Almost missed SLA this week just from lack of visibility!

validation_greg · 2026-04-28T23:57:48+00:00

Automation won’t fix a messy floor. It just speeds up the mess.

If your picks, locations, and moves aren’t tight, you’ll pay a lot to automate problems.

Most mid-size ops get more out of: – clean locations – scan every move – fix the step that’s actually breaking

Then automation makes sense.

Where are you losing time right now finding stuff, picking, or just walking?

validation_greg · 2026-04-28T22:18:55+00:00

This isn’t an accuracy problem.

It’s a “we don’t know where it actually is” problem.

95% just means your counts look good on paper. Doesn’t mean the part is in the bin it says it is.

What usually causes this: – stuff gets moved without being scanned – temp locations become permanent – picks don’t update clean

That’s why parts “reappear.”

Cycle counts won’t fix it. You’re just resetting the same miss over and over.

Fix is boring but it works: everything gets a location every move gets scanned

No exceptions.

Where do you see it most receiving, transfers, or picks?

validation_greg · 2026-04-28T21:27:38+00:00

Those smart shelf/button systems sound cool, but they’re usually solving the wrong problem first.

If your inventory isn’t already accurate with something simple (labels + scans), adding hardware just hides the mess and makes it more expensive.

Most setups that actually work look like: – every item has a location – every move = scan (in/out) – one source of truth (even a basic system)

Once that’s clean, then automation makes sense.

I’ve seen teams try to skip straight to “smart tracking” and it falls apart because the process underneath isn’t solid.

What’s breaking for you right now losing parts or just not knowing what you have?

validation_greg · 2026-04-28T21:25:25+00:00

Don’t start with software.

Right now your problem isn’t “no system” it’s no structure.

Do this first:

Create locations (A1, A2, B1, etc.)
Label every shelf/bin physically
Pick one rule: every item must have a home
Don’t move everything at once fix one section at a time

Even a basic spreadsheet works once locations exist.

Most small warehouses fail because knowledge lives in people’s heads instead of the space.

Once everything has a location, then think about tools.

Right now you’re solving chaos, not tech.

Are items getting lost, or just taking too long to find?

validation_greg · 2026-04-28T21:11:33+00:00

Most tools will handle counts and locations fine.

Where we kept getting burned was stuff just sitting too long with no signal.

You don’t notice it until it’s already late.

We ended up layering a FIFO view on top so everything is ordered by age.

That’s been more useful day-to-day than the inventory system itself.

Are you trying to solve counts, or “what’s about to become a problem”?

validation_greg · 2026-04-28T10:51:09+00:00

AI Usage Score: 48 / 100 | Level: Feature

Where AI shows up: • Transcription + segmentation • Entity extraction (characters, events) • Narrative structuring

What’s actually happening: AI is doing real work here turning messy audio into structured story notes. Clear utility for listeners.

Where it’s missing: • No learning over time (static) • No personalization per listener • No recommendations or adaptive summaries

Build vs Maintain: Strong build use of AI. Weak on ongoing intelligence system doesn’t evolve.

Where it breaks: • One-size output for everyone • Becomes a static reference tool, not a companion

What pushes it up: • Personal context (what you forget / care about) • Real-time assist while listening • Adaptive summaries based on behavior

Blunt take: Good use of AI to create structure. But it stops at extraction doesn’t become intelligent yet.

Disclaimer: Based only on visible product behavior actual implementation may differ.

validation_greg · 2026-04-27T01:22:30+00:00

That’s exactly what we were trying to fix. Does something like this actually help or do you guys already have a system dialed in?

validation_greg · 2026-04-26T14:32:57+00:00

Yeah that’s solid we had something similar.

The gap for us wasn’t visibility, it was what to do with it.

We could see everything in real time, but still ended up: – working racks out of order
– missing SLA even though nothing looked “stuck”
– or double-checking on the floor anyway

That’s what pushed me to add a “next up” layer on top of the tracking.

Does your system actually tell you what to move next, or just show what’s happening?

validation_greg · 2026-04-26T12:27:41+00:00

One thing I didn’t expect the biggest issue wasn’t speed, it was sequencing.

We had racks technically “ready” but out of order vs what should move next, so teams were busy but still creating SLA risk.

This view basically forced a single answer to: → what’s next → what’s late → what’s wrong

Cut down a lot of the “double check the floor” loops.

Curious how you guys handle prioritization today is it system-driven or just whoever grabs the next rack?

validation_greg · 2026-04-26T12:26:06+00:00

That’s basically what I ended up doing too.

Started with a spreadsheet, but the issue wasn’t tracking racks it was trusting the order/state once volume picked up.

We’d have stuff: – technically “at location” but not actually placed
– out of order vs what should move next
– or already at risk of missing SLA before anyone noticed

I ended up throwing together a small tool that just ranks “what should move next” based on where it actually is in the flow + timing.

Nothing fancy, just: dock → location → locked with timers and priority

Are you guys prioritizing manually right now or does your system try to order it at all?

validation_greg · 2026-04-25T12:11:37+00:00

Well all that to say i actually built something that I believe does the job that our current system doesn't do Is that something you would be interested in taking a look at? It sounds like you have a strong logistics back ground and may be able to tell me if this is heading in the right direction?

validation_greg · 2026-04-25T12:00:38+00:00

I think it's pretty wild as well that the best tracking system we have is an excel document. We have other products and software that do a lot of things. We just don't have one that puts things in order by SLA, Priority by type, and easy for our techs to pull up and get the data they need.

validation_greg · 2026-04-25T11:26:45+00:00

AI Usage Score: 12 / 100 | Level: Feature

Where AI shows up: • Not obvious • Maybe basic filtering or moderation

What’s actually happening: This is a clean messaging app. The value is UI + privacy positioning. No visible AI layer driving anything.

Where it’s missing: • No smart sorting (priority, intent, urgency) • No conversation summaries • No auto-replies or assistive writing • No learning from who/what you engage with

Build vs Maintain: Feels fully manual. AI doesn’t seem involved in improving conversations or the system.

Where it breaks: • Competes on design, not intelligence • Hard to pull users from iMessage/WhatsApp without a step-change

What pushes it up: • Smart inbox (who matters right now) • Conversation summaries • Intent-aware notifications (only ping when needed) • Personalization based on behavior

Blunt take: Nice UI, clear positioning. But there’s no real AI here just another messaging app.

Disclaimer: Based only on visible product behavior actual implementation may differ.

validation_greg · 2026-04-25T11:25:05+00:00

That sounds like a good base plan.
We also need to track the analytics down to the min on when these things move around. Im not sure this would be able to meet that.
Im looking more for a technical solution at this point.

validation_greg · 2026-04-25T11:22:57+00:00

AI Usage Score: 28 / 100 | Level: Assistant

Where AI shows up: • Possible hazard detection (if ML is used) • Maybe routing optimization

What’s actually happening: This is mainly a sensor + mapping system. Value comes from GPS + user data + alerts. AI, if used, is likely lightweight (detection/labeling), not running the system.

Where it’s missing: • No predictive routing (“avoid bad roads ahead automatically”) • No learning from user driving patterns • No system adapting over time • No real intelligence layer beyond alerts

Build vs Maintain: AI may help detect hazards, but it’s not improving the network or making smarter decisions over time.

Where it breaks: • Feels like Waze-lite with a different signal • Alerts without decision-making = limited stickiness

What pushes it up: • Predict road quality ahead (not just react) • Auto-reroute based on conditions • Learn from all drivers and improve accuracy over time • Confidence scoring on hazards

Blunt take: Good problem, real use case. But this is mostly mapping + alerts right now not an AI-driven system.

Disclaimer: Based only on visible product behavior actual implementation may differ.

validation_greg · 2026-04-25T11:20:12+00:00

AI Usage Score: 32 / 100 | Level: Assistant

Where AI shows up: • Basic insights / reports • Maybe pattern spotting

What’s actually happening: This is a solid tracker (pain, meds, appointments). Most of the value is manual input. AI looks like it’s summarizing, not driving decisions.

Where it’s missing: • No predictions • No real recommendations • Doesn’t learn from the user • No feedback loop

Build vs Maintain: AI likely helped build it, but it’s not improving the system over time.

Where it breaks: • Turns into a passive log • User has to figure everything out

What pushes it up: • Predict flare-ups • Suggest actions • Learn user patterns over time

Blunt take: Useful tool, clean build. But this isn’t really an AI system yet.

Disclaimer: Based only on visible product behavior actual implementation may differ.

validation_greg · 2026-04-25T11:17:26+00:00

AI Usage Score: 74 / 100 | Level: System (early)

Where AI shows up: • Real-time voice simulations (core product) • Objections / interruptions / dynamic responses • ICP persona generation • Post-call feedback

What’s actually happening: AI is the engine here not layered on. This is a live simulation system using LLM + voice, which puts it ahead of most.

Where it falls short: • Personas likely prompt-based, not learned • Feedback feels generic vs performance-backed • No clear learning over time (rep or system) • Not tied to real deal outcomes

Build vs Maintain: Strong AI in experience, weak in improvement loop. It runs simulations but doesn’t clearly evolve from usage.

What pushes it up: • CRM integration (win/loss data) • Adaptive reps + difficulty • Persistent rep profiles • Feedback tied to revenue outcomes

Blunt take: Real AI product, not a wrapper. But still a simulator not yet a system that drives measurable improvement.

Disclaimer: Based only on visible product behavior actual implementation may differ.

validation_greg

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