Insights & Case Studies

Engineering perspectivesfrom production AI deployments.

Case studies, technical deep-dives, and practical guides. No hype, no buzzwords—just lessons learned from building AI systems that work.

1 Case Study

5 Articles

1 Guide

Featured

Automated

previously manual document workflows

Case Study

AI-Driven Supply Chain & Logistics Platform

An AI-native SaaS platform that replaced manual, document-heavy logistics workflows with automated extraction, routing, and optimization for large distribution operators.

Production AIDocument AutomationSupply ChainSaaS

Daniel Voigt•7 min read

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Article9 min read

Database Scalability: Beyond Vertical Scaling

Vertical scaling buys time, not a future. A practical look at query optimization, rate limiting, caching, read replicas, and when sharding is actually worth it.

ArchitectureDatabasesScalability

Daniel Voigt

Article8 min read

What a Modern TMS Actually Buys You

Transportation Management Systems quietly decide a logistics operation's margins. A look at where the savings really come from — routing, rate shopping, carrier selection, and the architecture that holds at scale.

Supply ChainLogisticsTMS

Daniel Voigt

Article8 min read

Leading Engineering Teams as a Fractional CTO

Running distributed engineering teams without a full-time seat: the communication, time-zone, and process realities that decide whether the work ships.

Engineering LeadershipFractional CTOTeams

Daniel Voigt

Article8 min read

Modern Serverless APIs on AWS

For the right workload, serverless lets a small team ship a scalable API with almost no infrastructure to run. Where it fits, where it doesn't, and the tradeoffs to plan for.

AWSServerlessArchitecture

Daniel Voigt

ArticlePublication

Enterprise AI Patterns

A weekly publication on what it actually takes to ship AI in production for leaders and builders designing systems that hold up under agent-scale volume.

Production AIEngineering LeadershipLLMOps

Daniel Voigt

Guide2 weeks

AI Delivery Diagnostic

A 2-week principal-led assessment of architecture, data readiness, risk, evals, observability, and the path from AI idea to production system.

DiagnosticArchitectureDelivery

TeqEngine

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Daniel writes on what it actually takes to ship AI in production: harnesses, evals, observability, agentic systems, and the operating layer around frontier models.