AI Analyst Lab

Cohort 1

Bootcamp 2

Cohort 2

Bootcamp 1

36 free lessons on evaluating, measuring, and improving AI-powered products. Full slide decks with written explanations. Weekly quizzes and a certification exam. What you'll learn<ul><li>Design observable AI systems with proper logging and trace architecture</li><li>Run structured failure analysis on production data</li><li>Translate AI quality metrics to user behavior and business results</li><li>Build evaluation workflows from offline tests through production monitoring</li><li>Make ship/iterate/pause/rollback decisions based on evaluation data</li></ul>

Free 6-week AI Evals Certification Course

Leave with a working AI analyst system on your laptop (skills, agents, data connections) and a finished analysis ready for Monday.

Principal Data Scientist @ Ontra 

Shane Butler

Principal Data Scientist @ Ontra | Ex-Stripe, Nextdoor, PwC, Appfolio

Data Science Leader |Ex-Microsoft

Sravya Madipalli

Data Science Leader@Superhuman (Prev. Grammarly)| Ex-Microsoft, eBay, Nextdoor

Head of Data @ Ontra AI | Ex-LinkedIn

Hai Guan

Head of Data @ Ontra | Ex-Nextdoor, LinkedIn, Pinterest, Meta

This is a hands-on weekend bootcamp (4 hours Saturday, 4 hours Sunday) where you build AI analysts powered by Claude Code. Not a lecture series. Not a demo you watch. You set up your own environment, build custom skills and agents, connect to real data, and leave with a working system that turns business questions into charts, narratives, and presentations in minutes.No Python. No SQL. No coding of any kind. Claude Code writes all the code. You direct the analysis.By Sunday evening, you will have a complete AI analyst repo on your computer: 45 skills, 27 agents, data connections, and a finished analysis you can share with your manager Monday morning. Everything runs locally. No vendor lock-in. No proprietary platform. Just a repo you own.AI is changing how teams make decisions with data. The bottleneck is no longer the analysis — it is knowing how to set up the tools. This bootcamp gives you that setup in a weekend. You will not learn about agentic systems. You will build one.

Build agentic data analysts for your company in one weekend. No code.

Set up Claude Code as your personal AI analyst environment

Build custom skills that encode analytics best practices as markdown

 Build agents that automate multi-step analysis workflows

Produce a complete analysis with charts, narrative, and slide deck

Connect your own company data and analyze it before you leave

Product managers who are tired of waiting in the data team's queue. Stop waiting. Analyze it yourself.

Data scientists who want to multiply their output. You know how to do the analysis manually. Now automate it.

Engineers who want to make data-driven decisions without hiring a data team.

Transform from consumer of analytics to independent operator - asking sharp questions, validating answers, shipping decisions.

Stripe

Nextdoor

PwC India

Ontra

AppFolio

Microsoft

eBay

Grammarly

Superhuman

LinkedIn

Pinterest

Ask Better Questions, Make Faster Decisions - with AI as Your Analyst

Frame Problems Like a Senior Data Scientist

Design Metrics That Actually Drive Decisions

Diagnose Root Causes Without Guessing

Design Experiments That Ship

Turn Analysis Into Action With AI as Your Copilot

Synthesize Insights and Prioritize for Impact

Product Managers, engineers, and operators who need analytical independence and frameworks to measure success and drive decisions.

Designers and researchers stepping into strategy roles who need to quantify impact, run experiments, and influence with data-driven insights

Data professionals who want to frame better questions, delegate technical execution to AI, and drive impact with agentic analytics tools.

Learn practical, hands-on methods to evaluate, measure, and improve AI products using data science to make better product decisions.

Principal Data Scientist, AI Evaluations

Principal Data Scientist, AI Evaluations at Ontra

With the recent leaps in model capabilities, it is becoming clear that much of the AI evaluation work previously requiring humans to build and run can be automated through agentic systems. This course teaches you how to build those systems, and where human judgment and input is still required.The foundational work of designing eval frameworks, defining quality signals, and structuring failure analysis remains critical. But the execution of that work, running evaluations, scoring results, iterating on improvements, and surfacing what matters, can now be handled by agentic workflows that operate continuously and at scale.This course equips you with the capability to build, test, and deploy automated evaluation systems using Claude Code. You will leave with working systems, not just conceptual understanding.The focus is on automation that is trustworthy. Systems that score their own output, flag uncertainty, and know when to surface decisions to a human rather than proceeding autonomously.The foundational content on AI evals for product development is available free at <a target="_blank" rel="noopener noreferrer nofollow" href="https://aianalystlab.ai">aianalystlab.ai</a>. If you are new to AI evals, start there.

Build the agentic systems that automate your AI evaluation workflow from end to 

Build agentic evaluation workflows in Claude Code

Automate scoring and quality assessment

Implement iterative improvement loops

Scale experiment execution

Integrate with production data and decision-making

You will get the most out of it if you are actively working on, or about to ship, AI features in a real product.

Real-World AI Product Experience

Product Managers: Want automated eval systems for your team? Learn what is possible, reliable, and where human oversight stays essential.<pre><code class="language-markdown"></code></pre>

Data Scientists &amp; Analysts: Spending more time running evals than learning? Build systems that handle execution so you focus on decisions.

Engineers and ML Engineers: val bottlenecks slowing your release cycle? Automate repetitive work and ship with speed and confidence.

Build Custom Annotation UIs for AI Evals

Know when you need annotations for ground truth, production review, and continuous discovery.

Identify where annotation fits in the AI evaluation lifecyle

Choose the right interface patterns so reviewers can judge quality without fighting the UI.

Design annotation tasks that match the evaluator’s workload

Specify what the UI must show, what data it reads and writes, and how to standardize to make work is repeatable.

Define a lightweight “custom UI” blueprint that scales

Learn Agentic Analytics workflows and AI Eval design. Get feedback on what you’re building from other AI builders.

Join AI Builders Slack Community. Link: bit.ly/ai-connect

Design an AI Evaluation Plan

Principal Data Scientist at Ontra

Decompose a real AI feature into inputs, context, outputs, UX, and outcomes, and list what to evaluate at each step.

Break an AI feature into evaluable steps

For each step, specify what evidence would justify ship, iterate, or stop, and what “evidence” is misleading.

Define evidence that supports a ship decision

Spot what’s missing in logging, annotation, or metrics, then capture it as clear next steps in the plan.

Identify gaps that block confident evaluation

Measure AI Impact with Causal Inference

Use it when you cannot randomize but still need decision-grade evidence for product or business impact.

Know when you need causal inference

Panel regression, diff-in-diff, and propensity matching: what each assumes and when each is appropriate.

Choose among three practical methods

Learn simple checks to spot confounding risk and communicate uncertainty without hand-waving.

Stress-test credibility before you trust it

Crack Product Sense & Analytics Interviews with AI

Senior DS Leader (Ex-Microsoft)

Head of Data at Ontra, Ex-LinkedIn

Practice product sense and analytics questions with AI that pushes, probes, and mirrors real interviews.

Work with AI as an interview prep partner

Learn how to use AI to evaluate your responses across scoping, metrics, tradeoffs, and decisions

Assess your interview skills and pinpoint gaps

Structure answers consistently and use AI to test, refine, and improve them across repeated practice.

A clear framework for product sense & analytics interviews

Design Product Analysis with Claude Code

Head of Data at Ontra

Senior Manager, Data Science 
@Superhuman (Prev. Grammarly)| Ex-Microsoft

Use Claude Code to formalize the decision, success metrics, guardrails, and “what changes our mind” thresholds.

Turn vague requests into decision-grade briefs

Generate a structured 1-pager: scope, cohorts, hypotheses, methods, outputs, plus risks and mitigations.

Build a full analysis design doc in Claude Code

Surface confounders, selection effects, baseline gaps, and scope creep. Add checks that reduce risk of failure.

Validate the plan in Claude Code before execution

Storytelling & Presentation Building Using Claude Code

Use a 4-point checklist to cut data dumps into focused narratives with clear asks.

Structure data findings into executive stories

Go from raw data to a complete slide deck with charts, narrative, and recommendations in minutes.

Build stakeholder presentations with Claude Code

Use an AI story structurer to identify the so-what, choose the right evidence, and craft a clear ask.

Learn directly from AI Analyst Lab

AI Analyst Lab

Courses

Build AI Analysts in Claude Code

AI Analytics for Builders

Automate AI Evals with Claude Code

Lightning Lessons