The instinct
to see AI clearly.

Not a survey of AI topics. A structured workflow — from process inspection to a defensible ROI figure — that mirrors exactly what your organization runs when launching an AI initiative.

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Not another AI course

Every other program starts after the decision. MitraAI operates at the diagnostic layer that precedes it.

Start after you've already decided to deploy AI

MIT, Wharton, Google courses teach deployment to organizations already committed. They assume the decision is made. They address the how, not the whether.

The diagnostic spine before you spend a dollar

Humans already run algorithmic processes intuitively. MitraAI makes that recognition explicit — teaching the instinct to see whether AI belongs in a process at all, before approving budget or deployment.

Three questions. Every AI proposal.

If the team presenting to you cannot categorize their problem as one of these three types, the initiative is not ready.

Prediction Problem?

You have historical data and want to know what happens next. Forecasting, churn scoring, demand planning.

Regression · Classification · Time-series

Optimization Problem?

You have constraints and want the best allocation of resources, time, or cost. Scheduling, routing, prioritization.

Greedy · Dynamic programming · Topological sort

Pattern Recognition Problem?

You have signal buried in noise and want to surface it consistently at scale. Fraud, segmentation, document processing.

Clustering · Anomaly detection · NLP · Computer vision

A forward-pass.
Not a topic survey.

Each module's output artifact is the next module's input. This mirrors the exact workflow an organization runs when launching an AI initiative.

  1. Strategic Decision-Making
    The three diagnostic questions. Prediction, optimization, or pattern recognition — and how to tell before approving anything.
    Diagnose
  2. Understanding Data
    Quality, types, bias, and AI readiness. Why the data layer is where most initiatives fail before they start.
    Diagnose
  3. Algorithms and Decision Context
    The 25 foundational patterns, errors, and tradeoffs. Algorithms are formalized human reasoning — named, mapped, and usable.
    Diagnose
  4. Recognition
    Mapping algorithms to organizational processes. Five org models, their embedded algorithms, and where AI can formalize them.
    Decide
  5. Recognizing AI in Existing Tools
    Your org is already an AI user. Outlook, Salesforce, Workday, ServiceNow. Governance changed when AI started acting, not just recommending.
    Audit
  6. Building the Business Case
    NPV, IRR, payback — and the with/without delta that turns "we should do AI" into a fundable CFO proposal.
    Fund
  7. Deploying Without Breaking
    MVP, Blue-Green, and the Crawl-Walk-Run discipline. The gap between funded and live is where most AI initiatives die.
    Deploy
  8. Psychology of Metrics Framing
    The same number means different things to different roles. How to present accurate data through the frame most legible for each decision-maker.
    Measure
  9. Roles and Functions Required for AI
    The Human-AI RACI model and accountability structure. The Super Child Principle. Accountability assigned before deployment, not after failure.
    Govern
  10. Governance and Continuous Alignment
    Managing AI systems after deployment. Data drift, concept drift, feedback loop failure. The structures that keep AI aligned with the intent of those who authorized it.
    Sustain
  11. Bonus: AI Orchestration
    Agentic AI, multi-model pipelines, orchestration patterns for leaders governing systems that govern themselves.
    Orchestrate

Get notified when Module 1 drops.

One email. No pitch. Just the publish date.

What no other program has built

The difference is structure, not subject matter. No competitor organizes by deployable workflow.

  • Start after the deployment decision is made
  • Organized by AI topic area, not organizational workflow
  • AI literacy as the end goal
  • Separate courses: strategy, data, finance, governance
  • No diagnostic spine before the commitment
  • Diagnostic layer that precedes the commitment
  • Forward-pass: Diagnose → Decide → Fund → Deploy → Measure → Govern → Sustain
  • Speed-to-decision is the outcome — process inspection to defensible ROI
  • One integrated workflow spanning CS, finance, org design, cognitive science
  • 9 proprietary frameworks: 9-Yard, Triple-A, Human-AI RACI, With/Without, and more

Start free. Go as deep as the decision requires.

The diagnostic spine is free. The implementation layer — templates, capstone, evaluation — is the paid product.

Tier 1
Free
No email required
YouTube Series
  • All 10 core modules, full content
  • Slide decks at algorithmicthinker.com/decks
  • Complete diagnostic frameworks
  • Share with your team
Watch on YouTube
Tier 2
$1
per scenario exercise
Scenario Exercises
  • Modules 6–10 scenario-based exercises
  • Tests application, not recall
  • No right or wrong — evaluate your reasoning
  • Commitment signal before capstone
Tier 3 — Most complete
$499
one-time · full bundle
Professional Bundle
  • Bonus Orchestration module
  • Capstone with AI/peer/instructor evaluation
  • DCF model, RACI, Business Case templates
  • Open Badge certificate
Tier 4
$4,000
invitation only
Vineyard Weekend
  • 12–14 curated senior executives
  • Application-gated, never advertised
  • All meals and wine included
  • Introduced via post-capstone video only

Built by someone who has run these decisions.

Nandeep Nagarkar is the founder of SVAG Labs and creator of MitraAI. 25+ years of enterprise technology and transformation experience. Published author on AI leadership.

MitraAI was built because no program existed that addressed the diagnostic and governance layer upstream of deployment — where most AI initiatives actually fail.

LinkedIn →     YouTube →

Start with the free modules.
Build the instinct.

Ten modules. Full content. No email required. The application layer is there when you're ready.

Watch on YouTube — free Download the decks →