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Cohort 014 · Pega Training & AI Agents · Enrolling Now

Pega Training
+ AI Agents

Master end-to-end ServiceNow and AI Agents with real-world, job-ready implementation skills. Build foundations in Python and SQL, ship pipelines with PySpark and Databricks, scale on Microsoft Fabric, and integrate Generative + Agentic AI into production data workflows.

3mo
duration
30+
modules
4.7/5
cohort rating
100k+
enrolled
Where our ServiceNow alumni work
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
What you leave with

Four things every Pega Training grad walks away with.

Most programs stop at tools. Ours makes you ship pipelines, platforms, and AI-powered data products that hiring teams can verify.

01
Agent-Ready skills
Build, deploy, and monitor AI agents that run production workflows — not chatbot toys.
02
A shipped capstone
A live React + FastAPI + LangGraph app on Kubernetes, monitored, observable, public URL.
03
Verifiable credential
2026 Agent-Ready rubric, graded 1–5 with a public verification URL recruiters can check.
04
Direct placement pipeline
GitHub + LinkedIn rewrite, resume rebuild, and warm intros to our 1,000+ hiring partners.
3 months, four phases

From "loads CSVs" to ships AI-native data pipelines.

Weeks 1–3 build Python and SQL depth. Weeks 4–7 cover Power BI and data storytelling. Weeks 8–10 move into PySpark, Databricks, and Fabric. Weeks 11–12 ship Generative + Agentic AI data agents.

WEEKS 1–3 · FOUNDATIONS

Python + SQL for ServiceNow

  • Python data structures, iterators, OOP
  • PostgreSQL querying, joins, windows, CTEs
  • Database design, indexing, optimization
  • Data file formats and transformation patterns
YOU SHIPA Python + SQL ingestion and transformation workflow over production-like datasets.
WEEKS 4–7 · ANALYTICS

Power BI and business intelligence

  • Power Query and source integrations
  • Star schema modeling and DAX measures
  • Advanced visuals, storytelling, KPI dashboards
  • Publishing, sharing, governance, refresh
YOU SHIPA complete Power BI reporting suite consumed by business teams and leaders.
WEEKS 8–10 · DATA PLATFORM

PySpark, Databricks, and Microsoft Fabric

  • Spark DataFrames, joins, windows, optimization
  • Databricks workflows, Delta Lake, Unity Catalog
  • Fabric OneLake, Lakehouse, Warehouse, RTI
  • Streaming, orchestration, and governance
YOU SHIPAn enterprise-grade ELT platform with scheduled jobs, observability, and governed data products.
WEEKS 11–12 · GENERATIVE + AGENTIC AI

Deploy AI agents that automate analytics, retrieval, and reporting across your data platform.

Use LLM APIs, LangChain, RAG, and LangGraph workflows with persistence and HITL. Add MCP tool access and enterprise guardrails. Your capstone connects pipelines, dashboards, and AI agents into a single production-ready data intelligence system.

Partner orgs (2026)48
Capstones deployed280+
→ Placement offers82%
Course curriculum

Seven sections. 65+ modules. The AI-native Pega Training stack.

Jump to any section on the left. Click a module to see topics, hands-on lab, and key technologies.

01

Platform Foundations and Case Management

How modern apps work, how teams ship them with Agile, where compute & cloud fit, and how AI plugs into the 2026 stack.
10 MODULES
WEEK 1
What You'll Learn:: What is Pega? The Enterprise Transformation Company and its mission Pega Infinity '24 — platform overview, capabilities, and 2026 features Core Pega products: Pega Platform, Customer Decision Hub, Customer Service, CLM, RPA Pega's customer base — industries and use cases: banking, insurance, healthcare, telecom, government The Situational Layer Cake — Pega's reusability and inheritance architecture PegaWorld 2026 — Blueprint Vibe Coding and platform direction Career pathways: System Architect, Business Architect, Decisioning Consultant, CLSA
What You'll Learn: : What is a Rule in Pega? The foundational concept behind all development Rule types: Case Types, Flows, UI Forms, Decision Rules, Data Objects, Integrations The Class hierarchy — Base, Abstract, and Concrete classes Inheritance in Pega — how rules resolve up the class hierarchy The Situational Layer Cake: Enterprise, Division, Unit, and Application layers RuleSet Stacks — how Pega finds and applies the correct version of a rule Versioning and locking — change management in multi-developer environments
What You'll Learn: App Studio — the low-code business-user-friendly development environment Dev Studio — the full-featured environment for system architects When to use App Studio vs. Dev Studio Dev Studio features: rule forms, class explorer, clipboard, tracer, and performance tools What is an Application in Pega? Components, channels, and organizational structure Case Types — the core unit of work in Pega applications Case hierarchies — parent cases, child cases, and case relationships Application overlay — extending built-in frameworks and industry templates
What You'll Learn : Business Process Management principles, goals, and Pega's approach Straight-through processing (STP) vs. human-in-the-loop workflows SLAs and escalation in business process management Pega's security model — authentication, authorization, and access control Operators, access groups, and access roles Privilege rules — defining what users can see and do Page-level, field-level, and process-level security ISO/IEC 42001 AI management certification and Pega Cloud security
What You'll Learn: The case life cycle — modeling real business work Stages, Processes, and Steps — the three tiers of case design Alternate stages — handling exceptions, cancellations, and special paths Stage-skipping logic and case-wide processes Designing life cycles for real-world scenarios: loan processing, insurance claims, HR cases
What You'll Learn: Assignments — human tasks within case workflows Router types: Work Queue, Specific Operator, Reporting Manager, Expression-based Workbaskets vs. worklists — team queues vs. individual inboxes SLA rule types: Assignment SLAs, Case SLAs, and Custom SLAs SLA intervals — Goal, Deadline, Passed Deadline, and Overdue Escalation actions — email notifications and cascading escalations Reporting on SLA compliance
What You'll Learn: Flow rules — the graphical automation backbone of Pega processes Flow shapes: Assignments, Decision shapes, Utility shapes, Connectors, Spinoffs Spinoffs — launching parallel subprocesses while the main flow continues Flow actions — what users do within assignments Connector rules — calling subflows and sub-processes modularly Best practices for flow design — readability, maintainability, and testing
What You'll Learn: Approval patterns — single-level, multi-level, and conditional approvals Authority Matrix — dynamic approval routing based on case data Parallel approvals and approval delegation Audit trails — capturing the full approval history Correspondence rules — automated communication via email, SMS, push Pega Pulse — in-platform collaboration and case notifications Multi-language correspondence for global deployments
What You'll Learn: Parent-child case hierarchies for complex multi-case scenarios Case sharing and case locking — preventing conflicting simultaneous edits Cover cases — master case linking related cases Linked cases — building relationships between independent cases Optional actions and case-wide actions for flexible workflows Industry deep dive: insurance claims with sub-cases for investigation, payment, and recovery
What You'll Learn: Lab: Set up Personal Developer Instance (PDI) via Pega Academy Lab: Build a full loan application case — stages, processes, assignments, SLA Lab: Implement a 3-level approval workflow with authority matrix routing Lab: Configure SLA escalation — email notification and supervisor reassignment Lab: Build a parent-child case hierarchy — master claim with sub-cases Lab: Use Blueprint to generate an initial application design from a business description
02

Data, Integration and User Interface

Modern React with hooks, Redux Toolkit and routing, paired with PostgreSQL fundamentals through query optimization.
10 MODULES
WEEKS 2–4
What You'll Learn : Data classes vs. work classes in Pega Properties and their types: Single Value, Page, Page List, Page Group, Value List The Pega clipboard — how data lives in memory during case processing Field-level values and data model best practices Mapping case data to external database structures
What You'll Learn: What is a Data Page? On-demand, in-memory data loading Data page scope — Thread, Requestor, and Node-level Data page source types: Data Transform, Activity, Report Definition, Connector, REST Refresh strategies and dependent data pages Data page parameters and caching for high-volume applications
What You'll Learn: Data Transforms — declarative, visual data manipulation Setting, appending, removing, and copying property values Conditional data transforms and default values on case create Activities — procedural step-based automation for complex logic Declare Expressions — automatically calculated property values with forward chaining Declare Triggers and Declare OnChange for event-driven automation
What You'll Learn: REST Service Rules — configuring outbound REST API calls SOAP Connector and Service rules — calling WSDL-based web services Authentication types: Basic, OAuth 2.0, API Key, and JWT Mapping REST/SOAP responses to Pega data structures Error handling — timeouts, errors, and retry logic
What You'll Learn: Report Definitions — querying Pega's database for reporting and lookups Lookup data patterns — populating dropdown lists and reference data Database integration via JDBC File-based integration — reading and writing CSV, XML, JSON Message-based integration — JMS and Kafka with Pega Real-time vs. batch integration patterns
What You'll Learn: Constellation overview — Pega's modern, component-based UI architecture DX (Digital Experience) API — how the UI layer connects to Pega's backend Constellation components — the pre-built design system library View types: Case View, Create View, Review View Building views in App Studio — templates, layouts, and field configuration Tabs, accordions, and collapsible sections for complex case UIs
What You'll Learn: Operator portals — customizing the user workspace in Pega Navigation pages, My Work list, and home screen configuration Mashup — embedding Pega case interfaces within external websites Mobile channel configuration and self-service portals UI Policies — controlling field visibility, enablement, and required status dynamically Validation rules — ensuring data quality before case processing continues Dynamic Select — populating dropdown options based on other field values
What You'll Learn: Report Definitions — querying and displaying Pega data in lists and summaries Summary list views — aggregate reports with grouping and totals Configuring list reports — columns, sorting, filtering, and formatting Dashboards — configuring operator workspaces with embedded reports Custom chart types: bar, pie, and bubble charts Exporting reports — Excel, CSV, and PDF outputs
What You'll Learn: Lab: Build a data model for an insurance claims application Lab: Configure a REST connector to call a customer data API and map the response Lab: Build a data transform to set default values and conditional logic on case create Lab: Create a Report Definition to query open cases by type and status Lab: Implement OAuth 2.0 authentication for a REST integration
What You'll Learn: Lab: Build a case creation view and case review screen in App Studio Lab: Implement UI policies — conditionally show fields based on case type selection Lab: Build a validation rule — enforce business rules on form submission Lab: Configure an operator portal dashboard with embedded work list and charts Lab: Build a self-service customer portal page for case submission Lab: Create a summary list report and embed it in a manager dashboard
03

Decision Rules, Business Logic and Customer Decision Hub

Python from fundamentals through OOP, then FastAPI — async APIs with Pydantic validation, SQLAlchemy, and JWT auth.
15 MODULES
WEEKS 5–8
What You'll Learn: When rules — the most fundamental decision rule in Pega Boolean logic: AND, OR, NOT, and comparison operators Testing When rules — run and preview tools in Dev Studio Applying When rules in flows, SLAs, UI policies, and routing Performance best practices for high-frequency evaluation
What You'll Learn: Decision Tables — matrix-based multi-condition decision logic Row ordering and conflict resolution Decision Trees — hierarchical branching decision logic When to use Decision Tables vs. Decision Trees vs. When rules Industry examples: loan risk scoring, insurance premium calculation, credit eligibility
What You'll Learn: Map Value rules — translating one value to another Pega built-in function library: string, math, date, and collection functions Custom functions — implementing Java utility functions callable from Pega rules Scorecard rules — combining multiple weighted factors into a single numeric score Common scorecard use cases: credit scoring, fraud detection, customer health scoring Scorecard integration with Customer Decision Hub for AI-enhanced decisioning
What You'll Learn: Predictive models in Pega — using ML models to predict outcomes Prediction Studio overview — Pega's AI/ML model management environment Importing external models (H2O, Python, R, PMML) into Pega Adaptive models — models that learn and update in real time from every interaction Propensity scores — probability-based action prioritization Model monitoring — performance, drift detection, and model refresh
What You'll Learn: Lab: Build a loan eligibility Decision Table — income, credit score, and loan amount matrix Lab: Implement a Decision Tree for insurance premium calculation Lab: Create a fraud risk Scorecard — weighted combination of 6 risk factors Lab: Configure Declare Expressions for automatic field calculations Lab: Import a PMML model into Prediction Studio and test propensity scoring
What You'll Learn: What is Customer Decision Hub? The AI brain for 1:1 customer engagement The Next-Best-Action (NBA) framework — always-on, inbound, and outbound decisioning Issues, Groups, and Actions — the CDH taxonomy for organizing offers and actions Treatments — how actions are delivered across channels CDH vs. traditional campaign management — always-on vs. batch campaigns CDH use cases: banking cross-sell, telco churn prevention, insurance retention
What You'll Learn: Next-Best-Action Designer — guided strategy configuration for business users Configuring Engagement Policies — eligibility, applicability, and suitability rules Prioritization and Arbitration — selecting the optimal action for each customer Business and ethics levers — balancing commercial objectives with customer wellbeing Strategy components: Filter, Aggregate, Prioritize, Set Property, Group By Chaining strategies — composing complex decisions from reusable components
What You'll Learn: How adaptive models power CDH personalization in real time The Pega learning cycle: present action, customer responds, model updates, better predictions Adaptive model configuration — learning contexts and response tracking Always-On Outbound — continuously evaluating which customers should be contacted Email channel configuration — CDH Email Designer and send scheduling SMS, push notification, and paid media integration Volume constraints and frequency caps to protect customer experience
What You'll Learn: Visual Business Director (VBD) — the marketing analytics workspace in CDH Analyzing action performance — what is being offered and accepted Interaction History — the full record of all CDH decisions and customer responses Decision audit — why a specific action was or was not selected for a customer A/B testing in CDH — comparing decision strategies and measuring lift Reporting for business stakeholders — dashboards from Interaction History
What You'll Learn: Lab: Configure a CDH implementation application — initial setup and channel configuration Lab: Build an action hierarchy — Issues, Groups, Actions, and Treatments for a telco cross-sell scenario Lab: Configure engagement policies — eligibility, applicability, and suitability for 3 actions Lab: Set up NBA Designer — prioritization, arbitration, and ethics and business levers Lab: Build an outbound email engagement program with CDH Email Designer Lab: Use Visual Business Director to analyze action performance
04

Customer Service, RPA and Cloud DevOps

Production ServiceNow : Power BI for analytics, then Microsoft Fabric — OneLake, Lakehouse medallion architecture, Spark, real-time intelligence, and Copilot.
25 MODULES
WEEKS 9–14
What You'll Learn: Pega Customer Service overview — Forrester Leader in Customer Service Solutions, Q1 2026 Architecture: Interaction Container, Service Case Types, Knowledge Management Customer context — 360-degree customer view for service agents Service case type design — capture, resolve, fulfill, and close flows AI-powered case resolution guidance — Knowledge Buddy for agent assistance Intelligent case routing based on issue type, customer segment, and agent skill Integration with Customer Decision Hub for NBA during service interactions
What You'll Learn: Pega Digital Messaging — unified chat, SMS, and social media interactions Chatbot configuration — conversational AI for self-service deflection Voice integration — CTI with screen pop for automatic customer context surfacing Interaction history — complete cross-channel record for every customer Knowledge articles — creating, organizing, and maintaining a knowledge base AI-powered knowledge search — surfacing relevant articles based on case context Knowledge Buddy — generative AI for instant answers from the knowledge base
What You'll Learn: Lab: Build a service case type for billing dispute — contact creation to resolution Lab: Configure omnichannel routing — skill-based routing for voice and chat Lab: Implement a chatbot for common queries — account balance, payment status, address change Lab: Configure Knowledge Buddy for AI-powered article suggestion during case work Lab: Connect CDH to the service interaction — next-best retention offer during service call Capstone: Present a complete Pega Customer Service implementation design for a telco
What You'll Learn: What is RPA? Robotic Process Automation concepts and enterprise use cases Pega's integrated RPA — RPA within the BPM and case management platform Attended vs. unattended robots — when to use each Pega Robotic Studio — the development environment for Pega bots Desktop adapters — connecting bots to Windows, web browsers, and Java apps Automation building blocks: Get, Set, Click, Invoke, Loop, and Condition actions Error handling — try/catch, retry logic, and fallback strategies
What You'll Learn: Browser-based RPA — automating web applications with Pega robots Dynamic web element identification — handling SPAs and dynamically loaded content Screen scraping — extracting structured data from web pages Calling robots from Pega case flows — embedding automation in business processes Robot task types: Start Task, Monitor Task, and End Task Attended automation patterns for agent assistance in customer service Unattended automation patterns for background batch processing Robot queue management and monitoring dashboards
What You'll Learn: Lab: Build a desktop bot that reads from CSV and enters data into a legacy Windows application Lab: Build a web bot that logs into a portal, extracts account data, and updates a Pega case Lab: Integrate a robot task into a Pega case flow — trigger bot from assignment, map output back to case Lab: Implement error handling and retry logic Lab: Configure a robot dashboard — monitor execution, errors, and throughput
What You'll Learn: Pega Cloud — cloud-native deployment on AWS infrastructure Pega Cloud tiers, SLAs, availability commitments, and disaster recovery ISO 9001:2015 and ISO/IEC 42001 certifications for Pega Cloud RuleSet versioning, locking, and packaging for deployment Pega Deployment Manager — automated deployment pipelines Pipeline stages: DEV to QA to UAT to PRODUCTION Branch development — parallel development by multiple teams Regional data residency — EU, APAC, and US data sovereignty
What You'll Learn: Unit testing in Pega — testing individual rules in isolation PegaUnit — the built-in unit testing framework Test Automation Framework (TAF) — UI-level automated testing Running automated regression tests on each deployment Pega Performance Analyzer (PAL) — identifying performance bottlenecks Common performance anti-patterns: heavy activities, large clipboard, slow data pages Caching strategies for high-volume applications Pega Guardian — automated performance monitoring and alerting
What You'll Learn: Agile development with Pega — sprint planning and iteration management Git integration — source control for Pega application artifacts Jenkins, Azure DevOps, and GitHub Actions integration with Pega pipelines Application Guardrails — Pega's built-in code quality standards OWASP Top 10 — securing Pega applications against common vulnerabilities Pega encryption — data at rest and in transit configuration GDPR and data privacy compliance — data masking, deletion, and consent Pega Guardsman security scan — identifying security issues in applications
What You'll Learn: Lab: Configure a multi-stage deployment pipeline in Pega Deployment Manager Lab: Write PegaUnit tests for a Decision Table and a data transform Lab: Run PAL on a case processing flow — identify and fix a performance bottleneck Lab: Run Pega Guardsman security scan — review and resolve findings Lab: Configure Application Guardrails and review compliance Lab: Package an application for production deployment — version, lock, and export RuleSet
05

GenAI, Blueprint and Agentic AI

The mathematical backbone behind every ML and DL model: linear algebra, probability, distributions, hypothesis testing, and applied statistics for ML.
5 MODULES
WEEK 15
What You'll Learn: Pega GenAI overview — how generative AI is embedded throughout Pega Infinity '24 Pega's AI portfolio: Blueprint, AI Coach, Knowledge Buddy, Prediction Studio, Adaptive Models, CDH ISO/IEC 42001:2023 certification — Pega's AI management governance framework Pega's multi-LLM approach — supporting multiple large language models AI in development: AI Coach for rule guidance, Blueprint for app design AI in runtime: Knowledge Buddy for agents, CDH for engagement, adaptive models for decisioning
What You'll Learn: Pega Blueprint overview — the generative AI design and workflow agent Blueprint capabilities: natural language workflow design, case design, data model, and integration The Blueprint design canvas — graphical workflow editor powered by AI Using Blueprint for legacy modernization — uploading documents, code, UI screenshots, and videos Agentic capabilities in Blueprint — AI agents analyzing legacy assets to generate modernized apps Blueprint security: regional data residency, client-level file storage, and federated access controls
What You'll Learn: What is Vibe Coding? The conversational development paradigm in enterprise context Pega Blueprint Vibe Coding (launched March 5, 2026) Text and voice interaction with app designs — real-time workflow, data, and logic refinement Switching between vibe coding and graphical drag-and-drop modeling seamlessly Combining AI speed with Pega's enterprise governance framework Accessing Blueprint Vibe Coding via the AI Assistant tab at [pega.com/blueprint](http://pega.com/blueprint) Real customer results: Proximus (one day to design, four months to production), Vodafone (seven months to one month)
What You'll Learn: AI Coach — in-platform guidance for business users and developers building rules AI Coach use cases: suggesting rule configurations, explaining errors, recommending best practices Knowledge Buddy — generative AI for instant answers from knowledge bases Knowledge Buddy integration with Pega Customer Service for real-time agent assistance Knowledge Buddy for self-service — customer-facing conversational knowledge access AI governance controls — controlling what the AI can and cannot answer
What You'll Learn: What is Agentic AI in the context of Pega? Autonomous, multi-step reasoning and action Pega's agentic architecture — how agents perceive, reason, act, and learn Building agentic workflows in Blueprint — conversational AI designing agentic sequences Case design for agentic processing — cases where AI drives decisions and actions Human-in-the-loop controls — when to pause agentic processing for human judgment Agentic AI in CDH — autonomous decision-making in customer engagement
What You'll Learn: Pega Client Lifecycle Management (CLM) — advanced agentic AI for client onboarding AI-powered KYC, document processing, screening, and risk assessment Moody's partnership — enhanced CLM and Know Your Customer capabilities Notes to Blueprint (January 2026) — AI-powered legacy Lotus Notes modernization Industry AI use cases: banking CLM, insurance underwriting, healthcare prior authorization Pega CLM agentic workflows — autonomous compliance and onboarding processing
What You'll Learn: Prediction Studio overview — Pega's centralized AI/ML model management environment Importing and deploying external models: H2O, Python, R, and PMML formats Adaptive model lifecycle — from initial training to production learning Model performance monitoring — click-through rates, accept rates, and lift analysis Prediction sets — grouping predictions for CDH Next-Best-Action strategies AI model versioning and governance — controlling which models are active in production
What You'll Learn: Connecting Prediction Studio models directly to CDH decision strategies Propensity scores in arbitration — how AI probability drives action selection Combining adaptive models with business eligibility rules for hybrid decisioning Real-time model updates — how CDH learns from every customer interaction Context weights — adjusting model sensitivity to different customer signals AI-driven audience segmentation for outbound engagement programs
What You'll Learn: Pega's ISO/IEC 42001:2023 AI management certification — detailed breakdown AI impact assessments — evaluating risk before deploying AI in production Explainability in Pega AI — explaining model decisions to regulators and customers Bias monitoring — detecting and mitigating unfair AI outcomes in Pega decisioning Data privacy and AI — GDPR-compliant AI in CDH and Prediction Studio Human oversight requirements — when regulators require human approval of AI decisions EU AI Act implications for Pega customers — high-risk AI use case classification and controls
Explainability in Pega AI — explaining model decisions to regulators and customers Bias monitoring — detecting and mitigating unfair AI outcomes in Pega decisioning
Tools you'll master

40+ tools, one production capstone.

Not a shallow tour. You'll use every one of these in at least one graded exercise.

R
React 18
RT
Redux Toolkit
TS
TypeScript
V
Vite
Nd
Node.js
Py
Python
FA
FastAPI
SA
SQLAlchemy
Pg
PostgreSQL
M
MongoDB
PB
Power BI
MF
MS Fabric
Np
NumPy
Pd
Pandas
Sk
scikit-learn
TF
TensorFlow
PT
PyTorch
HF
Hugging Face
SM
spaCy
OAI
OpenAI
LC
LangChain
LG
LangGraph
LS
LangSmith
MC
MCP
VD
Vector DBs
D
Docker
K
Kubernetes
G
Git
GH
GitHub
aws
AWS
Az
Azure
C
Cursor AI
Real-time projects

You don't watch videos. You ship software.

Three full-production projects, each threaded through the entire curriculum. By the capstone, you've built the whole stack around them.

Hero project · weeks 3–12

LMS analytics platform

Ingest learner events, build transformation layers, and publish executive and academic dashboards with AI-generated insight summaries.

PySparkDatabricksPower BILangGraphPostgreSQL
View project →
Enterprise · weeks 6–11

HRMS data pipeline

Build secure ETL workflows for employee, payroll, and performance datasets with governed semantic models and decision-ready KPIs.

MS FabricDelta LakePower BIUnity Catalog
Real-time · weeks 8–12

CRM intelligence stream

Create near real-time customer analytics with streaming events, automated anomaly flags, and AI-assisted executive reporting.

Structured StreamingKQLPower BILangChain
Capstone · weeks 11–12

Your AI ServiceNow agent in a real partner org.

Pick a real partner data problem. Deploy a production data pipeline and an AI agent that explains metrics, detects risks, and accelerates business decisions.

2026: 220+ deployed76% → placement offers
See capstone gallery →
Your instructor

Taught by engineers who shipped agentic AI to production.

Not a career trainer. A practitioner who still ships code.

AS
Aarav Sharma
Lead Instructor · ServiceNow & AI
React · FastAPI · PyTorch · LangChain
"A 2026 full-stack engineer doesn't stop at React + an API. They train the model, deploy it behind FastAPI, wrap it in an agent, and ship the whole thing to a real org. That's what we build, every cohort."
10 yrs
FULL STACK
2,400+
LEARNERS
4.9 /5
RATING

Aarav started as a React engineer at an Indian unicorn before leading platform teams across three continents. He's shipped React + FastAPI products for a healthcare network with 80M users, trained NLP classifiers in production for a top-3 bank, and — most recently — deployed the first LangGraph agent into a Fortune-500 insurer's claims pipeline.

His cohorts get two things other programs don't give you: a real engineer who still ships code, and a curriculum rewritten every quarter to match what hiring managers actually ask about.

FAQ

Questions we actually get — answered honestly.

If the answer you need isn't here, book a 20-minute advisor call. No-slides, no-pitch — just your questions.

No. About 40% of our ServiceNow cohort comes from non-CS backgrounds — mechanical, electrical, and commerce. The first phase is foundations by design. What you need: consistency and around 12–15 hours/week.
Plan for 12–15 hours: 2 live classes × 2 hours, 1 lab × 3 hours, and roughly 5 hours of asynchronous project work. Weekends are optional office hours with the TA team.
Yes. Every student gets a dedicated placement advisor from week 8 onwards — not a helpdesk. They review your resume, redo your LinkedIn, mock-interview you, and make direct warm introductions to our 1,000+ hiring partners. We track individual outcomes, not cohort averages.
Full refund within 7 days of cohort start, no questions. Pro-rata refund through week 4 if the program isn't working for you. We'd rather refund than have an unhappy alum.
You actually build. Sections 6 (ML), 7 (DL/NLP), and 8 (Generative + Agentic AI) are hands-on — you'll train classifiers, build a RAG pipeline, ship a LangGraph workflow, and deploy your capstone agent into a real partner org. Nothing in the AI track is theory-only.
You get the Agent-Ready 2026 credential, graded on a 1–5 band with a public verification URL. It's co-branded with our partner ecosystem (Salesforce Partner + ServiceNow), and it names the specific capstone artifact you deployed. Recruiters can verify in 10 seconds.
All three. On-campus at our Hyderabad flagship; online cohorts on IST and PST; weekend cohorts for working professionals. Every format ships the same three projects and the same capstone.
We'd rather pause your cohort than push you through. You can freeze your seat for up to 90 days and rejoin the next cohort without paying again. TAs run catch-up sessions every Saturday for anyone more than one week behind.

Cohort 014 starts 14 May 2026.
40 seats. 12 already claimed.

Book a 20-minute advisor call. We'll walk through the curriculum, match it to your current role, and show you two real capstones from cohort 022.

₹89,000
₹1,20,000
25% off · EARLY BIRD
3 MONTHS · STARTS 14 MAY · 40 SEATS · 12 CLAIMED

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