AI that actually starts with teams thatunderstand it.
Practical AI training for engineering teams and business stakeholders. We cover real systems — document processing, internal tooling, process automation — not toy demos. Both technical and non-technical tracks available.
Average reduction in manual processing time reported by teams after training
Faster project scoping when decision-makers understand AI constraints
Of AI projects that fail do so due to engineering and process gaps, not the model
Two tracks. One outcome.
Effective AI adoption requires both sides of the table. We train engineers to build it and stakeholders to buy and direct it — so projects don't stall in translation.
AI for Decision-Makers
Understand how modern AI systems work, where they add genuine value, and how to evaluate vendors, tools, and proposals without needing to write a single line of code.
- Identify high-ROI automation opportunities in your workflows
- Evaluate AI vendor claims and avoid common pitfalls
- Scope and commission AI projects with clear success criteria
- Understand risk, compliance, and data governance basics
Building Production AI Systems
Hands-on engineering curriculum covering the full lifecycle of production AI: data pipelines, model integration, evaluation frameworks, and operational monitoring.
- Design reliable data ingestion and preprocessing pipelines
- Integrate LLM APIs with deterministic fallback logic
- Build evaluation and regression test suites for AI outputs
- Instrument, monitor, and debug AI systems in production
What we actually build.
Training modules are built around real production use cases — not hypothetical demos. Every module includes architectural walkthroughs and live implementation exercises.
Document Processing Pipelines
Train your team to build systems that extract structured data from PDFs, contracts, and forms — replacing brittle regex rules with models that generalize to layout variation.
- OCR integration
- Schema extraction
- Confidence scoring
- Human-in-the-loop review queues
- Reduce manual review time
- Eliminate transcription errors
- Handle document volume spikes
Internal Knowledge Systems
Build RAG-based (Retrieval-Augmented Generation) systems that let your team query internal documentation, runbooks, and knowledge bases with natural language — and get cited, auditable answers.
- Vector database setup
- Chunking strategies
- Retrieval evaluation
- Source attribution
- Cut onboarding time
- Reduce repeat questions to senior staff
- Surface buried institutional knowledge
Business Process Automation
Move beyond simple if/then automation. Build AI-orchestrated workflows that handle exceptions, route decisions to humans at the right moments, and improve over time with feedback loops.
- Agent orchestration patterns
- Tool use and function calling
- Structured output validation
- State machine design
- Automate complex multi-step processes
- Reduce exception handling cost
- Create auditable decision trails
AI-Assisted Data Analysis
Enable analysts and engineers to build systems that transform raw operational data into structured insights — trend detection, anomaly identification, and natural language report generation.
- Data normalization pipelines
- Prompt engineering for analysis
- Output schema enforcement
- Dashboard integration
- Reduce analyst toil on routine reports
- Surface operational anomalies faster
- Make data accessible to non-technical stakeholders
API & System Integration
Design and build reliable integrations between AI models and your existing software stack — with proper error handling, rate limit management, cost controls, and graceful degradation.
- Retry and fallback logic
- Cost and token tracking
- Async job queues
- Webhook and event architectures
- Predictable AI costs
- No single points of failure
- Smooth rollout to production
Fine-Tuning & Custom Models
Learn when fine-tuning a model actually pays off versus prompt engineering, and how to run the full cycle: dataset curation, training, evaluation, and A/B testing against base models.
- Dataset preparation and labeling
- Fine-tuning via API
- Evaluation benchmarking
- Version management
- Improve accuracy on domain-specific tasks
- Reduce per-call token costs
- Own your model's behavior
What you’ll learn.
The technical track is structured as a four-phase program that takes engineers from AI fundamentals to running a production system — with code and critique at every step.
Foundations
- How LLMs actually work — without the hype
- Prompt engineering fundamentals and failure modes
- Evaluating AI outputs: metrics, benchmarks, and intuition
- Cost modeling: when AI is worth the investment
System Design
- Stateless vs stateful AI architectures
- RAG: retrieval systems, chunking, and embedding models
- Agent patterns: orchestrators, tools, and guardrails
- Human-in-the-loop design for high-stakes decisions
Production Engineering
- Building evaluation test suites before you ship
- Monitoring: latency, cost, accuracy, and drift
- Structured output enforcement and schema validation
- Error budgets and graceful degradation strategies
Applied Projects
- Capstone: build a production-ready AI feature end-to-end
- Code review and architecture critique sessions
- Incident simulation: debugging a broken AI system
- Planning your team's 90-day AI roadmap
Our approach.
Systems first
AI is infrastructure. We train to that standard — covering reliability, observability, and failure modes from day one. Models are components in a larger system, not magic boxes.
No cargo-culting
We explain the mechanics behind every technique. You'll know why a RAG chunk size matters, not just what to set it to — so you can adapt when your data doesn't fit the textbook.
Production bias
Every exercise targets a production scenario. We skip the Jupyter notebook tutorials in favor of API contracts, error handling, and monitoring from the first session.
Both sides of the table
Technical training without stakeholder buy-in stalls. Business training without technical grounding creates bad RFPs. We run both tracks so your team speaks the same language.
Ready to train your team?
Training is customized to your stack, your domain, and your team’s starting point. We scope a program around your goals — not a generic syllabus.