Intelfactor AI @IntelFactorAI
Digitalizing the data infrastructure for agentic manufacturing https://t.co/CC2AO0SDNR intelfactor.ai CA, United States Joined October 2025-
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@AndyRenfrew Its maximizing value through truth.
@awscloud More AI-Agents doesn’t make your revenue grow faster. It might actually slow you down.
@paulg Office hours sounds like high school.
I was waiting for someone to point that out. Truth is we expect our (factories) customers to have their GPU/Compute/Edge device along with their industrial cameras(IR, thermal etc) and also setup lighting. Intelfactor isn’t a hardware company. We are the layer above: turning unstructured data into decisions (CAPA triggers, spec routing, operator workflows, audit trail). The first 2-3 customers forces us to fall into the “consulting trap” . Thus where we go per-site, not a single global policy. SKU mix, lighting, even shift patterns shift the distribution enough that one universal threshold ends up either over- or under-triggering somewhere. We calibrate per-line during pilot and let the Quality Ops layer re-tune as labeled events accumulate. What's your take — are you trying to converge on one policy, or living with per-site drift?
Plan mode handled the flow surprisingly well. Big win: it kept the deployment sequence grounded: Jetson → FLIR capture → TensorRT inference → PASS/FAIL/REVIEW → local SQLite evidence → IntelBase cloud sync → dashboard visibility. Main friction: edge deployments still expose the boring-but-critical stuff: camera config, lighting, mounting, network reliability, and making sure local ops don’t depend on cloud being perfect. That’s exactly why we’re building IntelBase edge-first.
@IntelFactorAI @MichaelGannotti @databricks That's awesome! 🔥 Loving the real-world edge AI deployment with Grok Build Beta on your IntelBase platform—Jetson + FLIR, TensorRT optimization, defectIQ policy upgrades, SQLite store, and Databricks sync is a solid stack. How did the planning mode handle the full flow? Any
Gthered the context, executed 90% workflow of Jetson + FLIR on 1st test: TensorRT inference, SQLite evidence storage, local PASS/FAIL/REVIEW, and IntelBase synchronization all generated clean results, minimal issues. #grokbuild fr flawlessly executes w/systemd and watchdog scripts, resulting in zero revisions and eliminating any potential friction points. 🙏
@FlohCreative @NVIDIAAI @flir Why dont you ask grok then?
3 million units of kitchen knives, fast lead times, low defect rates? This is how factory AI actually works. @NVIDIAAI + @flir spotting defects on knife blades in real time with our API - you can have a fleet.
1/ We just installed IntelFactor at a knife factory in an enterprise factory. 2/ Real-time pass/fail on every blade. 3/ Workers love the dashboard on their phones. Video in next post ↓
@garrytan Finally decided to test intelbran MVP in intelfactor using GBrain. Next - Real Jetson + FLIR inspection data (DynamoDB → scoped markdown brain repo). The graphQL + schema for synthesis, defect trends, CAPA support, and customer traceability is attractive Edge remains authoritative though. Cloud + GBrain is additive only.
@grok @MichaelGannotti literlayy using @grokbuildbeta on IntelBase edge AI platform right now. FIrst planning, then writing Jetson + FLIR deployment for faster local TensorRT inference, improvments in our defectIQ policy, SQLite evidence store, and cloud sync to @databricks live dashboards.
@Shockwave_App @NVIDIAAI @flir @Shockwave_App Stay in your lane, king. 🔥Your XRPL token-shilling AI company can’t even detect basic manufacturing defects but suddenly you’re the pixel police?
@julianweisser @solofounders I support this.
1/ We just installed IntelFactor at a knife factory in an enterprise factory. 2/ Real-time pass/fail on every blade. 3/ Workers love the dashboard on their phones. Video in next post ↓
Factory floor AI that actually works. Jetson + FLIR spotting defects on knife blades in real time. Minimal latency, cybersecurity risks . No cloud needed. This is the next step to AI for manufacturing #EdgeAI #Intelfactor #Greengrass
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