analyze Perforce depot structures, identify coupling patterns, and detect ownership gaps by learning from your existing documentation and naming conventions.

Configurations Mining

What we mine: Infrastructure configs, deployment scripts, feature flags, environment variables

Technology ecosystem:

  • Infrastructure as Code: Terraform, CloudFormation, Pulumi, ARM templates, Ansible, Chef, Puppet
  • Container orchestration: Kubernetes YAML, Docker Compose, OpenShift, Rancher
  • CI/CD: Jenkins (Groovy DSL), GitLab CI, GitHub Actions, Azure DevOps, TeamCity, Bamboo
  • Configuration management: Consul, etcd, Zookeeper, Spring Cloud Config
  • Legacy automation: BMC BladeLogic, IBM Tivoli, CA Unicenter

Example: AI adapters can parse Jenkins Groovy DSL and BMC BladeLogic scripts to identify deployment complexity, environment drift, and automation gaps, then recommend GitOps migration paths.

Costs Mining

What we mine: Cloud spend patterns, resource utilization, over/under-provisioning

Technology ecosystem:

  • Cloud billing: AWS Cost Explorer, Azure Cost Management, GCP Billing, Oracle Cloud Cost Analysis
  • FinOps tools: CloudHealth, Cloudability, Apptio, ParkMyCloud, Spot.io
  • Resource monitoring: CloudWatch, Azure Monitor, Stackdriver, Datadog Infrastructure
  • Legacy ITSM: IBM Tivoli Asset Manager, CA Asset Portfolio Management, ServiceNow ITAM

Change Controls Mining

What we mine: Tickets, PRs, approvals, incident patterns, change velocity

Technology ecosystem:

  • Modern ITSM: ServiceNow, Jira Service Management, Freshservice, Zendesk
  • Legacy ITSM: BMC Remedy, CA Service Desk, HP Service Manager, IBM Tivoli
  • Enterprise workflow: Salesforce, Microsoft Dynamics, Oracle Service Cloud
  • Specialized: COEUS/Request systems, custom workflow engines
  • Change Advisory: ServiceNow CAB, custom approval workflows

Example: AI adapters can analyze dual ITSM environments (ServiceNow + BMC Remedy), identify approval bottlenecks, and correlate change patterns with incident data to recommend workflow optimizations.

Compliance Mining

What we mine: Audit logs, policy violations, regulatory signals, security events

Technology ecosystem:

  • GRC platforms: ServiceNow GRC, IBM OpenPages, MetricStream, LogicGate
  • Security tools: Splunk Enterprise Security, QRadar, ArcSight, Chronicle
  • Compliance automation: Chef InSpec, AWS Config, Azure Policy, GCP Security Command Center
  • Legacy audit: CA GRC, Oracle GRC, SAP GRC, custom compliance frameworks

Capacity Mining

What we mine: Throughput patterns, utilization trends, scalability limits, performance baselines

Technology ecosystem:

  • Modern APM: Datadog, Dynatrace, New Relic, AppDynamics, Elastic APM
  • Infrastructure monitoring: Prometheus + Grafana, InfluxDB, TimescaleDB
  • Legacy monitoring: IBM Tivoli Monitoring, CA Spectrum, HP Operations Manager, SolarWinds
  • Specialized: Compuware APM, BMC TrueSight, Oracle Enterprise Manager

Example: AI adapters can correlate modern APM tools (Dynatrace) with legacy monitoring (IBM Tivoli) to discover cross-platform performance relationships and recommend targeted modernization strategies.

Custody Mining

What we mine: Ownership patterns, team mappings, CODEOWNERS, escalation paths

Technology ecosystem:

  • Team management: GitHub Teams, GitLab Groups, Azure DevOps, Slack workspace mapping
  • ITSM ownership: ServiceNow CMDB, Jira project permissions, PagerDuty escalation
  • Legacy systems: LDAP/AD group mappings, custom RBAC systems, mainframe security groups
  • Documentation: Confluence, Notion, SharePoint, wiki systems, RACI matrices

Catalogs Mining

What we mine: APIs, schemas, data lineage, service dependencies, contract definitions

Technology ecosystem:

  • API management: Kong, Apigee, AWS API Gateway, Azure API Management, MuleSoft
  • Service mesh: Istio, Linkerd, Consul Connect, AWS App Mesh
  • Schema registries: Confluent Schema Registry, AWS Glue, Azure Data Catalog
  • Legacy integration: IBM DataStage, Informatica, Talend, TIBCO, webMethods

Caching Mining

What we mine: Cache hit rates, invalidation patterns, distributed cache topology

Technology ecosystem:

  • Modern caching: Redis, Memcached, Hazelcast, Amazon ElastiCache, Azure Cache
  • Application caching: Caffeine, EHCache, Guava Cache, Spring Cache
  • CDN: CloudFlare, AWS CloudFront, Azure CDN, Akamai, Fastly
  • Legacy: IBM WebSphere eXtreme Scale, Oracle Coherence, Terracotta

Credentials Mining

What we mine: Auth flows, secrets sprawl, certificate management, access patterns

Technology ecosystem:

  • Secrets management: HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, CyberArk
  • Identity providers: Okta, Auth0, Azure AD, AWS Cognito, Ping Identity
  • Legacy IAM: CA SiteMinder, IBM Tivoli Access Manager, Oracle Access Manager
  • Certificate management: Let's Encrypt, DigiCert, internal CAs, cert-manager

Clusters Mining

What we mine: Container deployments, pod scaling patterns, resource allocation

Technology ecosystem:

  • Container orchestration: Kubernetes, OpenShift, Rancher, Amazon EKS, Azure AKS, GKE
  • Legacy clustering: VMware vSphere, IBM PowerHA, Oracle RAC, Windows Failover Clustering
  • Service mesh: Istio, Linkerd, Consul Connect observability
  • Monitoring: Prometheus, Kubernetes Dashboard, Rancher monitoring

Contracts Mining

What we mine: SLAs, API contracts, service boundaries, vendor dependencies

Technology ecosystem:

  • API contracts: OpenAPI/Swagger, GraphQL schemas, gRPC protobuf, AsyncAPI
  • Testing: Pact contract testing, Postman, Insomnia, REST Assured
  • SLA monitoring: PagerDuty SLA tracking, ServiceNow SLA management
  • Legacy integration: SOAP WSDL, EDI standards, mainframe COBOL copybooks

Connections Mining

What we mine: Network topology, service communication patterns, data flows

Technology ecosystem:

  • Network monitoring: Wireshark, ntopng, SolarWinds NPM, PRTG
  • Service mesh observability: Istio telemetry, Linkerd viz, Consul Connect insights
  • Legacy networking: IBM Tivoli Netcool, CA Spectrum, HP Network Node Manager
  • Cloud networking: AWS VPC Flow Logs, Azure Network Watcher, GCP VPC Flow Logs

AI-Powered Quick Adapters

AccelerAIte's core innovation: AI miners that learn from YOUR environment by ingesting your documentation, recognizing patterns in your logs and workflows, and generating custom mining adapters tailored to your specific tech stack, business logic, and organizational structure.

Integration with Existing Tools

AccelerAIte integrates seamlessly with your existing monitoring and management tools to provide AI-powered architectural insights alongside your current dashboards and workflows.

Key Integration Benefits:

  • Real-time metrics combined with architectural insights
  • Prioritized modernization roadmap based on business impact
  • Risk-aware migration planning with rollback scenarios
  • Evidence-linked decisions for audit trails
  • Cost impact projections before implementation

Conclusion

Don't lift and shift. Don't take the long way building everything on your own.

AccelerAIte enables modernization through Enterprise Architecture Mining, enriched by Infrastructure Discovery and Edge Detection, unified in a Living Architecture Map.

Modernization isn't a leap; it's a sequence. With AccelerAIte, every step is transparent, evidence-backed, and under your control.