Enterprise AI training · Live bootcamps

Enterprise AI bootcamps. Defined outcomes.

Outcome-led AI training for engineers and architects, taught by enterprise architects and CTOs with hands-on enterprise AI experience. Build, deploy, test, and defend a working system—not just follow lessons.

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Enterprise AI programs

Build AI that survives production reality.

Both AI tracks produce a deployed system. Choose Enterprise AI System Design to own the engineering inside your company, or the Field-Readiness track to deliver that system in a customer environment.

01 · Enterprise AITBC

Enterprise AI System Design

Design and build an enterprise agentic AI system from first principles through production readiness.

Work through agent architecture, retrieval, evaluation, security, deployment, and operations while extending one application into a reviewable capstone.

Draft course outcomes

A production-shaped agent application and architecture
An evaluation suite covering quality, safety, and regressions
A deployed capstone with security and operations evidence

Best for

Enterprise architects, CTOs, software and platform engineers, and AI practitioners building beyond prototypes.

Course Design in Progress →

02 · Field-Readiness Track16 weeks

AI Forward Deployed Engineer

Deliver agentic AI at the customer’s door, not only in your own stack.

Build the production system, then scope it from a vague brief, integrate it into a deliberately messy environment, debug it live, and defend an adoption plan.

You leave with

A scoped, approvable technical proposal
A secured, integrated, cost-optimized cloud deployment
Live incident practice and a full engagement simulation

Best for

Forward Deployed Engineers, implementation consultants, solutions engineers, and customer-facing delivery teams.

Get FDE Track access →

Agentic AI specialization series

Build depth in the systems behind agents.

Four focused 8–10 week bootcamps for engineers and architects. Each one centers on a defined production outcome and is taught by practitioners with enterprise AI experience.

Agentic RAG

Build an agent that plans retrieval across enterprise documents and live sources, uses tools when needed, and returns grounded answers with evidence.

8–10 weeksGrounded research agentAsk about this series →

GraphRAG & knowledge graph agents

Resolve multi-step enterprise questions by combining graph relationships with source documents, then show the path behind each answer.

8–10 weeksMulti-hop answer systemAsk about this series →

Agentic data analysis

Build an agent that investigates business questions with governed data tools, validates its analysis, and explains results with traceable evidence.

8–10 weeksGoverned analysis agentAsk about this series →

Agent evaluation & observability

Measure task success, groundedness, safety, latency, and cost; trace failures and build a repeatable evaluation loop for agent releases.

8–10 weeksEvaluation harnessAsk about this series →

Quality engineering track

Take one test suite from first structure to production ownership.

Two sequential eight-week courses use the same evolving application. Start with a maintainable Playwright foundation, then extend it into API coverage, CI/CD, observability, and deliberate AI-assisted QA.

03 · QA Course 18 weeks

Core Playwright & Testing Frameworks

Write Playwright tests the way production suites are actually built.

Build Page Object Model structure early, then add fixtures, data-driven coverage, test isolation, parallel execution, BDD scenarios, and stakeholder-readable reporting.

You leave with

A TypeScript-first Page Object Model suite
Reliable data-driven and parallel execution
A BDD-structured, graded end-to-end capstone

Best for

QA engineers, SDETs, and developers who know basic Playwright and want a suite that remains maintainable as the product changes.

Start with Core Playwright →

04 · QA Course 28 weeks

Advanced Playwright & Production Automation

Own automation as a production system, not a folder of scripts.

Extend the same suite into hybrid API/UI coverage, production auth, visual regression, scalable framework design, containerized CI/CD, dashboards, and Playwright MCP workflows.

You leave with

A hybrid API and UI automation architecture
A containerized, observable CI/CD pipeline
An MCP-driven, AI-augmented production capstone

Best for

Engineers already comfortable with Playwright fundamentals who are ready to own automation architecture and delivery at scale.

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The learning contract

Build. Prove. Defend.

The subject changes by program. The production standard does not.

01 / Build

One system grows every week

Each module adds capability to the same project, so architecture decisions and technical debt remain visible.

02 / Prove

Evidence replaces “it works”

Tests, evaluation results, logs, failure handling, security controls, and pipeline behavior form the assessment.

03 / Defend

Explain the tradeoffs live

Present the capstone, respond to review, and show that the system can be owned after the demo ends.

Live instructionBuild-alongs and direct feedback

Graded workProduction-readiness criteria

Working capstoneReviewable system or test platform

Earned certificateAwarded after evaluation and defense

How we measure progress

Expertise is tracked, not assumed.

Every module produces a score against explicit criteria, not a completion checkbox—so both the participant and their manager can see exactly what improved and what still needs work.

Weekly skill checkpoints

Each module is graded against a production-readiness rubric—architecture choices, error handling, test coverage—so skill gaps surface early, not at the final review.

Evaluation harness scores

The evolving system is scored on accuracy, latency, cost, and safety checks every week, giving a running, quantitative trend line instead of a single pass/fail moment.

Live capstone defense

Participants defend architecture decisions and tradeoffs under live questioning—verifying they can own and extend the system, not just narrate a demo.

For your organization

You get more than a certificate: a working system your team can inspect, a graded evidence trail showing where each engineer is strong or still growing, and people verified—by defense, not self-report—to extend and operate what they shipped.

What participants ship

Portfolio evidence that shows how you work.

Choose a production-shaped domain, then carry it through implementation, evaluation, deployment, and review.

Enterprise AI

Grounded knowledge assistant

Agent platform

Tool-using operations agent

Field delivery

Customer engagement simulation

Incident response

Live debugging defense

Playwright Core

BDD end-to-end suite

Playwright Advanced

Hybrid API/UI pipeline

AI evaluation

Regression and observability harness

AI-augmented QA

MCP-driven automation workflow

Practitioner-led

Learn from people who have owned the outcome.

Every mentor and advisor has worked inside the industry, not just around it—shipping production-ready applications and agentic AI systems for Tier 1 and Tier 2 enterprise customers. The failure modes they teach are ones they've hit and fixed themselves.

AI systems + cloud architecture

Amit Kayal

Technology leader focused on AI-first, cloud-native enterprise platforms, agentic AI architecture, production operations, and AWS-native systems—with production-ready agentic AI deployed for Tier 1 and Tier 2 enterprise customers.

LinkedIn ↗

Quality engineering + test automation

Avishek Das

QA architect with 13+ years building quality engineering and automation at scale, including Playwright frameworks and evaluation systems for AI-native products serving Tier 1 and Tier 2 enterprise customers.

LinkedIn ↗

Tell us the role you are targeting. We’ll map the right program.

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