Build Production AI That Scales Like Your Other Services
Your engineers already build reliable backend services. KindLogic teaches them to build AI systems with the same reliability—monitoring, failovers, cost control. We teach what Alonso built at Twitter and Huawei: patterns for AI at scale, not toy examples.
The Production AI Challenge
❌ Toy Examples Don't Scale
Online tutorials teach demos that work locally, then break in production. Your team spends weeks debugging latency, cost overruns, and reliability issues that tutorials never mention. Production AI is different.
❌ Infrastructure Costs Explode
First month: $500 in API costs. Second month: $5,000. Third month: panic. Without proper architecture, AI costs spiral fast. You need patterns for caching, batching, and smart model selection.
❌ Integration Nightmares
AI systems don't fit your existing architecture. Authentication? Authorization? Monitoring? Logging? You're hacking solutions together because tutorials skip the hard parts: making AI work with real systems.
✅ The KindLogic Approach
Learn production patterns in 1-2 days. Alonso teaches the same reliability patterns he used at Twitter (millions of users) and Huawei (autonomous vehicles). Your team learns AI architecture that scales, integrates cleanly, and stays within budget. Real production engineering.
Production AI Patterns (Battle-Tested)
Every pattern below was used at scale (Twitter, Huawei) and tested in KindLogic AI Labs. Not theory—proven engineering.
⚙️ AI as Microservices
Treat AI like any other service: REST/GraphQL APIs, versioning, health checks, observability. Integrate with your existing architecture without rewrites. Works with Docker, Kubernetes, serverless—whatever you're running.
Pattern: Isolated AI services that plug into existing systems
💰 Cost Optimization Architecture
Prevent runaway costs: semantic caching (70-90% hit rates), smart model selection (GPT-4 only when needed), batch processing for non-urgent tasks, and usage monitoring. Cut costs by 10x without sacrificing quality.
Pattern: Multi-tier caching + model selection strategy
📊 AI Observability & Monitoring
Track metrics that matter: latency (P50/P95/P99), accuracy drift, cost per request, failure rates. Set up alerts, dashboards, and runbooks. Know when your AI is degrading before users notice.
Pattern: Same observability tools you already use (Datadog, Grafana, etc.)
🛡️ Reliability & Fallback Patterns
AI services fail differently than traditional apps. Learn graceful degradation: fallback to simpler models, cached responses when APIs are down, circuit breakers, retry logic with exponential backoff. Keep serving users even when AI fails.
Pattern: Multi-tier fallbacks + circuit breakers
🔍 RAG Systems (Context-Aware AI)
Build AI that understands your data: documentation, code, internal knowledge. Retrieval-Augmented Generation that works in production. See it running in our lab: KindLogic AI Labs.
Pattern: Vector DB + semantic search + LLM generation
🎯 Custom Implementation
Complex requirements? KindLogic offers custom projects ($20-100K+) where we architect and build production AI systems tailored to your stack—with complete knowledge transfer. Your team owns it.
Approach: We architect it with you, you maintain it forever
Why Tech Teams Choose KindLogic
Founded by Alonso Gutierrez—ML infrastructure at Huawei, security systems at Twitter
⚡ Built at Scale, Taught Practically
Alonso built ML infrastructure for Huawei's autonomous vehicle teams and security systems protecting millions at Twitter. Not academic research—real production systems under real constraints. That's what you learn: patterns that work when reliability and scale matter.
🏗️ Lab-Tested Before Teaching
Every workshop teaches systems we built in KindLogic AI Labs. We run RAG search daily. We use AI assistants for our work. We automate our lead gen with n8n + AI. No hypotheticals—proven patterns running in production right now.
🔓 Engineering, Not Vendor Lock-In
We teach your team to build, deploy, and maintain AI systems using standard tools and frameworks. No proprietary platforms. No ongoing fees. You get knowledge transfer, not dependency. This is sustainable engineering capability.
🚀 Integrates with Your Stack
Your team's already using Python/Node.js/Go, Docker/Kubernetes, PostgreSQL/MongoDB, AWS/GCP/Azure? Perfect. KindLogic teaches AI patterns that integrate with your existing tools. No rewrites. No migrations. Just add AI capability to what you're already running.
Frequently Asked Questions
Can we integrate AI without rewriting our architecture?
Absolutely. KindLogic teaches patterns for adding AI to existing systems—treating AI as services/components that integrate via APIs. No rewrites needed. Alonso built ML systems at Twitter that integrated with existing infrastructure serving millions. We teach you the same incremental approach: add AI where it adds value, leave what works alone.
How do we prevent AI infrastructure costs from spiraling?
Smart architecture prevents cost explosions. In KindLogic workshops, you learn: efficient model selection (when to use GPT-4 vs. smaller models), caching strategies that cut API calls by 70-90%, batch processing patterns, and cost monitoring. Real patterns Alonso used at scale, now yours to implement.
What about production reliability and monitoring?
Production AI requires different monitoring than traditional apps. We teach AI-specific observability: latency tracking, accuracy drift detection, fallback patterns when models fail, and version control for models. Same reliability patterns used at Twitter and Huawei, adapted for teams of any size.
Do our engineers need AI/ML backgrounds?
No. If your team builds backend services or APIs, they can build AI systems. KindLogic teaches practical implementation with modern frameworks (LangChain, OpenAI, Anthropic, open-source models) that work like any other API. Focus on engineering, not theory.
Can we build this in-house or should we outsource?
You should build it in-house—with training. Outsourcing creates vendor dependency and knowledge gaps. KindLogic teaches your team to build it themselves, so you own the capability forever. One $5-10K workshop gives you permanent AI capability. That beats $50-200K consulting projects that leave you dependent.
Ready to Build Production AI Your Team Can Own?
Book a free 30-minute discovery call. We'll discuss your architecture, requirements, and whether KindLogic workshops are the right fit for your team. Technical conversation with Alonso—no sales pitch.
Book Your Discovery CallOr call: +1 (647) 676-1592