Writing from the studio.
Practical notes on AI automation — what works in production, what doesn’t, and how we think about the decisions in between.
What Actually Happens in Week One With an AI Automation Partner
A concrete walkthrough of a discovery week: what gets mapped, what gets ruled out, and the deliverable you should walk away with.
Rate Limits, Retries, Timeouts: What Makes AI Actually Reliable
A better model doesn't fix a flaky AI integration. Backoff, idempotency, and graceful degradation do — here's how we build them.
Your AI Feature Launch Was The Cheap Part
Model deprecations, prompt drift, and pricing shifts mean AI features keep costing money after launch — most quotes never account for it.
Automate the Boring Step First, Not the Impressive One
Why the highest-friction, most repetitive task should be the first thing you automate, and how that sequencing decides whether the project gets funded again.
Zapier vs Custom Code: A Real Decision Framework
Volume, error handling, and reasoning needs decide whether Zapier or n8n is enough — or whether you need custom code. Here's the framework.
Prompt Versioning: The Boring Practice That Saves AI Projects
Untracked prompts cause silent quality regressions in production AI systems — here's the lightweight version-control discipline that prevents it.
Which AI Automations Pay Back in 90 Days (And Which Don't)
A pattern from real client work: narrow, high-volume automations pay for themselves fast — ambitious "AI agent" builds usually don't.
What Breaks First When You Put an LLM in Production
Rate limits, silent format drift, and untested timeouts are what actually take down LLM pipelines — not the failure modes teams demo for.
Stop AI Project Blowouts: Scope Right from Day One
AI projects often derail after the demo. Learn how to scope with fixed evaluations, clear metrics, and bounded goals to ship production AI on time.
AI Agent or Automation? Choose Wisely for Production.
Most businesses asking for 'AI agents' actually need deterministic automation, which is cheaper, more reliable, and easier to debug for production systems.
Why Most AI Agent Projects Never Make It Past the Demo
The gap between an AI agent demo and a shipped product isn't the model — it's evals, failure handling, and the edge cases real users find in week one.
How Do You Handle Failures in an AI Pipeline Gracefully?
A production AI pipeline needs validation, retries, fallbacks, and escalation for every failure mode — not just a try/catch around the API call.
How to Set Up Your Own VPS Hosting (and Actually Secure It)
A plain-English, step-by-step walkthrough for setting up your first VPS: picking a provider, locking down SSH, a firewall, automatic updates, and getting a domain live with SSL.
How Do You Test an AI System Before It Goes to Production?
A practical eval stack for AI systems: unit evals, integration evals, and regression evals, and what "good enough" actually means before you ship.
AI Tools We Actually Use in Production (2026)
Forget the hype. We share the essential AI tools professional engineers at Dainty use daily for evals, tracing, prompting, and deployment in 2026.
Why “SEO” Now Means Being Cited by ChatGPT Too
Generative engine optimization (GEO) is the practice of structuring a site so AI answer engines like ChatGPT, Claude, and Perplexity can find, understand, and cite it accurately.
What an SEO & GEO Audit Actually Finds
The recurring, fixable issues we find on almost every unaudited site: broken schema, missing llms.txt, robots.txt accidentally blocking AI crawlers, and thin metadata.
What a Production AI Project Actually Costs
A production AI project typically costs $15k to over $200k, driven by complexity, evaluation needs, and robustness requirements.
Can AI Automate Your Process? Ask These 4 Questions.
Before investing in AI automation, ask four critical questions: Is the input consistent? Is the logic describable? Is the output verifiable? Is the volume worth it?
How long does it take to build a production AI agent?
A reliable production AI agent takes 2–3 months to build. The demo takes a week, but the gap is evaluation pipelines, fallback handling, and edge cases.
The unglamorous ops work behind production AI
What happens after the AI demo: managing P99 latency, sanitizing PII from prompt logs, and handling malformed JSON failures in production.
Stop Hitting LLM Rate Limits: What We Learned Shipping
Learn production patterns for rate limiting AI endpoints, including per-user, cost-based, and queue-based throttling, to avoid provider limits and unexpected bills.
Don't Ship AI Features to 100% on Day One
Gradual rollouts, A/B testing, and shadow mode are critical for AI features. Learn how to instrument and deploy safely.
How We Evaluate LLMs: Beyond Benchmarks
Stop guessing which LLM works best. Our framework helps you pick the right model for your task, measuring cost, quality, and long-term fit.
Self-hosting LiteLLM: 6 months in production
After half a year, we share what actually works when self-hosting LiteLLM as a unified LLM gateway, and where it adds complexity.
Rules Still Win: When Not to Use an LLM
LLMs aren't a silver bullet. We break down Dainty's decision tree for when deterministic rules outperform AI models.
Building AI Feedback Loops That Don't Require Manual Labeling
Stop waiting for user ratings. Learn how to build a reliable AI evaluation framework using automated checks, sampling, and implicit signals.
Token Cost Optimization: Where the Savings Actually Are
The highest ROI strategies for reducing LLM token costs in production: prompt caching, model routing, context trimming, and output constraints.
How to Add AI to an Existing SaaS Without Rewriting It
Most SaaS products don’t need a rebuild to get AI features. They need one well-chosen workflow, a clean API endpoint, and a prompt that doesn’t hallucinate on your data.
What an AI Automation Sprint Actually Looks Like
A fixed-scope, fixed-price, four-week engagement. Here’s what happens each week, what you get at the end, and what we won’t scope in.
LLM Routing: Why We Run Claude, Gemini, and OpenAI Behind One Gateway
Hard-coding a single model provider into your app is a liability. Here’s how we route across models — and the rules we use to decide which one runs what task.
The AI Features That Actually Show Up in Your P&L
Most AI features don’t move the needle. A few do — reliably and measurably. Here’s how to tell the difference before you build.
Webhook vs Polling for AI Integrations: When Each Makes Sense
Both patterns work. The right choice depends on latency requirements, whether the data source emits events, and how much you want to think about retry logic at 3am.