Resources
Playbooks for putting AI to work
Practical guides, templates, and decision frameworks for operators and engineering leaders building AI agents, automation, and production AI systems.
Most AI content answers the wrong question. It explains what a technology is when what an operator actually needs to know is whether it will work for their invoice queue, their call volume, their support backlog — and what it takes to get from a promising pilot to a system the business depends on.
This library exists to close that gap. We're documenting the playbooks we use in real engagements: how to pick the first workflow to automate, how to evaluate an AI agent before trusting it, how to keep a RAG assistant accurate as documents change, and how to estimate ROI from your own numbers instead of a vendor's brochure. Everything is written for decision-makers at US businesses — from ten-person firms to enterprise teams — in plain language, with the engineering detail where it earns its place.
The first guides are in production now. Until they ship, the blog and our service pages cover much of the same ground — each service page includes the questions we're asked most, answered honestly.
What we're covering
Six topic areas, one standard: useful
The resource library maps to the questions clients actually ask us — before, during, and after an AI engagement.
AI agents in production
How to scope a first agent, design human-in-the-loop approval gates, build evaluation suites, and measure cost per completed task — the engineering behind agents you can trust unattended.
Business process automation
Ranking automation candidates by ROI, running new automations in shadow mode, handling exceptions with review queues, and proving accuracy before cutover.
RAG & knowledge systems
Chunking strategies for real document types, permission-aware retrieval, golden-set answer evaluation, and keeping assistants current as content changes.
Voice & conversational AI
Designing phone agents callers don't hang up on: latency budgets, escalation rules, calendar and CRM integration, and HIPAA-aligned deployments for clinics.
AI ROI & buying decisions
Build-vs-buy frameworks, estimating ROI from your own operational numbers, unit-economics of LLM features, and the questions to ask any AI vendor.
AI governance & security
Least-privilege tool access, PII handling before model calls, audit logging, and usage policies that let teams adopt AI without creating new risk.
Editorial standards
How these resources get written
The same standard we hold our software to: specific, tested, and honest about trade-offs.
Written by builders
Every guide comes from engineers who ship these systems for clients — not from a content farm summarizing other people's blog posts.
Tested, not theorized
Recommendations reflect what has actually worked in production engagements: the failure modes, the workarounds, the numbers worth measuring.
No fluff, no hype
We publish when we have something specific to say. You won't find recycled listicles or breathless model announcements here.
Get new guides as they publish
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Have a question a guide hasn't answered yet?
Ask us directly. A short call with the team is faster than any playbook — and it's free.