Current State
Privacy-preserving AI capital intelligence, now part of the Joan history.
Project
Fabriq
A Joan product proof for the question every board is asking: is our AI investment actually creating business value?
AI capital intelligence connecting spend to workforce outcomes.
AI spendWorkforce outcomesJoan historyPaused
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. Joan AI Investment Intelligence makes AI capital legible.
Joan AI Investment Intelligence 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
Fabriq
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
- Focused the product 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.
Joan AI Investment Intelligence connects AI spend data, organizational context, and market benchmarks to produce confidence-rated insights and prioritized recommendations.
The Build
What I built around the problem
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.
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
Five-part platform
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).
Reality Check
Current boundary
The point is not to pretend the work was clean. This is what the project taught, where it changed shape, or where the boundary still matters.
01
What this page should do
This page should explain why the project mattered without turning unfinished, private, or earlier work into a bigger claim than the public record supports.
02
Status
Fabriq is currently represented as paused. The useful parts of the story are kept visible, but the page should stay clear about what is current and what is history.
03
Boundary
The public version should stay specific, plain-spoken, and careful about what is still being built.
Value
What changed because it existed
This is the practical value of the work. Where a project is unfinished, shelved, or still changing, the value is tied to what it actually taught.
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.
Technical Layer
How the system is built
This is the implementation surface behind the work: the architecture choices, operating layers, integrations, and controls that made the project real enough to learn from.
Cloud billing APIs
AI platform usage reports
OpenTelemetry-compatible trace ingestion
Directory + HRIS + ERP integrations
Market benchmark data feeds
Confidence 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. Joan AI Investment Intelligence 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. Joan AI Investment Intelligence analyzes AI investment across Microsoft Azure, AWS, Google Cloud, OpenAI, Anthropic, and SaaS AI tools.
Capability Signal
What this project shows
These capabilities land best when they are tied to specific work rather than broad claims.
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.
Continue
Want to go deeper?
Ask the Latif AI Guide about the architecture, the commercial logic, or the hard lessons behind this project.