Featured · AI guardrails
When AI fails, it's rarely the model's fault.
Chevrolet, Air Canada, and DPD all had AI go publicly wrong, for three different reasons, none of them a bad model. A three-question framework, reversibility, stakes, verifiability, for deciding where AI should run unsupervised in a business, and where it shouldn't.
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The Builds
What the guides look like once they're built.
Each one started as a documented problem before any tooling got touched. The method is the same regardless of which one it's pointed at next.
61%
auto-resolved within 30 days
ThreadWave: Support Triage
200+ tickets a day, all needing manual sorting before anyone can act on them.
24/7
on-brand coverage
Storefront Support Chatbot
Sizing, returns, and shipping questions go unanswered outside business hours.
∞
scale without headcount
Instagram DM Concierge
DMs and story replies pile up faster than anyone can reply to them.
150K+
words analyzed
How to Build an AI That Writes Like You
Derived a voice guide from a real person's posts and transcripts, then wrote new content for a completely different brand in that exact voice.
AI Modernization
Modernizing the mundane.
FAQ
Direct answers.
How do you decide what to automate first?
By mapping the manual process before touching any tooling: what triggers the work, who does it, and where the judgment calls actually happen. The build gets scoped around that, not a templated workflow. Support tickets are where this showed up first for ThreadWave, but the same method applies to ops reporting, fulfilment, or influencer tracking.
How long does a build like this typically take?
ThreadWave went from mapping the ticket taxonomy to a live system in 14 days. Most of that time is discovery, understanding the process as it actually runs, not the build itself. Once the process is mapped, wiring it into the existing tools is the fast part.
How does a system like this avoid sending a wrong reply?
ThreadWave only auto-replies to low-risk, well-defined query types, like order status or return policy questions, where confidence is high. Anything ambiguous or high-stakes gets escalated to a person with a draft already attached. The rule isn't automate everything, it's automate what's safe to automate and escalate the rest.
Why not just use basic rule-based automation like Zapier?
Rule-based automation breaks the moment something is phrased unexpectedly: a typo, an odd word order, a two-part request. The systems here read intent instead of matching keywords, so they hold up against that kind of variation, whether it's a support ticket, a fulfilment exception, or an inventory alert.
Does this only apply to customer support?
No, support is just where the first build landed, because that pain surfaced first in founder conversations. The method, map the manual process, then build the system around what actually happens, applies to any repetitive operational work: daily ops reporting, fulfilment checks, influencer or affiliate tracking, inventory alerts.