JP Galido · Business AI Architect · San Diego

Most enterprise AI fails between the demo and the deployment. I work in that gap.

Twenty years inside SAP programs and enterprise transformation. More than twelve miniapps deployed across CPG, home services, SMB, and consultant teams.

20+ yrsSAP & enterprise transformation
12+Miniapps deployed
4Sectors served
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Hiring? Roles & fit ↗ See the work ↗ Résumé ↗ LinkedIn ↗

JP Galido, smiling warmly in a gray blazer against a dark background.

San Diego · Replies personally

The work · see it first

Proof, before the pitch.

Real screens from my own products. Tap one — or skip the reading and see the full showcase.

ClarisTXM revenue console with a $187M outlook, today's queue, and an AI briefing
ClarisTXM · RevenueThe revenue console
 ClarisTXM for Services home, the Total Experience Manager
ClarisTXM for ServicesTotal Experience Manager
 ClarisTXM boardroom instruments with a forecast corridor chart
ClarisTXM · RevenueBoardroom instruments
See the full showcase →

Who I am · What I believe

I build enterprise AI that survives contact with compliance.

Compliance, audit, and change‑weary organizations don’t need smarter demos. They need production AI with deterministic cores, human‑in‑the‑loop approval gates, per‑agent permission limits — every agent gets an explicit tool allowlist and credential scope, nothing inherits ambient authority — and audit trails built to survive regulated‑industry review.

AI exactly where it’s justified. Deterministic everywhere else. Honest all the way down.

The record behind that: more than two decades inside SAP programs, compliance work, and steering committees — today as an Enterprise Solution Architect and SAP CX Capability Director. On my own time I’ve built and deployed more than twelve miniapps across CPG, home services, SMB, and consultant teams, and I run RevTXM, a multi‑tenant revenue‑management SaaS with subscription billing. Plenty of other prototypes earned a no — the cost ladder applies to my own roadmap too, and a prototype that proves something shouldn’t be built is a cheap success, not an expensive failure. I’m the founder of TXM Institute, where I publish the method free.

THE COST LADDER cheapest reliable mechanism PROVENANCE four labels, never a fifth DETERMINISTIC CORE AI explains, never computes the score APPROVAL GATES humans own consequences HONEST UI a button that lies is a bug FACT DISCIPLINE every claim carries a tag
Six principles, one constellation: the cost ladder · provenance labels · deterministic core · approval gates · honest UI · fact discipline. Every product I ship is navigable by these stars.

Deployable

It runs in production, not in a slide deck. If it can’t survive a real deploy, it isn’t finished.

Governed

Approval gates, permission limits, audit trails. Autonomy is granted in inches and logged in full.

Honest

No claim ships unless it’s true. That rule governs my products, my resume, and this page.

Design & brand

Why the night sky.

The universe here isn’t decoration — it’s the argument, rendered. Enterprise AI is a vast dark field where most things fail. You don’t cross it by guessing; you cross it by fixed stars. So the brand is built from the same idea as the work: a few things that don’t move, and careful motion around them.

A star is a principle.Fixed, and you steer by it — the cost ladder, provenance labels, deterministic core, approval gates, honest UI, fact discipline. The six in the constellation above. They don’t move; that’s the point of a deterministic core.
An orbit is a governed path.Bounded, predictable motion — autonomy with a boundary. The AI moves, but only along a track a human laid down and can stop.
The comet is the rare AI moment.One, not a storm. AI used sparingly and dramatically, exactly where it’s justified — economical, precise, policy-safe. A sky full of noise is the thing I’m against.
The field is the enterprise.Vast and dark, until you know the stars. The job is to make it navigable — not to add more lights.

The system, not the vibe

It’s a real design system: tokenized colors, system fonts, a monospace voice for labels, and motion that always respects prefers-reduced-motion. Every color is checked for WCAG AA contrast — the ratios live in the stylesheet, because honesty extends to the pixels too.

This galaxy language recurs wherever I design — a visual second-brain rendered as a galaxy, product surfaces built on the same dark, navigable field. It’s my signature because it’s my thesis: vast and dark on the outside, fixed and navigable once you know the stars. Drama from restraint, never from neon — the same discipline I hold in the products.

Why miniapps

Don’t mistake the motor for the machine.

The enterprise AI debate gets framed as agents versus apps. That’s the wrong debate. The real question is product architecture — and the history of every powerful technology already answered it.

Foundation models

the electricity

Agents

the motor

Miniapps

the machine

The electric motor was the breakthrough that turned power into action. But no one handed a worker a bare motor and wished them luck. They built the motor into a machine — an elevator, a conveyor, a drill — that did one job well, safely, every time.

Agents are the motor: real reasoning, real capability, but on their own, exposed. A miniapp is the machine built around it — the interface, the workflow, the approvals, the exception handling, the audit trail, the measurable output. The miniapp sets the boundary; the AI handles the variation inside it. That isn’t anti-agent. The agent is the engine. It just isn’t the finished product.

Built this way, the AI inside a miniapp stays three things at once: economical — it uses the cheapest mechanism that does the job, and reaches for a model only when one is justified; precise — it adapts only inside a boundary you defined, not across your whole business; and policy-safe — every consequential step carries a human approval and an audit trail.

Agents aren’t the product. They’re a component inside it — and the miniapp is the product the enterprise actually runs.

Public diary

What I’m learning, dated and unvarnished.

A working log, newest first. I write down the lessons while the bruises are fresh, because pain you don’t write down is pain you’ve scheduled for later.

The auditor, audited

I ran a five-persona adversarial panel against my own resume today — AI reviewers, the same hostile-review harness I run against my products before they ship. They found weak verbs and vague scope, but the finding that stung was structural: I built audit trails for my agents and none for myself. Two decades of work, and I couldn’t produce a clean evidence chain for half of it. To be precise about what that means: the work happened, and the people who were there can vouch for it — what was missing was the assembled record. Unassembled is not untrue. Closing that gap is what this diary is for.

LessonApply your own standards to yourself first, not last.

Four organizations, one map

LessonStructure is not overhead. It’s the kindness you owe the next person who has to understand your work — including the future you who has forgotten it.

Read the entry

I consolidated the whole portfolio into four GitHub organizations and wrote a single portfolio map — one document that lets any fresh collaborator, human or AI, orient in one read. It felt like administrative work until I watched it function: a new session with no memory of anything I’d built found the right repository, the right doctrine, and the right next step, unaided.

Even the architect drifts

LessonDiscipline is not a personality trait; it’s an architecture. If the gate isn’t in the path, assume it gets skipped — even by the person who built the gate.

Read the entry

Uncomfortable discovery: two recent designs were built without loading my own canonical rules. I wrote those rules. I enforce them on everything else. And I still drifted the moment no gate stood in my path. I spent today retrofitting both into the portfolio governance, and it cost more than building them right would have.

A second brain, rendered as a galaxy

LessonWhen a structure gets too large to hold in your head, change the medium before you change the structure.

Read the entry

I scaffolded Cortex today — a visual second brain that renders my portfolio doctrine as a galaxy. Products as systems, principles as stars, dependencies as orbits. I built it because text files were failing me: I could no longer see how thirteen products related, only read about it. The first render showed me what the documents never did — which parts of the portfolio are dense, and which are drifting alone at the edge.

The deploy that fought back

LessonThe runbook cost twenty extra minutes. The next deploy will cost an evening less.

Read the entry

First production deploy of Rapid Impact Analyzer to SAP BTP Cloud Foundry, migrated off Render. The deploy fought back the whole way: certificate chains that wouldn’t verify, a buildpack stuck in a rebuild loop, database permissions that failed only in production. I fixed each one and wrote each fix down as I went, and by the end the notes had become a reusable runbook — the same one I now publish, in full, in the library below.

These are quick notes. The longer write-ups — lessons turned into something you can use — live in Field Notes →

The library · All of it free

Methods you can use today. Complete, free, yours to pass on.

These are the working rules behind everything I ship, stated in full — no email wall, no excerpt games. Take them, use them, pass them on. If one saves you a bad quarter, tell someone else about it.

Framework

The Cost Ladder — when AI is and isn’t justified

Before reaching for AI, walk the ladder: rule → router → template → calculator → validator → AI → AI agent → human. Each step up costs more and runs slower than the one below it, and from validator through agent each step is harder to predict and to audit. The human sits at the top not because people are unpredictable, but because someone must own the consequences. Use the cheapest mechanism that is reliably correct; escalate only when the rung below demonstrably can’t do the job — and “demonstrably” means a failing test set, not a hunch: a feature doesn’t move up the ladder until the rung below fails written cases, and the AI rung doesn’t ship until it passes them. In my own products, most “AI features” turned out to be rules, templates, or calculators wearing a costume — and they got faster, cheaper, and more auditable the day I demoted them.

COST · LATENCY rule router template calculator validator AI AI agent human DETERMINISTIC — PREFER THESE ONLY WHEN JUSTIFIED FINAL AUTHORITY
  1. rule
  2. router
  3. template
  4. calculator
  5. validator — deterministic: prefer these
  6. AI
  7. AI agent — only when justified
  8. human — final authority

Apply todayPick one “AI feature” you own and ask which rung it actually needs. If a rule or a calculator can do it reliably, demote it — you’ll gain speed, money, and auditability in one move.

Standard

The Four Provenance Labels

Every AI-touched output in my products carries exactly one of four labels: Verified (traced to a source system), Calculated (deterministic math you can recompute), AI-Suggested (a model produced it; no human has accepted it), or Approved (a named human accepted it). There is never a fifth label and never an unlabeled value. Users always know what they can trust; auditors always know what to check. 

// not a guideline — a type. The renderer refuses
// any figure without a provenance; there is no default.
type Provenance =
  "verified" | "calculated" | "ai_suggested" | "approved";

interface Figure {
  value: number;
  provenance: Provenance;  // required
  sourceRef: string;       // where an auditor goes to check
}

Apply todayTake one dashboard you ship and label every number. The values you can’t label are the ones to worry about.

Doctrine

The Honesty Doctrine for product UI

A button that lies is a bug — same severity as a crash. If a control says “Export,” it exports. If a feature is a preview, the screen says so. If the data is sample data, it’s labeled “sample — not your result.” No disabled buttons posing as features, no progress bars animating over nothing, no “AI-powered” badge on a rule engine. Users forgive missing features. They don’t forgive being deceived — and in regulated industries, neither do auditors.

Apply todayClick every control on your main screen and ask whether it does exactly what its label promises. Fix or relabel the ones that don’t.

Runbook

The SAP BTP deploy runbook

The pipeline that finally worked for shipping a Node.js app to SAP BTP Cloud Foundry: GitHub Actions builds a multi-stage Docker image on every push to main, publishes it to GitHub Container Registry, and cf push pulls the image — no local Docker, ever. The gotchas that cost me hours: self-signed certificates in the database TLS chain (trust them explicitly; don’t disable verification), a buildpack rebuild loop (push the image, not the source), and a production database user that can’t create extensions (split schema setup from seeding). Every fix came out of the May 27 deploy in the diary above.

The full runbook — published here, in full
  1. Build in CI, never locally. GitHub Actions builds a multi-stage Docker image on every push to main and publishes it to GitHub Container Registry. Your laptop never needs Docker installed.
  2. Deploy the image, not the source. cf push --docker-image ghcr.io/<org>/<app>:<tag>. Pushing source invites the buildpack to rebuild your app its own way — that’s the rebuild loop. The image you tested is the image that runs.
  3. Monorepo gotcha: if you use pnpm workspaces, workspace:* references break inside the container build. Resolve them at image-build time in the Dockerfile (multi-stage: install, build, prune) rather than expecting the registry to understand them.
  4. Database TLS: BTP PostgreSQL presents self-signed certificates in the chain. When Node throws SELF_SIGNED_CERT_IN_CHAIN, trust the service’s CA certificate explicitly in your client config. Never set rejectUnauthorized:false — that passes the demo and fails the audit.
  5. Split schema from seed. The production database user typically cannot CREATE EXTENSION. Run extension and schema setup as a separate provisioning step with the elevated user; let the app’s boot path only create tables and seed data it owns.
  6. Container permissions: if the app writes anything at runtime, chown the app directory to the runtime user in the Dockerfile — Cloud Foundry won’t run your container as root, and the failure only shows up in production.
  7. Health checks: give manifest.yml a real HTTP health endpoint and make sure it answers before the DB is warm, or CF will kill a healthy app mid-boot.
  8. Write down every error the moment you fix it. This list exists because I did. The runbook cost twenty extra minutes; the next deploy cost an evening less.

Apply todayBuild in CI, deploy an image, and keep your own error log from the first failure on. Stuck on a step anyway? Email me — I answer these personally.

Framework

The 90-minute revenue diagnostic

In my experience, ninety minutes of evidence-scored review is enough to find the two constraints that matter. Score your revenue engine across six dimensions on a 1–5 scale, with one non-negotiable rule: no score without evidence. A 4 you can’t substantiate is a 2. Then let the two lowest scores define your next ninety days, and deliberately ignore the rest until then — spreading effort across all six is how diagnostics turn into wallpaper. The gap between the score you wanted to give and the score the evidence supports is itself the diagnostic.

Apply todayPull last quarter’s pipeline data and score honestly, evidence beside each number. The method above is the whole method; the scoring worksheet is a document, not a page — email me and I’ll send it complete and free.

Writing method

Fact discipline for documents

Every claim in a serious document gets one of four tags: OBSERVED (you saw it or measured it), INFERRED (it follows from things you observed), ASSUMPTION (you’re treating it as true in order to proceed), UNKNOWN (you don’t know, and you’re saying so out loud). The tags do their best work before anyone else reads the document: the moment you must choose one, you discover how much of what you “know” is actually assumption. I use this on architecture decisions, status reports, and on this website.

Apply todayTag every claim in the next status report you write. Expect to be humbled. Ship it with the tags left in — readers trust documents that admit their own uncertainty.

Do it yourself · Free

Place your own AI decision.

This is the first move I make on every engagement, turned into a tool you can run yourself. Pick one thing you’re thinking of putting AI on. It tells you — deterministically, with no AI involved — where that decision actually belongs on the cost ladder, and what it needs to be trusted. Nothing is sent anywhere; it runs entirely in your browser.

1 · What does this task actually produce?
3 · Which of these are true? Check all that apply — this decides the governance, not the mechanism.

The store · Paid work

Where I can do the work with you.

The library and the tool above are free and always will be. This is the part where I roll up my sleeves on your real system. Run the tool first — then, if you want it done on your initiative with your constraints and your compliance people in the room, that’s a deep dive.

Deep dive · Start with the free tool above

Business AI Architecture review

The tool above places your decisions for free. This is the same thing, done on your real system: I’ll review your AI initiative and tell you honestly whether it should be deterministic, AI-assisted, or agentic — and what it needs to survive compliance review. You get a written assessment with the reasoning shown: where the cost ladder says “rule, not model,” where approval gates belong, and what your audit trail is missing. If the honest answer is “you don’t need AI here,” that’s the answer you’ll get.

Try the tool, then tell me what it surfaced — that’s the fastest start.

Ask for a deep dive
Diagnostic · Available

Revenue architecture diagnostic

For revenue leaders. The same six-dimension, 1–5 framework I give away in the library — but run with me, on your real data, with the evidence rule enforced. You get a scored readout, the two dimensions that should own your next ninety days, and a written rationale you can put in front of your board without me in the room.

Run with me, on your real numbers, with the evidence rule enforced.

Scope the diagnostic
Subscription product · Ask for a walkthrough

RevTXM

My revenue-management SaaS: multi-tenant, subscription-billed, in production and early — I’ll tell you exactly how early on the walkthrough. Built on the same architecture I advise on — deterministic core, provenance labels on every figure, approval gates on every consequential action. I’d rather show it than describe it, so the honest next step is a walkthrough on your screen, with your questions.

No self-serve checkout here on purpose — a demo first is fairer to you.

Ask me for a walkthrough
Engagement · Available

Hands-on build

I don’t only draw the architecture — I build it. Production AI with a deterministic core, human approval gates, per-agent permission limits, and an audit trail designed for regulated review. This is how I built RevTXM and the miniapps, and it’s the only standard I build to. Best fit: an enterprise team with a real problem, real data, and a compliance function that will eventually ask hard questions.

For when the blueprint needs to become a running system.

Talk about a build

A note on how this store works: every button above opens an email to me, because that’s the truth of how an engagement starts. There’s no checkout, no “only 2 spots left,” and no price I made up to look confident. You write, I answer personally, and we find out together whether the fit is real. If it isn’t, I’ll say so and point you somewhere better. One more thing you’d find out anyway: I hold a full-time enterprise role alongside this practice. Conflicts and boundaries are the first thing we cover in that first email — I’d rather lose an engagement than blur that line.

Hiring instead of buying? Some teams engage me through this store; some hire me into the building — forward-deployed AI engineering and SAP AI architecture roles. Same person, same doctrine. Email me the role and I’ll answer with specifics.

The TL;DR

The deterministic-first read, every week.

Not a link dump. Five or six things from where business, AI, and tech actually collide — chosen because they’re controversial, genuinely relevant, or because the headline missed the point and I have a sharper read. Each one comes with what it means and how it changes what you should do.

The honest contract: everything in it is free on this site too. Subscribing changes when you get it, not whether you can read it. No spam, no selling your address, unsubscribe whenever.

Get the weekly TL;DR

The last word

If you’re standing in that gap right now —

— between the demo that impressed everyone and the deployment nobody will sign off on — write to me. I read everything, I answer personally, and “you don’t need me” is an answer I give often.

Write to me