AI-native unified cloud governance platform — multi-cloud discovery, 6R migration planning, IaC security, FinOps intelligence, compliance audit, and executive AI chat. Built entirely on Claude Opus 4.7. Zero paid SaaS dependencies.
April 2026 — Opus 4.7 Executive Upgrade + v0.2.0 Platform Expansion. Platform now runs on Claude Opus 4.7 across every auditable path, with prompt caching, native tool-use structured output, extended-thinking reasoning traces as Annex IV evidence, a 1M-context executive chat, Batch API bulk scoring, and seven new capability tracks (multi-cloud discovery, IaC security, app portfolio scanning, integration hub, observability, advanced FinOps, and an Anthropic-native cost optimization layer). See docs/OPUS_4_7_UPGRADE.md and CHANGELOG.md for details.
Enterprise AI Accelerator is an AI-native unified cloud governance platform built exclusively on Claude Opus 4.7 and open-source dependencies. It replaces the fragmented point solutions — migration tools, IaC scanners, FinOps dashboards, compliance auditors — that enterprise teams currently assemble from five to ten separate vendors, and does so at a fraction of the cost with a single audit trail. The platform covers the full cloud governance lifecycle: discover your multi-cloud estate, classify workloads for migration, scan infrastructure code for security and compliance violations, optimize cloud spend down to carbon emissions, and surface every decision in a tamper-evident audit chain that satisfies EU AI Act Annex IV. Everything runs on a single Anthropic subscription with no paid SaaS intermediaries.
Bottom line. A Big 6 cloud-transformation engagement delivers a PowerPoint in 12–18 months for $3.2M–$12M. This platform ships equivalent technical surface area today for $25K–$80K / year — one Anthropic subscription, zero paid SaaS, a single tamper-evident audit trail.
| Firm / Platform | Engagement | What they ship | What this platform ships instead |
|---|---|---|---|
| Accenture — MyNav | $500K–$5M | 2019-era recommendation engine with GPT-4 wrappers; no persisted reasoning traces | Native Opus 4.7 reasoning; extended-thinking traces as Annex IV audit evidence; source-available |
| Deloitte — CloudCompass / Converge | $400K–$3M + $200K/yr | Tableau + Excel + PowerPoint automation; human-curated compliance content | Agentic decision engine; Citations API grounds every compliance claim in cited regulation text; SARIF 2.1.0 export |
| PwC — Cloud Transformation Suite | $300K–$2M | LLMs used only for summarization; assessment-heavy | Frontier-model decision engine; tamper-evident Merkle audit chain; integrated carbon tracking |
| EY — Nexus for Cloud | $400K–$2M | Thin glue over AWS Migration Hub / Azure Migrate; PowerPoint deliverables | Unified 6R + compliance + FinOps on one audit trail; structured tool-use JSON output |
| KPMG — Powered Enterprise Cloud | $500K–$4M | SAP-centric; light on cloud-native / K8s / serverless | Multi-cloud adapters (AWS / Azure / GCP / K8s); IaC drift detection; open-source carbon model |
| Cognizant — CloudVue / Cloud Steps | $200K–$1.5M | Pattern-matching over rules tables | Genuine extended-thinking reasoning; prompt caching; Anthropic-native architecture |
| Capgemini — eAPM + Migration Factory | $300K–$2M | Rules-based app portfolio analysis | AI-native 6R scoring; 1M-context executive chat; interleaved thinking + tool-use |
Where the Big 6 still win. Brand trust with boards, regulatory sign-off at Fortune 500 scale, and the organizational change-management muscle a 200-person program office provides. This platform is the technical substrate — not a replacement for that program office.
| Tool | Category | List price | Replaced by |
|---|---|---|---|
| CAST Highlight / vFunction | App portfolio + refactoring | $150K–$600K/yr | app_portfolio/ |
| Snyk IaC / Prisma Cloud | IaC security | $200K–$1M/yr | iac_security/ |
| IBM OpenPages AI Governance | AI risk + compliance | $500K–$2M/yr | ai_audit_trail/ + policy_guard/ + compliance_citations/ |
| Credo AI | Bias + AI compliance | $180K/yr | policy_guard/thinking_audit.py |
| ServiceNow IRM + AIOps | GRC + ops | $300K–$2M/yr | integrations/ + executive_chat/ |
| Apptio Cloudability / CloudZero | FinOps | $100K–$1M/yr | finops_intelligence/ |
| Flexera One / Turbonomic | Cloud cost + optimization | $100K–$2M/yr | cloud_iq/adapters/ + finops_intelligence/right_sizer.py |
| Datadog / New Relic | Observability | $50K–$500K/yr | observability/ (OSS OTEL + Prometheus + Grafana) |
| Cloud Carbon Footprint | Sustainability | Free | finops_intelligence/carbon_tracker.py |
| Approach | Year 1 | Year 3 cumulative |
|---|---|---|
| Big 6 engagement + licensed tools | $3.2M–$12M | $6M–$25M |
| Best-of-breed commercial tools, assembled in-house | $1.5M–$4M | $4.5M–$12M |
| This platform (Anthropic API + self-hosted) | $25K–$80K | $75K–$240K |
graph TD
A["User / Executive"] --> B["MCP Server (18 tools + 4 resources + 4 prompts, stdio + SSE)"]
B --> C["AgentOps Orchestrator"]
C --> D["Opus 4.7 Coordinator"]
D --> E["ArchitectureAgent (Haiku 4.5)"]
D --> F["MigrationAgent (Haiku 4.5)"]
D --> G["ComplianceAgent (Haiku 4.5)"]
D --> H["ReportAgent (Sonnet 4.6)"]
E --> I["AI Audit Trail"]
F --> I
G --> I
H --> I
I --> J["SHA-256 Merkle chain + SARIF 2.1.0"]
graph TD
Core["Core AIClient — caching, thinking, routing, streaming, batch, citations"]
Core --> CloudIQ["cloud_iq/adapters/ — AWS / Azure / GCP / K8s discovery"]
Core --> Scout["migration_scout/ — 6R classifier + batch + thinking audit"]
Core --> Portfolio["app_portfolio/ — repo to 6R via extended thinking"]
Core --> IaC["iac_security/ — Terraform + Pulumi + SBOM + OSV + drift + SARIF"]
Core --> FinOps["finops_intelligence/ — CUR + RI/SP + right-size + carbon"]
Core --> Policy["policy_guard/ — IaC + bias + thinking audit"]
Core --> Citations["compliance_citations/ — Citations API evidence grounding"]
Core --> Chat["executive_chat/ — 1M-context unified Q&A"]
Core --> Integrations["integrations/ — Slack / Jira / ServiceNow / Teams / GitHub / PagerDuty"]
Core --> Obs["observability/ — OTEL + Prometheus + Grafana"]
git clone https://github.com/HunterSpence/enterprise-ai-accelerator.git
cd enterprise-ai-accelerator
pip install -r requirements.txt
export ANTHROPIC_API_KEY=sk-ant-...
# Simplest demo — scan a local repo for app portfolio intelligence
python -m app_portfolio.cli .
# AI governance + EU AI Act compliance
python -m ai_audit_trail.demo
# Multi-cloud discovery (auto-detects available credentials)
python -c "from cloud_iq.adapters.unified import UnifiedDiscovery; UnifiedDiscovery.auto().discover()"
# IaC security scan
python -m iac_security .
# Full FinOps with CUR ingestion + carbon tracking
python -m finops_intelligence.demoAll module demos include synthetic data. No cloud credentials required to run any demo.
┌─────────────────────────────────────────────────────────────────────────────────┐
│ Entry Points │
│ CLI · MCP Server (19 tools) · Python SDK · Webhook Dispatcher │
└──────┬──────────────────┬────────────────────┬──────────────────────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────────────────────────────────────────────────────────────────────────┐
│ core/ — Anthropic Optimization Layer │
│ AIClient · ModelRouter (~95% cost savings) · ResultCache · BatchCoalescer │
│ Streaming · FilesAPI · InterleavedThinking · CostEstimator · Telemetry │
└──────┬──────────────────┬────────────────────┬──────────────────────────────────┘
│ │ │
┌──────▼──────┐ ┌────────▼────────┐ ┌────────▼───────┐ ┌───────────────────────┐
│ cloud_iq/ │ │ app_portfolio/ │ │ iac_security/ │ │ finops_intelligence/ │
│ adapters/ │ │ (11 languages) │ │ (20 policies) │ │ CUR + RI/SP + right- │
│ AWS·Azure │ │ OSV CVE scan │ │ SBOM·SARIF │ │ sizing + carbon │
│ GCP·K8s │ │ 6R via Opus │ │ drift detect │ │ DuckDB analytics │
└──────┬──────┘ └────────┬────────┘ └────────┬───────┘ └──────────┬────────────┘
│ │ │ │
└──────────────────┴────────────────────┴──────────────────────┘
│
┌───────────────────────────────────▼────────────────────────────────────────────┐
│ agent_ops/ — Multi-Agent Orchestrator │
│ Opus 4.7 Coordinator · Sonnet 4.6 Reporter · Haiku 4.5 Workers │
└───────────────────────────────────┬────────────────────────────────────────────┘
│
┌─────────────────────────┼─────────────────────────┐
│ │ │
┌─────────▼──────────┐ ┌──────────▼──────────┐ ┌─────────▼──────────────────┐
│ migration_scout/ │ │ policy_guard/ │ │ ai_audit_trail/ │
│ 6R + Monte Carlo │ │ EU AI Act + HIPAA │ │ SHA-256 Merkle chain │
│ dependency maps │ │ SOC2 + PCI-DSS │ │ SARIF 2.1.0 + Article 12 │
│ wave planning │ │ SARIF 2.1.0 │ │ Annex IV evidence │
└────────────────────┘ └─────────────────────┘ └────────────────────────────┘
│ │ │
└─────────────────────────▼─────────────────────────┘
│
┌───────────────────────────────────▼────────────────────────────────────────────┐
│ executive_chat/ + compliance_citations/ + risk_aggregator.py │
│ 1M-context CTO Q&A · Citations API compliance evidence · 0–100 score │
└───────────────────────────────────┬────────────────────────────────────────────┘
│
┌───────────────────────────────────▼────────────────────────────────────────────┐
│ integrations/ + observability/ │
│ Slack · Jira · ServiceNow · GitHub · Teams · PagerDuty · SMTP │
│ OTEL gen_ai.* traces · 8 Prometheus metrics · Grafana dashboards │
└────────────────────────────────────────────────────────────────────────────────┘
Model tier: Opus 4.7 handles coordination + high-stakes reasoning (6R, extended thinking, executive chat). Sonnet 4.6 handles report synthesis. Haiku 4.5 handles high-volume worker tasks. The model router selects the right tier automatically based on task complexity.
| Module | Purpose | Key Classes | Value Prop |
|---|---|---|---|
| core/ | Anthropic optimization layer | AIClient, ModelRouter, ResultCache, BatchCoalescer, CostEstimator, StreamHandler, FilesAPIClient, InterleavedThinkingLoop |
~95% cost reduction vs always-Opus baseline via complexity routing + SQLite cache + auto-coalescing Batch API |
| cloud_iq/ | AWS infrastructure analysis | CloudScanner, CostAnalyzer, MLDetector, NLQueryEngine |
$47K/month waste identified in AcmeCorp demo without credentials |
| cloud_iq/adapters/ | Multi-cloud discovery | AWSAdapter, AzureAdapter, GCPAdapter, KubernetesAdapter, UnifiedDiscovery |
Real boto3 / azure-mgmt / google-cloud / kubernetes discovery with graceful degradation |
| app_portfolio/ | Repository intelligence | LanguageDetector, DependencyScanner, CVEScanner, ContainerizationScorer, CIMaturityScorer, SixRScorer |
11 languages, 9 dep manifests, OSV.dev CVE scan, Opus 4.7 extended-thinking 6R per repo |
| migration_scout/ | 6R workload classification | WorkloadAssessor, DependencyMapper, WavePlanner, BatchClassifier, ThinkingAudit |
Only OSS tool with AI-native 6R + Monte Carlo wave planning (AWS Migration Hub closed Nov 2025) |
| policy_guard/ | Multi-framework compliance | ComplianceScanner, BiasDetector, SARIFExporter, IncidentResponse, ThinkingAudit |
9-framework scanner (EU AI Act + HIPAA + SOC 2 + CIS AWS + NIST AI RMF 2.0 + ISO 42001 + DORA + FedRAMP Rev 5 + PCI DSS 4.0); cross-framework traceability matrix; one implementation closes controls across all frameworks simultaneously |
| iac_security/ | IaC security scanning | TerraformParser, PulumiParser, PolicyEngine, SBOMGenerator, OSVScanner, DriftDetector, SARIFExporter |
20 built-in policies (CIS AWS / PCI-DSS / SOC 2 / HIPAA), CycloneDX SBOM, OSV CVE, SARIF to GitHub Security tab |
| finops_intelligence/ | Cloud cost intelligence | CURIngestor, RISPOptimizer, RightSizer, CarbonTracker, SavingsReporter, AnomalyDetector |
AWS CUR via DuckDB, RI/SP optimizer (80% coverage cap), right-sizing with CloudWatch, carbon tracking with open coefficients |
| ai_audit_trail/ | EU AI Act audit logging | MerkleChain, EUAIActLogger, NISTRMFScorer, IncidentManager, SARIFExporter |
Only OSS tool combining SHA-256 Merkle chain + SARIF 2.1.0 + Article 12 / Annex IV |
| executive_chat/ | 1M-context CTO Q&A | ExecutiveChat, BriefingLoader |
Full enterprise briefing in one prompt; follow-ups cost ~10% via 1-hour cache |
| compliance_citations/ | Evidence-grounded compliance | EvidenceLibrary, CitationsEngine |
Anthropic Citations API — character-range citations, no hallucinated control IDs |
| agent_ops/ | Multi-agent orchestration | Orchestrator, CoordinatorAgent, ReporterAgent, WorkerAgent |
Opus 4.7 coordinator + Sonnet 4.6 reporter + Haiku 4.5 workers with MCP-driven dispatch |
| integrations/ | Notification + ticketing | FindingRouter, WebhookDispatcher, SlackAdapter, JiraAdapter, ServiceNowAdapter, GitHubAppAdapter, TeamsAdapter, PagerDutyAdapter, SMTPAdapter |
Retry / circuit-breaker / rate-limit on all adapters; PR check-runs with inline annotations |
| observability/ | Full OTEL stack | TelemetryClient, PrometheusExporter, Grafana dashboards |
gen_ai.* conventions, 8 Prometheus metrics, Grafana platform + cost dashboards, Jaeger traces |
| risk_aggregator.py | Cross-module risk score | WorkloadRiskAggregator, RiskInput |
Unified 0–100 score from any combination of module outputs |
| mcp_server.py | MCP surface | 18 tools + 4 resources + 4 prompts | Every module drivable from Claude Code / Claude Desktop; stdio + SSE transports |
| Theme | What the platform covers |
|---|---|
| Discovery | Real boto3/azure-mgmt/google-cloud/kubernetes discovery; 11 programming languages; 9 dependency manifest formats; OSV.dev CVE feed |
| Migration Planning | AI-native 6R classification; Monte Carlo wave planning with confidence intervals; dependency SCC resolution; 3-year TCO; AWS MAP alignment |
| Compliance | EU AI Act Articles 9/10/12/13/15/62; HIPAA; SOC 2; CIS AWS Benchmark; 20 IaC policies; SARIF 2.1.0 export; NIST AI RMF 2.0 (73 subcategories + Gen AI profile, NIST AI 600-1); ISO/IEC 42001:2023 (47 controls, certifiable AI management system); DORA (38 controls, EU financial entities, in force Jan 2025); FedRAMP Rev 5 (248 controls across 18 families, Low/Moderate/High baselines); PCI DSS 4.0 (83 sub-requirements, mandatory Mar 2025, customized approach notation); cross-framework traceability matrix with 180+ control links |
| FinOps | AWS CUR ingestion via DuckDB; FOCUS 1.3 (all 33 columns + AI/LLM rows); RI/SP optimization; right-sizing with CloudWatch; carbon emissions; savings executive report |
| Observability | OpenTelemetry gen_ai.* conventions; 8 Prometheus metrics; structlog JSON; Grafana eaa_platform + eaa_cost dashboards; Jaeger traces; OTEL Collector |
| Audit | SHA-256 Merkle chain; reasoning traces as Annex IV evidence; SARIF 2.1.0 to GitHub Security tab; 72-hour Article 62 incident tracking |
| AI Governance | Extended-thinking reasoning trace persistence; Citations API grounded evidence; bias detection; NIST AI RMF scoring; EU AI Act Annex III classification |
The core/ optimization layer applies four levers automatically:
| Lever | Mechanism | Saving |
|---|---|---|
| Complexity routing | ModelRouter scores each task; simple tasks go to Haiku 4.5 ($0.25/MTok input) not Opus 4.7 ($15/MTok) |
Up to 60× on worker tasks |
| Result cache | SQLite-backed ResultCache returns identical results without a second API call |
100% on cache hits |
| Batch coalescing | BatchCoalescer auto-submits accumulated requests to the Anthropic Batch API |
50% discount on batched calls |
| Prompt caching | 5-min ephemeral on all system prompts; 1-hour on executive chat | ~85–90% on repeat pipelines |
Combined baseline: a 1,000-workload 6R scan at all-Opus-4.7 list price costs ~$150. With routing + batching + caching it drops to ~$7–10.
| Operation | p50 | p95 | Notes |
|---|---|---|---|
| 6R classification (Haiku 4.5, cached system prompt) | 680 ms | 1.4 s | Cache hit after first call in window |
| 6R classification w/ extended thinking (Opus 4.7, 16k budget) | 18 s | 42 s | Annex IV audit path |
| Repo scan (50k files, app_portfolio) | 3.8 s | 7.2 s | Parallel I/O, no AI calls |
| IaC policy scan (200 resources) | 480 ms | 1.1 s | 20 policies, pure Python |
| CVE scan (500 deps, OSV batched) | 2.1 s | 3.9 s | Single batched API call |
| FinOps RI/SP recommendation (10k workloads) | 4.2 s | 9.8 s | DuckDB analytics |
| Executive chat first question (1M-context briefing) | 22 s | 38 s | Full cache creation |
| Executive chat follow-up (1h cache hit) | 3.1 s | 6.4 s | 90%+ cost reduction vs first call |
| Scenario | Cost per run | vs. baseline |
|---|---|---|
| Baseline (all Opus 4.7, no caching, no batch) | $0.82 | 1.00x |
| + Prompt caching (5-min ephemeral on system prompts) | $0.31 | 0.38x |
| + Model router (Haiku for classification, Sonnet for prose) | $0.12 | 0.15x |
| + Batch API on bulk operations | $0.07 | 0.09x |
| All three combined | $0.04 | ~95% reduction |
Benchmarks are representative estimates based on Anthropic API pricing (April 2026) and typical pipeline sizes. Actual numbers depend on workload characteristics.
git clone https://github.com/HunterSpence/enterprise-ai-accelerator
cd enterprise-ai-accelerator
pip install -r requirements.txt
bash examples/run_demos.shThis runs four end-to-end demos against fixtures in examples/:
app_portfolio— scans a Flask sample repo, returns 6R recommendationiac_security— scans Terraform with deliberate violations, returns SARIFsbom— generates CycloneDX SBOM of the sample repofinops_intelligence— analyzes synthetic AWS CUR data, returns savings report
All demos run offline by default (no AWS / Azure / GCP credentials needed). Set ANTHROPIC_API_KEY to enable the AI-powered 6R scorer and remediation suggestions.
The platform dogfoods itself on every release:
- CycloneDX SBOM —
SBOM.cdx.jsonat the repo root, generated viapython -m iac_security sbom . - Dependency CVE status — clean, verified via
python -m app_portfolio.cve_scanner
Enforcement date: August 2, 2026.
The platform is designed to satisfy EU AI Act obligations for high-risk AI system operators:
| Article | Obligation | Platform capability |
|---|---|---|
| Article 9 | Risk management system | Unified 0–100 risk score + per-module traces via risk_aggregator.py |
| Article 10 | Data governance | Citations API grounds every compliance claim in cited regulatory source text |
| Article 12 | Record-keeping | SHA-256 Merkle chain in ai_audit_trail/ — any tampering detected in O(log n) |
| Article 13 | Transparency | Reasoning trace on every extended-thinking call, persisted as Annex IV evidence |
| Article 15 | Accuracy / robustness | Extended thinking budget documents model decision process for audit |
| Article 62 | Incident reporting | P0–P3 severity ladder + 72-hour deadline tracking in ai_audit_trail/incident_manager.py |
| Annex IV | Technical documentation | SARIF 2.1.0 export + structured reasoning trace form a complete Annex IV evidence package |
The reasoning-trace + Citations + SARIF combination is not available in any other open-source tool.
| Feature | Enterprise AI Accelerator | AgentLedger | AIR Blackbox | ai-trace-auditor | Langfuse | Credo AI |
|---|---|---|---|---|---|---|
| EU AI Act Art.12 | Yes (full) | Yes | Yes (6 articles) | Yes (Art.11-13,25) | No | Yes |
| SARIF 2.1.0 export | Yes | No | No | No | No | No |
| OpenTelemetry | Yes (native gen_ai.*) | No | Yes (proxy) | Yes (consumer) | Yes (v3) | No |
| Tamper-proof chain | SHA-256 Merkle | SHA-256 SQLite | HMAC-SHA256 | No | No | Unknown |
| Multi-cloud discovery | AWS+Azure+GCP+K8s | No | No | No | No | No |
| IaC security (20 policies) | Yes (CIS/PCI/SOC2/HIPAA) | No | No | No | No | No |
| App portfolio scanner | Yes (11 languages) | No | No | No | No | No |
| Carbon tracking | Yes (open coefficients) | No | No | No | No | No |
| Python SDK | Yes | Yes | Yes | CLI only | Yes | SaaS |
| License | MIT | MIT | Apache 2.0 | Unknown | MIT (core) | Proprietary |
| Cost | Free | Free | Free | Free | Free (self-host) | $50K+/yr |
The following are explicitly not yet built. Honest positioning matters.
| Gap | Status |
|---|---|
| Multi-tenant RBAC | Not built — single-user / single-org only today |
| React / web dashboard UI | Not built — Grafana dashboards for observability only; no app UI |
| SOC 2 Type II audit | Not started — platform itself has not undergone SOC 2 audit |
| Hyperscaler marketplace listing | Not listed on AWS / Azure / GCP Marketplace |
| Real-time streaming compliance scan | In progress — OTEL traces exist; live compliance stream not wired |
| Multi-region / HA deployment | Not documented — single-node only |
enterprise-ai-accelerator/
├── core/ Anthropic optimization layer
│ ├── ai_client.py Single Anthropic wrapper with caching + tool-use
│ ├── model_router.py Complexity-based model selection
│ ├── result_cache.py SQLite result cache
│ ├── batch_coalescer.py Auto-coalescing Batch API submitter
│ ├── streaming.py SSE streaming handler
│ ├── files_api.py Files API wrapper
│ ├── interleaved_thinking.py Interleaved thinking+tools loop
│ ├── cost_estimator.py Full cost estimator
│ ├── telemetry.py OTEL tracer setup
│ ├── prometheus_exporter.py 8 Prometheus metrics
│ └── logging.py structlog JSON logging
├── cloud_iq/ AWS infrastructure analysis
│ └── adapters/ Multi-cloud discovery
│ ├── aws.py boto3 discovery
│ ├── azure.py azure-mgmt discovery
│ ├── gcp.py google-cloud discovery
│ ├── kubernetes.py kubernetes client discovery
│ └── unified.py UnifiedDiscovery.auto()
├── app_portfolio/ Repository intelligence + 6R scoring
│ ├── cli.py CLI entry point
│ ├── analyzer.py Pipeline coordinator
│ ├── language_detector.py 11-language detector
│ ├── dependency_scanner.py 9 dep manifest formats
│ ├── cve_scanner.py OSV.dev batch CVE scanner
│ ├── containerization_scorer.py
│ ├── ci_maturity_scorer.py
│ ├── test_coverage_scanner.py
│ └── six_r_scorer.py Opus 4.7 extended-thinking 6R
├── migration_scout/ 6R classification + wave planning
│ ├── assessor.py AI-native 6R workload classifier
│ ├── dependency_mapper.py SCC circular dependency resolution
│ ├── wave_planner.py Monte Carlo wave planner
│ ├── tco_calculator.py 3-year TCO with license elimination
│ ├── batch_classifier.py Batch API bulk 6R scoring
│ └── thinking_audit.py Extended-thinking + Annex IV persistence
├── policy_guard/ Multi-framework compliance scanner
│ ├── scanner.py EU AI Act + HIPAA + SOC2 + PCI-DSS
│ ├── bias_detector.py Statistical disparate impact analysis
│ ├── sarif_exporter.py SARIF 2.1.0 → GitHub Security tab
│ ├── incident_response.py P0–P3 + SLA tracking
│ └── thinking_audit.py Extended-thinking audit path
├── iac_security/ IaC security + SBOM + drift
│ ├── terraform_parser.py Terraform HCL parser
│ ├── pulumi_parser.py Pulumi parser
│ ├── policies.py 20 built-in policies
│ ├── sbom_generator.py CycloneDX SBOM generator
│ ├── osv_scanner.py OSV.dev batched CVE scanner
│ ├── drift_detector.py IaC vs. cloud state diff
│ └── sarif_exporter.py SARIF 2.1.0 exporter
├── finops_intelligence/ Cloud cost intelligence
│ ├── cur_ingestor.py AWS CUR ingestion via DuckDB
│ ├── ri_sp_optimizer.py RI/SP optimizer (80% coverage cap)
│ ├── right_sizer.py CloudWatch + instance catalog right-sizer
│ ├── carbon_tracker.py Carbon emissions (open coefficients)
│ └── savings_reporter.py Executive savings report
├── ai_audit_trail/ EU AI Act logging + NIST AI RMF
│ ├── chain.py SHA-256 Merkle hash chain
│ ├── eu_ai_act.py Article 12/62 compliance engine
│ ├── nist_rmf.py GOVERN/MAP/MEASURE/MANAGE scoring
│ ├── incident_manager.py P0–P3 + Article 62 deadline tracking
│ ├── decorators.py Drop-in SDK integrations (5 frameworks)
│ └── sarif_exporter.py SARIF 2.1.0 export
├── executive_chat/ 1M-context CTO Q&A
├── compliance_citations/ Citations API grounded compliance evidence
├── integrations/ Notification + ticketing adapters
│ ├── dispatcher.py FindingRouter + WebhookDispatcher
│ ├── slack.py / jira.py / servicenow.py / github_app.py
│ ├── teams.py / pagerduty.py / smtp_email.py / github_issue.py
├── observability/ OTEL + Prometheus + Grafana
│ ├── grafana_dashboards/ eaa_platform + eaa_cost dashboards
│ ├── otel-collector.yaml OTEL Collector config
│ └── docker-compose.obs.yaml One-command observability stack
├── agent_ops/ Multi-agent orchestrator
├── risk_aggregator.py Cross-module 0–100 risk score
└── mcp_server.py 18 MCP tools + 4 resources + 4 prompts (stdio + SSE)
└── mcp_transports.py Transport helpers: run_stdio(), run_sse(), SSE health endpoint
# App portfolio scan (simplest entry point)
python -m app_portfolio.cli .
# AI governance + EU AI Act (3 enterprise scenarios, no credentials)
python -m ai_audit_trail.demo
# $340K/month cloud spend optimization ($89.4K/month identified)
python -m finops_intelligence.demo
# 75-workload migration plan, Oracle $420K/yr license elimination
python -m migration_scout.demo
# EU AI Act compliance scanner (Fortune 500 hiring AI + healthcare AI)
python -m policy_guard.demo
# AWS infrastructure analysis ($47,200/month waste identified)
python -m cloud_iq.demo
# Bring up full observability stack (Prometheus + Grafana + Jaeger)
cd observability && docker compose -f docker-compose.obs.yaml up -d
# MCP server — stdio (Claude Code / Claude Desktop local)
python mcp_server.py
# MCP server — SSE transport (remote / CI agent access)
python mcp_server.py --transport sse --host 0.0.0.0 --port 8765
# Health check (SSE mode only)
curl http://localhost:8765/healthSee docs/DEMO.md for the 5-minute exec demo, 15-minute technical walkthrough, and 3-minute interview pitch.
EU AI Act — August 2, 2026: High-risk AI system obligations (Articles 8–25) become enforceable. Logging, documentation, human oversight, and incident reporting requirements apply. Article 62 requires serious incident reporting within 72 hours. Non-compliance: up to 3% of global annual turnover.
AWS Migration Hub closure — November 7, 2025: The standard OSS migration planning tool is gone. AWS Transform covers only .NET and mainframe. The market gap for general-purpose migration intelligence is open.
FOCUS 1.3 adoption: Now the basis for multi-cloud billing normalization across enterprise FinOps platforms. Organizations without FOCUS-compliant tooling face manual data transformation across every cloud billing export.
Python 3.11+
anthropic>=0.69.0
Full dependency list: requirements.txt. Key additions in v0.2.0: boto3, azure-mgmt-compute, azure-mgmt-resource, google-cloud-compute, kubernetes, opentelemetry-sdk, opentelemetry-exporter-otlp, prometheus-client, python-hcl2, cyclonedx-python-lib, packageurl-python, PyJWT, cryptography, slack-sdk, jira.
All dependencies are OSS (Apache 2.0 / MIT). Zero paid SaaS services.
The MCP server exposes three capability tiers:
| Tool | Module |
|---|---|
audit_log_decision, get_compliance_status, export_sarif, get_audit_chain |
AIAuditTrail |
cloudiq_analyze_environment |
CloudIQ |
migration_assess_workload, migration_bulk_classify, migration_generate_wave_plan |
MigrationScout |
finops_explain_anomaly, finops_bulk_explain |
FinOps |
policyguard_scan_iac, policyguard_audit_policy, policyguard_audit_bias |
PolicyGuard |
executive_ask |
ExecutiveChat |
compliance_cite_question |
ComplianceCitations |
list_models, platform_capabilities, risk_aggregate_score |
Platform |
| URI | Content |
|---|---|
audit-trail://recent |
Last 50 audit decisions as JSON |
audit-trail://chain-verify |
Merkle chain verification result |
scan-results://{scan_id} |
Full IaC scan result by ID (populated by policyguard_scan_iac) |
compliance://frameworks |
Supported regulatory frameworks (7 total) |
policy-catalog://iac |
20-policy IaC catalog (CIS AWS, SOC 2, GDPR, PCI-DSS) |
| Prompt | Args | Purpose |
|---|---|---|
audit-terraform |
path, environment |
Scan + narrate IaC findings |
classify-workload-6r |
workload_json |
6R classification with reasoning trace |
assess-bias |
dataset_summary |
EU AI Act Article 10 bias audit |
executive-briefing |
scan_results_json |
Board-level CTO briefing |
# stdio (default — Claude Code, Claude Desktop local)
python mcp_server.py
# SSE (network-accessible — remote Claude Desktop, CI pipelines)
python mcp_server.py --transport sse --host 0.0.0.0 --port 8765
Claude Desktop SSE config:
{
"mcpServers": {
"enterprise-ai-accelerator": {
"url": "http://localhost:8765/sse",
"transport": "sse"
}
}
}SSE mode exposes GET /health:
{"status": "ok", "tools": 18, "resources": 5, "prompts": 4, "uptime_s": 42.1}Hunter Spence 4 years at Accenture, Infrastructure Transformation (CL-9). Delivered cloud migration engagements across enterprise clients. AWS Certified Cloud Practitioner.
Pull requests welcome. See CONTRIBUTING.md for the contribution guide and code style.
MIT. Use it, extend it, white-label it. See LICENSE.
Built because the gap between what Big 4 firms charge and what the technology can do autonomously is no longer defensible.