Evidence Signal
5-layer architecture · 46 data sources · 4 analytical models
Project Deep Dive
StealthCorp
Helping executives answer the question every board is asking: is our AI investment actually creating business value?
Helping executives answer a simple question: is our AI investment actually working?
Category creationEnterprise data architectureExecutive intelligence
Flagship Case Study
The category argument inside the product
If leaders cannot connect AI spend to accountable outcomes, they do not have strategy. They have activity. StealthCorp was designed to make AI capital legible.
StealthCorp is a strategic lens made concrete. The product is doing two jobs at once: creating a new executive category and giving that category a system leaders can actually operate from.
Where the market was blind
Enterprises could see licenses, vendors, and cost centers, but not whether AI capital was producing readiness, capability, or governance strength.
What the product reframed
The core move was to treat AI spend as a multi-layer intelligence problem spanning telemetry, org structure, benchmarks, and confidence-rated interventions.
Why it matters
It demonstrates that I can build from first principles when no obvious category exists yet, then translate that thinking into architecture and market narrative.
Positioning Lens
StealthCorp
This project shows category thinking as much as product thinking. It reframes AI investment as an operating and workforce intelligence problem, not a dashboard problem.
Signature strategic decisions
- Defined the wedge around accountable AI capital rather than generic analytics or observability.
- Made confidence and privacy posture explicit product features instead of footnotes.
- Built the architecture to span technical telemetry and workforce context so the output lands with operators and executives alike.
The Problem
What problem was worth solving?
I build things to solve a specific pain, not to decorate a portfolio. This is the pressure that made this project necessary.
Companies are spending aggressively on AI but cannot connect that spend to specific teams, outcomes, or business value.
Most tools show usage data or billing numbers in isolation — executives still cannot answer whether their AI investment is paying off.
StealthCorp connects AI spend data, organizational context, and market benchmarks to produce confidence-rated insights and prioritized recommendations.
The Build
What I built to solve it
A five-layer intelligence platform: (1) AI spend data from cloud providers and AI tools, (2) organizational context like teams, roles, and cost centers, (3) an analysis engine that connects spend to outcomes, (4) market benchmarks for peer comparison, and (5) executive outputs including an AI Readiness Score, governance risk register, and ranked recommendations.
L1Client TelemetryCloud billing APIs, usage metrics, OTEL instrumentation
Ingest
L2Org ContextIdentity providers, HRIS, cost centers, role mapping
Contextualize
L3Intelligence EngineNormalize, pixelate, triangulate across all data
Triangulate
L4External Intelligence33 external sources with reliability scoring
Deliver
L5OutputsAWEI Score, Governance Debt Register, Ranked Interventions
Click any node to explore details
Data sources
46 verified
14 cloud provider connectors, 3 organizational data sources, and 29+ external market intelligence feeds — all validated against vendor documentation.
Platform depth
5 layers
AI spend data, organizational context, analysis engine, market benchmarks, and executive outputs — each layer builds on the ones below it.
Analysis pipeline
3 stages
Raw data is normalized, connected to organizational context, and cross-referenced across sources to produce validated intelligence.
Analysis models
4 core models
Who is using AI (identity resolution), what it costs (cost attribution), where gaps exist (gap analysis), and how confident we are (confidence scoring).
Value
What changed because it existed
This is the clearest evidence of practical value, system leverage, and execution quality.
AI Readiness Score
0-100 composite score
A single number executives can track over time — combining adoption, usage efficiency, cost management, capability, and trajectory.
Action plans
7/14/30-day prioritized recommendations
Not just insights — concrete, prioritized actions grouped into immediate, near-term, and strategic windows.
Privacy
Never reads AI prompts or responses
Analyzes infrastructure and usage data only. Never sees what employees actually ask or what AI responds — privacy-safe by design.
Executive dashboard
Command Center
A dedicated interface for executives — confidence-rated signals and actionable recommendations, not another analytics dashboard.
Tech Stack
What it runs on
The stack matters here because it reflects design choices, constraints, and how the system was intended to scale or integrate.
Cloud billing APIsAI platform usage reportsOpenTelemetry-compatible trace ingestionDirectory + HRIS + ERP integrationsMarket benchmark data feedsConfidence scoring and intervention ranking engine
Build Story
How the thinking unfolded
This is the reasoning path behind the output, not just the finished artifact.
01
From dashboards to decisions
Most AI management tools show charts. StealthCorp produces confidence-rated recommendations that executives can act on — not just data to look at.
02
Honest about uncertainty
Every insight carries a confidence rating. The platform tells executives not just what it found, but how sure it is — because boardroom decisions require honesty about data quality.
03
Works across all clouds and AI providers
Not locked to one vendor. StealthCorp analyzes AI investment across Microsoft Azure, AWS, Google Cloud, OpenAI, Anthropic, and SaaS AI tools.
Capability Signal
What this project demonstrates
Each project is a proof point. These are the capabilities it most clearly reveals.
Enterprise Data Architecture
Designed a five-layer platform that connects AI spend data, organizational context, market benchmarks, and executive outputs.
Executive Intelligence Design
Outputs include an AI Readiness Score, governance risk register, and prioritized action plans — built for boardroom decisions.
Privacy and Trust Engineering
Privacy-safe by design — analyzes infrastructure data only, never reads AI prompts or responses. Every output is confidence-rated.
Executive UX Design
The Command Center turns complex intelligence into signals and recommendations executives can act on in minutes.
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Want to go deeper?
Ask the Latif AI Guide about the architecture, the commercial logic, or what this project says about how I approach hard problems.