+ <- Back +
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+ ai didn't break the laws of physics
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+ + Understanding Clients · + +
+ ++ At Table22 we’ve been pouring time into using the latest LLMs to + understand our partners better. On the engineering side, I’ve been + building out our onboarding pipeline, which takes a single business + URL and turns it into a full brand identity and a sample offering in + a couple of minutes. It captures a partner’s voice and tone, their + vertical, their target audience, and their key differentiators in + less time than it takes me to pour a cup of coffee. Architecting + that pipeline has fundamentally shifted how I think about AI, well + beyond our own product. +
++ I’m going to write a whole series on that pipeline, and on how the + patterns behind each piece have worked their way into my everyday + routines. First, though, let’s walk the high-level shape of it. +
+background
++ Table22 is a subscription platform for food, beverage, and specialty + retail businesses. We handle everything from signup to fulfillment + so owners can focus on what they’re actually good at, which is + giving their guests the best possible experience. The app is legacy. + I mean real legacy, compounding decisions since 2021. +
++ Saddled with all that debt but small enough to pivot on a dime, we + decided we could rebuild it. Not just the app, though. We decided to + rebuild the foundational pillar the whole company runs on. In early + spring we pushed the first commit of a new AI-powered, composable, + evolving knowledge base, one that could let any Table22 employee + become an expert on any partner we work with. +
+the goal
++ There’s a Jack Dorsey idea I keep coming back to, the + hub-and-spoke model. One person, one set of hands, plugged into several efforts at + once, the work radiating out from a small center instead of + trickling down a big org chart. We designed the + Entity Knowledge Base Engine to be that center: a + single point of evolving knowledge that any app, AI agent, + throwaway experiment, or person could plug into at any time. +
++ The engine’s first job was to feed that onboarding pipeline, which + had started life as a proof-of-concept stitched together across a + handful of SaaS platforms. We had to codify those pieces and rebuild + the pipeline as something that could expand and adapt as fast as the + ground was moving under us. +
++ And the engine was the linchpin. Get it wrong and the whole pipeline + falls over. Make it too rigid and we’d be right back here in a few + months, rebuilding from scratch. The values it produced had to + generate marketing materials and build a sample offering + and extrapolate winning patterns for prospective partners, + all from the same structured knowledge. That forced a shift in + how we thought about the whole thing. The engine was never + going to be a brand-ingestion step. It was going to be the + foundation. +
+where ai wasn’t the magic
++ This engine is the future of Table22, or at least the bet on it. The + whole pivot, the entire reason we took on the monumental job of + rebuilding the app, hinged on getting it right. Our entire + engineering department is six people, and all of them were already + busy keeping the legacy revenue product alive. The first iteration + of the engine fell to two of us. I didn’t have months. I didn’t even + have weeks. The deadline was one week. My one teammate on it is + genuinely incredible, but two people are still two people. + AI doesn’t break the laws of physics, and it doesn’t stop time. +
++ We wrote the proposal assuming each of us would be driving around + four AI agents across separate git worktrees at any given time. The + math looks incredible on paper. If each agent gives you six to eight + times the throughput on routine implementation, four of them is + twenty-four to thirty-two times. Rewrite the whole thing by Friday. +
++ We wrote that math down, and then we wrote down why it was a lie +
++ Our honest internal estimate put shipping the full spec in a week at + single-digit-percent odds. I literally asked Claude to + crunch those numbers again. Sadly, the answer didn’t change. The locked architecture was + eight to twelve weeks of work for two engineers at an honest pace. + Agents compress implementation, and that part is real. What they + don’t compress is everything that actually gates the work: the + architectural decisions, the humans reviewing the code, the SDK + integrations, the prompt authoring. +
++ So we did the only thing that actually works under that constraint. + We cut scope. We shipped a deliberately minimal version of the loop, + and we wrote down everything we left out: twenty-three items of + known debt, each one named. All the first version had to do was + prove the engine was worth investing in. +
++ That’s the actual lesson, and it’s the opposite of magic-thinking. + AI agents let a tiny team move at a velocity that used to take a + whole department. But the move wasn’t a clever model. The move was + discipline: knowing the bottleneck was integration and judgment + rather than typing, and then shrinking the scope to fit that + bottleneck instead of pretending it wasn’t there. +
+where it actually paid off
++ Before any of this was a product, it was a pile of throwaway + prototypes, each one proving a single primitive. A service that took + a URL and pulled a brand’s voice, logo, and visual tokens out of it. + A separate one that generated product pages. A scrappy single-page + app that was really just a research spike with a frontend bolted on. + Each answered one narrow question: can the AI actually do this + specific extraction reliably enough to bet on it. The rebuild didn’t + invent any of those capabilities. It centralized the ones that had + already passed the test. +
++ That’s the part I’d push any leader reading this to actually sit + with. The reason a six-person engineering department could put AI at + the structural core of a rebuild isn’t that AI suddenly got easy. + It’s that we de-risked it in disposable prototypes first, where + being wrong was cheap, and only committed to permanent architecture + once we knew which primitives held up. The rebuild was the payoff of + a dozen small, ugly, fast experiments. +
++ And “at the core” is literal, not a slogan. The engine is a real, + separable layer with its own database. Text goes in, structured + brand knowledge comes out, versioned, with its history preserved. + Other AI surfaces in the app read from it and build on top of it + without ever reaching inside. That’s not a chatbot stapled to a CRUD + app. That’s AI as a foundation. How it works, and why we built it as + structured knowledge instead of reaching for a vector store, is the + next post +
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