Articles, guides, and research from Sauce Technologies — what we learn shipping AI, integration, data, automation, and security work for mid-market businesses. Field notes, post-mortems, and the occasional opinion.
Resources is Sauce Technologies’ field journal on the operational stack of mid-market operating companies — the systems, integrations, and judgment calls that turn five disconnected tools into one running business. Read it for the numbers, the failure modes, and the honest retrospective on what we’d do differently.
Forty-two percent of what your operations manager knows isn’t written down anywhere. Three to twenty hours a week of your team’s time gets spent re-finding what someone already figured out. When that person leaves, recovery takes five to twelve months. Here’s what those numbers actually cost, where they come from, and the architecture that stops the bleeding.
Read the articleA practitioner’s guide on the invisible, compounding cost of running a business where institutional memory lives in people’s heads instead of systems. Sarah’s math, the four-stat picture, the five pillars, both doors. v6 · May 2026.
Read the guideThe buyer’s guide we wish we’d had at our first AI engagement. What to scope, what to skip, what costs more than the vendor tells you, and what production actually means once the demo is over.
Download PDFVendor pricing tells you nothing. The real cost lives in three places no quote captures — and the answer is rarely the model bill. Here is the math we walk every prospect through.
Coming soonA $36K/year iPaaS platform aimed at enterprises does not fit a $20M operator. Here is what does — and why we keep recommending a $0 stack for the first six months.
Coming soonMost mid-market teams over-buy their warehouse. Here is the four-question decision tree we use before quoting a data architecture engagement.
Coming soonEighty percent of automation bugs do not surface as errors — they surface as wrong numbers, two months later, in a finance review. Here is the alerting pattern we ship by default.
Coming soonWhat works, what does not, what we tell the founder before kickoff, and the three configuration choices that decide whether adoption sticks past month two.
Coming soonThe gap between LLM demos and operating value is not the model — it is the missing context layer. Where AI Adoption work actually pays back, and where it does not.
Coming soonThe honest retrospective. The clients we turned away and should have taken. The clients we took and should have turned away. The pricing change we made in month nine.
Coming soonWhat we shipped, what broke, what we learned. We don’t write a lot — when we do, it’s because there’s something worth saying.
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