Service
Enterprise AI Capability Built Through
Practice, Not Slides.
Generic AI workshops produce enthusiasm, not capability. Our academy programs are structured around role-based cohorts, real use-case labs, and measurable skill outcomes — so teams can ship AI products, not just talk about them.
Browse Academy Courses by Track
Why Most AI Training Produces Zero Capability
Organizations spend on AI training and get conference-style inspiration instead of production-ready skills. These patterns repeat in every failed upskilling initiative.
One-Size-Fits-All Content
Engineers, product managers, and business leads all sit through the same abstract curriculum — leaving no one with actionable depth.
Theory Without Practice
Slides and demos without hands-on labs — participants leave with concepts but cannot implement a RAG pipeline or evaluate an LLM output.
No Measurement
Without pre/post assessments and capability scoring, leadership has no way to know whether skill gaps actually closed.
One Academy, Five Ways to Engage
Start small and free, or go straight to a custom programme — each step builds on the last, so you can test our method before committing to a cohort.
Executive & Technical Masterclass
A live, no-cost session that walks your team from AI idea to working prototype — the easiest way to see how we teach before committing to a cohort.
Applied Enterprise Workshop
A hands-on workshop for enterprise teams to size a real use case, work through hands-on labs, and leave with a practical AI product brief.
AI Engineering Cohort
Our flagship program — LLM integration, RAG pipelines, evaluation, safety/guardrails, and production deployment, taught through role-based labs and a capstone.
Custom Academy Programme
A programme designed around your organisation's roles, systems, and approved use cases — with pre/post assessments and leadership-level reporting.
On-Demand Course Library
A self-serve library of foundational AI courses, planned as a lightweight entry point once our cohort formats are proven and refined.
Four Role-Based Learning Tracks
Each track is a category of individual courses, with real use-case labs, hands-on projects, and measurable skill assessments — not generic overviews.
AI Engineering Track
For software engineers building production AI systems — LLM integration, RAG architectures, prompt engineering, evaluation, and MLOps.
LLM API Integration Patterns
Connect OpenAI, Anthropic, and open-source models into real applications with streaming, function calling, and cost controls.
RAG Pipeline Design & Tuning
Build retrieval-augmented generation pipelines — chunking strategies, embeddings, vector stores, and relevance tuning.
Evaluation Frameworks & Guardrails
Design automated evals, red-teaming, and safety guardrails so AI outputs are trustworthy in production.
Production Deployment & Monitoring
Ship AI features behind feature flags with observability, latency budgets, and rollback plans.
Product Management Track
For product managers defining AI-powered features — opportunity sizing, feasibility assessment, success metrics, and user experience design for AI.
AI Opportunity Identification
Spot high-value AI use cases inside your product roadmap using a structured opportunity-sizing framework.
Feasibility & Risk Assessment
Separate feasible AI bets from science projects — data readiness, model risk, and build-vs-buy calls.
AI-Specific UX Design Patterns
Design trustworthy AI experiences — loading states, confidence signals, human-in-the-loop, and failure handling.
Success Metrics & A/B Testing
Define leading and lagging indicators for AI features, and run experiments that isolate real impact.
Business Leadership Track
For CXOs and directors making AI investment decisions — strategy frameworks, vendor evaluation, ROI models, and governance structures.
AI Strategy & Roadmap Planning
Translate executive AI ambition into a sequenced, fundable roadmap your teams can actually execute.
Build vs. Buy Decision Frameworks
Evaluate vendor platforms against in-house builds using cost, control, and time-to-value tradeoffs.
ROI Modeling for AI Investments
Build defensible ROI models for AI initiatives that hold up in budget and board reviews.
Governance & Risk Management
Stand up AI governance — approval workflows, data policies, and risk registers that scale with adoption.
Data & Analytics Track
For data teams preparing infrastructure for AI — data pipelines, quality frameworks, vector databases, embedding strategies, and retrieval optimization.
Data Pipeline Design for LLMs
Architect ingestion and preprocessing pipelines that keep LLM-facing data fresh, clean, and compliant.
Vector Database Setup & Tuning
Stand up and tune vector databases (pgvector, Pinecone, Weaviate) for low-latency retrieval at scale.
Data Quality & Governance
Implement data quality checks, lineage, and governance so AI systems aren't built on bad data.
Embedding & Retrieval Strategies
Choose and tune embedding models, hybrid search, and reranking for retrieval accuracy.
How Each Cohort Is Structured
Pre-Assessment
Baseline skill mapping through structured assessments before the program begins — so every cohort starts from an honest capability snapshot.
Hands-On Labs
Every module includes practical labs with real datasets, real APIs, and real evaluation scenarios — not toy examples or pre-baked demos.
Capstone Project
Each cohort completes a capstone using your organisation's actual data and workflows — producing an artifact your team can take to production.
Post-Assessment & Reporting
Skill gap closure reports, individual capability scores, and cohort-level analytics delivered to leadership for measurable training ROI.
Questions? Answers.
What is the AI Academy?
The AI Academy is a structured training program that upskills engineering, product, and business teams in AI/ML, LLM engineering, cloud platforms, and modern software development through hands-on labs and mentoring.
Who is the AI Academy designed for?
The program is designed for enterprise teams including software engineers, data engineers, product managers, and technical leaders who need practical AI skills applicable to their day-to-day work.
Are academy programs customisable?
Yes. We tailor curriculum, duration, and depth based on your team's current skill level, technology stack, and business objectives. Each cohort receives a personalised learning path.
What topics does the academy cover?
Topics include machine learning fundamentals, LLM engineering, RAG pipelines, prompt engineering, cloud architecture, DevOps, data engineering, and AI product development best practices.
Do participants receive certification?
Yes. Participants who complete the program and pass assessments receive a certification from Haivisoft AI Academy, validating their practical AI engineering skills and project experience.
Ready to Build Real AI Capability?
Tell us about your team, their roles, and where capability gaps exist. We will design a cohort program that produces measurable skill outcomes.