Claude Certified Architect
capability building,
designed for your organisation.
A custom-built corporate programme for AI engineers, senior software engineers, solution architects, ML engineers, and senior developers (3+ years) building production-grade applications on the Anthropic Claude API and ecosystem. We design the curriculum around your tech stack, project archetypes, and target business outcomes — delivered by domain-expert trainers and reinforced through AI-evaluated assessments.
A modular syllabus, built to be tailored.
Below is our reference curriculum. Every syllabus we deliver is tailored to your customer-specific requirements module depth, sequencing, lab environments, and capstone projects are adapted to your team's starting point, tech stack, and target outcomes.
- Claude Opus 4.x, Claude Sonnet 4.x, Claude Haiku 4.x capability, cost, latency trade-offs
- Claude.ai vs. Anthropic API vs. AWS Bedrock vs. GCP Vertex when to deploy where
- Claude Code, Computer Use, MCP the agentic surface
- When Claude is the right choice (and when it isn't)
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Enterprise learning solutions built for corporate teams.
Go beyond standard classroom delivery with enterprise-ready learning infrastructure, managed execution, capability insights, and production-like practice environments designed for corporate scale.
Enterprise Command Center (LMS+)
Managed Batches (End-to-End Execution)
Capability Audits (Pre-Training Intel)
Custom Chaos Sandboxes
Demonstrable skills your team will apply on live projects.
Architect production applications on Claude
Single-prompt, RAG, multi-step, agentic with the right pattern selected by use case.
Master Claude-specific features
Prompt caching for cost reduction, batch API for high-throughput jobs, Computer Use for browser automation, MCP for portable tooling.
Use Claude Code as an engineering tool
Agentic coding workflows that compress engineering work pair-programming, refactoring, codebase navigation, test generation.
Pass Anthropic-aligned certification
GSDC + Anthropic-aligned curriculum; Claude Architect-level practitioner certification.
Ship production Claude application
Capstone deliverable: working production-grade Claude application with full observability, cost optimisation, and red-team report in Claude corporate training.
Reduce Claude operating costs by 40–70%
Prompt caching, batch API, model selection, and prompt-engineering techniques applied to cohort capstones.
Where your team is now vs where they'll be after the programme.
Where most teams start
- ·Has used Claude.ai casually but never built a production application against the Anthropic API
- ·Limited fluency with Claude's prompting style (XML tags, careful-by-default behaviour, Constitutional AI patterns)
- ·Unaware of Claude-specific features: prompt caching, batch API, Computer Use, MCP servers, Projects, Artifacts
- ·Cannot decide when to use Claude vs. GPT vs. Gemini for a given enterprise task
- ·No working knowledge of Claude Code as an agentic coding tool
- ·Unfamiliar with Claude's deployment options (Anthropic API, AWS Bedrock, GCP Vertex)
Where they'll arrive
- ✓Claude API mastery fluent across messages API, streaming, tool use, batch API, prompt caching, with cost-aware patterns
- ✓Claude prompting style leverages XML tags, system prompts, multi-shot, extended thinking, and Claude's careful-by-default behaviour
- ✓Claude Code fluency uses Claude Code for agentic coding workflows in real engineering work
- ✓MCP server development builds, deploys, and integrates Model Context Protocol servers
- ✓Production deployment has shipped a Claude-based application across Anthropic API, AWS Bedrock, or GCP Vertex with proper observability
- ✓Architecture judgement defends Claude vs. GPT vs. Gemini choices on technical merit
Built for L&D outcomes, not seat counts.
API-first engineering discipline
Learners move from ad-hoc Claude usage to production-grade API patterns messages, streaming, tool use, prompt caching, and batch API with cost-aware design.
Claude-specific feature mastery
The programme covers every Claude-specific capability: prompt caching, batch API, MCP servers, Computer Use, Claude Code, Projects, and Artifacts not generic AI theory.
Hands-on labs on every module
Every module includes a production lab. Learners build real MCP servers, instrument observability, deploy on Bedrock or Vertex, and ship a capstone application.
Measured cost reduction
Prompt caching and batch API techniques are applied to cohort capstones with documented 40–70% reduction in Claude operating costs.
Enterprise deployment readiness
Learners cover AWS Bedrock Claude deployment, GCP Vertex, and direct Anthropic API with security, IAM, VPC, and regional sovereignty considerations.
Architecture judgement built in
Engineers leave able to defend Claude vs. GPT vs. Gemini decisions on technical merit model selection, cost modelling, and deployment trade-offs included.
A four-milestone path from skill gap to client-ready.
Ecosystem & API Foundation
Establish a production mental model of the Claude ecosystem model tiers, deployment options, API surface, and Claude-specific behaviours including XML prompting and careful-by-default patterns.
Claude Feature Engineering
Deep-dive labs on prompt caching, batch API, MCP server development, Claude Code agentic workflows, and Computer Use automation the Claude-specific capabilities that differentiate production applications.
Production Architecture
RAG vs. long-context decisions, AWS Bedrock Claude deployment and GCP Vertex deployment patterns, observability instrumentation, cost governance dashboards, and red-team evaluation.
Capstone & Certification
Each learner team designs, builds, deploys, and presents a production-grade Claude application. Panel review by NovelVista AI Practice. Outputs eligible for Anthropic ecosystem showcase. CCA-F certification alignment included.
Want this curriculum aligned to your tech stack and project archetypes?
Why enterprise teams choose the B2B engagement model.
Trusted by Industry Leaders for Enterprise AI Upskilling
See why CEOs, CTOs, and business leaders collaborate with NovelVista
to discuss the future of AI, digital transformation, and workforce readiness.
- Exclusive AI leadership summits featuring enterprise decision-makers and technology experts
- Recognized corporate training partner for AI, Agile, DevOps, ITSM, and cybersecurity programs
- Trusted by organizations to build future-ready teams with practical, industry-focused learning
- Real conversations, real business challenges, and actionable AI transformation insights from industry leaders
Learn from domain experts with 15+ years of experience.
"AI transformation is not just about adopting new tools it’s about helping organizations build intelligent systems, scalable workflows, and future-ready teams that can innovate with confidence."
Taught by people who've actually shipped the work.
Built for L&D leaders and their learners.
Who this is for
- ·AI engineers and senior software engineers building production applications on the Anthropic Claude API
- ·Solution architects designing enterprise Claude-based systems across Bedrock, Vertex, and direct API deployments
- ·ML engineers integrating Claude into RAG pipelines, agentic workflows, and data processing systems
- ·Senior developers (3+ years) who want to move from casual Claude usage to certified production-grade capability
- ·L&D leaders and engineering managers commissioning a structured AI architect course for their teams in India and globally
Pre-requisites
- ·3+ years of software engineering, ML engineering, or solution architecture experience
- ·Working knowledge of REST APIs and at least one backend language (Python, Node.js, or equivalent)
- ·Familiarity with cloud infrastructure concepts (AWS, GCP, or Azure) deep expertise not required
- ·Enterprise cohorts should align on data classification and API key governance before programme start
Trusted by L&D leaders across the world.
"The programme gave our engineers a structured path from Claude.ai usage to production API deployment. The prompt caching and MCP modules alone justified the investment."
"Our solution architects could not previously articulate Claude vs. GPT trade-offs to clients. After the programme, they defend those decisions confidently with cost and latency data."
"The capstone was the differentiator. Teams shipped real Claude applications with observability, cost dashboards, and red-team reports not slide decks."
Questions L&D teams ask before signing.
Claude is often preferred by developers for long-context reasoning, codebase understanding, structured outputs, tool use, agentic workflows, and Claude Code-based development. Claude Code can read a codebase, edit files, run commands, and work across developer tools. ChatGPT, especially with Codex, is also strong for coding, code review, debugging, and shipping code, so the difference is less about “which is best” and more about workflow fit. Claude is usually taught with a stronger focus on Anthropic API, Claude Code, MCP, Bedrock, tool use, and enterprise agent workflows.