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Our thinking, on the record.

Short pieces on getting AI systems into production and keeping them there. Each one is laid out below as the argument it makes, so you can follow the reasoning before you open it.

  1. 2 min readArchitecture

    Why most AI demos never reach production

    A working prototype and a system your team can rely on are different engineering problems. Here's where the gap actually comes from.

    The argument, in its own words

    1. A demo has to work once, in a room, on data you chose. A production system has to work every day, on data you didn’t choose, for people who will notice the day it doesn’t.

      • Failure has to be cheap to detect and cheap to recover from
      • The inputs are the ones your business actually produces
      • Someone has to own it after launch

    2. What we actually build differently

      Retrieval failure
      fall back to a narrower search, then to a human queue
      Model failure
      structured retry with a stricter prompt, then escalate
      Silent drift
      sampled human review + a weekly accuracy report

      The point isn’t to avoid AI’s unpredictability — it’s to build around it.

    3. Read it in full: Why most AI demos never reach production

  2. 2 min readMethod

    Before you automate anything, trace the workflow

    Most AI projects fail before a model is ever chosen — they start from the wrong step. Here's how we find the one that's actually worth automating.

    The argument, in its own words

    1. The most common question we get isn’t “what can AI do for us.” It’s “where should we point it.”

    2. The org chart lies

      1. Where does the work enter?
      2. Where does it wait?
      3. Where does a person make a judgment call a machine could make consistently?

    3. Why this order matters

      Skip the trace and you end up automating the visible 20% of a process while the actual bottleneck — usually a wait state, not a task — goes untouched.

    4. Read it in full: Before you automate anything, trace the workflow

Find where AI actually pays off in your operations.

Begin with an opportunity assessment. We’ll map your workflows and show you the highest-value systems worth building — before you commit to anything.

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