Your AI Works in Demos.
Why Does It Break in Production?
Most teams can build AI prototypes. Few can make them reliable, auditable, and scalable. That gap is costing you.
class AISystem:
# Designed for production, not just demos
workflow = "structured"
reliability = "auditable"
scale = "production_ready"
# 74% of AI demos fail in prod.
# This fixes that.
✓ System audit complete — 3 critical gaps found
Where AI systems break
These aren't edge cases. They're the standard failure modes when AI goes from demo to production.
Hallucinations in production
Outputs that passed eval harness but fail on real user data. No detection, no fallback, no audit trail.
No governance framework
AI usage spread across the codebase with no standard for how decisions are made, logged, or reviewed. Compliance risk.
Scaling failures
Architecture designed for a demo load. Latency spikes, rate limit errors, and cost overruns when traffic doubles.
No audit capability
When something goes wrong, you can't trace why. No structured logging of AI inputs, outputs, or decision paths.
Structured training, not general AI education
We don't teach you what AI is. We work with your team on the specific gaps between how your system works now and how it needs to work in production.
Assess
Review your current AI architecture, identify failure modes and reliability gaps.
Train
Focused sessions on production patterns: evaluation, governance, scaling, and auditability.
Document
Reusable playbooks your team owns — not a report that sits in a folder.
Audit trail implemented
All LLM calls logged with input/output/latency
Output validation layer
Schema validation + retry logic on parse failure
Governance documented
Decision log + review process defined
3 critical gaps found · 2 resolved in session · playbook delivered
What you get
Three concrete deliverables. No slide decks that gather dust.
System Audit
A structured review of your current AI architecture. We identify reliability gaps, failure modes, and scaling risks — before they become production incidents.
Deliverable: written findings + prioritized action list
Team Training
Focused sessions on production AI design: evaluation frameworks, output validation, governance patterns, and scaling architecture. Tailored to your stack.
Deliverable: session recordings + reference materials
Reusable Playbooks
Practical documentation your team can use immediately: architecture patterns, evaluation workflows, and governance templates — written for your context.
Deliverable: versioned docs your team owns
Who this is for
Engineering teams shipping AI-powered features who are experiencing reliability issues in production
Teams that have prototypes working in demos but struggling to make them production-ready
Organizations that need AI systems to meet governance, compliance, or audit requirements
CTOs and engineering leads who want their team upskilled on production AI architecture, not just LLM basics
Start with an audit
A system audit takes one session. We identify the critical gaps in your AI architecture and give you a clear action list — before anything fails in production.
Book a System Audit