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)
Want the full module-by-module syllabus, sample assignments, and pricing?
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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.
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, 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.
Prompt discipline, not prompt luck
Learners move from trial-and-error prompting to named patterns such as role prompting, few-shot, prompt chaining, and self-critique.
Reusable team assets
The programme produces Custom GPTs, reusable workflow templates, and a shared prompt library that teams can govern and scale.
Daily productivity workflows
Labs focus on email, reports, slides, meetings, spreadsheets, research synthesis, and role-based business assignments.
Measured time savings
Capstone workflows document recurring task compression, review-cycle reduction, and before/after productivity improvements.
Responsible enterprise use
Learners practise confidentiality, IP, bias detection, verification checklists, and safe-use protocols before adoption at scale.
Sustainment built in
30-day, 60-day, and 90-day check-ins help learners keep pace as ChatGPT features and frontier models evolve.
A four-milestone path from skill gap to client-ready.
Foundation & baseline
Establish a working mental model of ChatGPT, frontier models, tokens, context windows, hallucination risks, and model-selection trade-offs.
Prompt engineering labs
Learners practise CRISPE, SPEAR, role prompting, constraint-led prompting, few-shot prompting, self-critique, and prompt iteration on real work scenarios.
Custom GPTs & workflow automation
Each learner builds reusable GPTs and connects ChatGPT to productivity tools for email, documents, spreadsheets, meetings, and research workflows.
Capstone & sustainment
Learners demonstrate a personal AI productivity system and continue with prompt-of-the-week, model-of-the-month, and 30/60/90-day check-ins.
Want this curriculum aligned to your tech stack and project archetypes?
Why enterprise teams choose the B2B engagement model.
Domain-expert trainers, not professional presenters.
"My job isn't to teach ChatGPT as a tool — it's to help professionals build repeatable AI workflows, verify the output, and reclaim hours from routine work."
Taught by people who've actually shipped the work.
Built for L&D leaders and their learners.
Who this is for
- ·Knowledge workers who want to apply ChatGPT productively in their daily workflows
- ·Business analysts, consultants, marketing professionals, project managers, and individual contributors
- ·Teams that use ChatGPT for occasional drafting but need reliable, business-grade outputs
- ·Managers looking to establish team-wide prompt standards and safe-use protocols
- ·Organisations that want to automate repetitive work across email, spreadsheets, calendars, and documents
Pre-requisites
- ·No coding prerequisite for business and productivity tracks
- ·Basic familiarity with workplace tools such as email, documents, spreadsheets, slides, and meetings
- ·Willingness to bring real recurring tasks into labs for workflow redesign
- ·Enterprise cohorts should align data-handling expectations before learners use company or client information
Trusted by L&D leaders across the world.
"The programme moved our team from random prompting to a repeatable method. The prompt library and Custom GPTs became assets we could actually reuse."
"The most useful part was workflow automation. Learners took their weekly reports, meeting recaps, and research tasks and reduced hours of repetitive effort."
"Responsible use was handled practically. The team finally understood what can be pasted, what must be masked, and how to verify output before sending it."
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.