The intelligence layer over your production floor.
We build the Industry 4.0 software that turns machine, line, and supply-chain data into decisions — production analytics, predictive maintenance, quality systems, and end-to-end visibility.
It’s grounded in the data your plant already produces and wired to the ERP and MES you already run. We build the software and the intelligence — we do not touch the hardware, PLCs, or controls. Built on your data, fixed scope, production in 4–8 weeks.
Because the data is trapped, not absent. A modern plant is already instrumented — PLCs, SCADA, MES, ERP, quality logs, maintenance records — yet most of it sits in silos that don’t talk to each other, sampled by hand into spreadsheets, read after the shift instead of during it.
The result is the most expensive failure mode in manufacturing: you find out a machine was failing, a batch was out of spec, or a line was starving after it has already cost you.
The gap isn’t more sensors or a rip-and-replace of the line — it’s the software layer that reads what the floor already knows, predicts what’s about to happen, and puts it in front of the right person while there’s still time to act. That layer is what manufacturing software development, done properly, delivers.
This isn’t one product. It’s a set of high-leverage applications that sit on top of your existing equipment and systems. For each: what it does, the benefit it produces, and a one-line illustration of how that plays out.
Pulls run, downtime, scrap, and cycle data from your machines and MES into one live picture of overall equipment effectiveness — by line, shift, and SKU, in real time instead of in a Monday report. Benefit — bottlenecks and losses become visible while you can still act on them. Hidden micro-stoppages and slow cycles surface as the largest recoverable losses rather than vanishing into a shift average.
Example: a plant manager sees a single station quietly dragging line OEE down at 2 p.m. and reroutes around it before the shift’s output target is blown — instead of discovering the shortfall in the next-day report.
Learns the normal signature of a machine from its sensor and historian data and flags the deviation that precedes a failure — turning “fix it when it breaks” into “service it on the next planned stop.” Benefit — unplanned downtime falls and equipment lasts longer.
Example: a bearing trending toward failure is caught a week out and replaced during a scheduled changeover, so the line never suffers the unplanned stop — and the $2-million-an-hour idle automotive line never happens.
Monitors process parameters and inspection data to catch out-of-spec drift early — and, where vision data exists, classifies defects far faster and more consistently than a manual check. Benefit — lower scrap, fewer escapes, and the root cause found instead of guessed.
Example: a process variable drifting toward the spec limit triggers an alert mid-run, so the batch is corrected before it becomes scrap or a customer return — rather than being caught at final inspection after the cost is sunk.
Connects supplier, inventory, and production-schedule data into one view, with demand and lead-time signals surfaced before they become a line-down or an overstock. Benefit — fewer stockouts and material-driven line stoppages, and less cash frozen in inventory.
Example: a delayed inbound component is flagged against the production schedule days early, so planning re-sequences the line instead of discovering the shortage when the station starves.
Puts SOPs, work instructions, changeover steps, and maintenance procedures in front of operators and technicians in plain language at the point of work — and lets the floor query them instead of paging a supervisor. Benefit — faster, more consistent execution and less tribal-knowledge risk.
Example: an operator on a rarely-run product retrieves the exact changeover steps on the line instead of waiting for the one person who remembers them — so the changeover is right the first time.
Builds the targeted application your operation needs that off-the-shelf systems don’t cover — a scheduling tool, a traceability layer, a shop-floor data-collection app — integrated with your existing MES and ERP rather than replacing them. Benefit — the workflow your plant actually runs gets purpose-built software, without a multi-year platform migration.
Example: a traceability requirement from a new customer is met with a focused app that stitches existing lot and batch data into a compliant genealogy — in weeks, not a system overhaul.
The scope below is the software and intelligence layer — what reads, predicts, and decides on top of your floor. We do not build or modify the hardware, PLCs, or machine controls; we read from them.
Real-time and historical analytics over machine, line, and MES data — OEE, downtime, scrap, and throughput surfaced by line, shift, and SKU. Built on your data through our data engineering work, so the numbers are trusted before anyone acts on them.
We build the machine-learning models that learn each asset’s normal behavior and flag pre-failure drift, validated against your historical breakdown data so the alerts are trustworthy — not a false-alarm generator the floor learns to ignore.
The connectivity and pipelines that get machine, sensor, and historian data off the floor and into a usable store — our IoT and AIoT development for the data plumbing, edge-to-cloud, that everything else depends on.
Statistical process monitoring, defect detection, and lot/batch traceability — including vision-based inspection where the camera data exists — so out-of-spec drift is caught early and genealogy is provable for audits and recalls.
Applications that unify supplier, inventory, and schedule data into one view, with demand and lead-time signals that warn before a stockout or a line-down.
Targeted applications integrated with your existing MES and ERP — scheduling, data collection, traceability, connected-worker tools — plus modernization of the legacy plant systems that are too brittle to build on, without ripping out what works.
What you get when you hire us — all assigned to you under full work-for-hire IP transfer
The same delivery model behind all our work, tuned for the plant floor — one accountable lead, fixed scope, no handoffs to account managers.
Scope the use case and the loss it targets (downtime, scrap, lead time), and confirm the data exists to support it. Run as our AI readiness assessment, with the honest “the data isn’t ready for this one yet” call included.
Output: a ranked plan & the metric we’ll be judged on
Pull machine, MES, ERP, and historian data into a usable store through governed, read-only pipelines. We read from your existing systems; we do not replace the line or touch the controls.
Output: a trusted data foundation
Develop the application and, where it applies, train and validate the model against your historical data, in your own cloud or on-prem environment.
Output: a working system tested on your real data, not a demo
Pilot on one line or asset, prove the metric moves, then scale across the plant — your team trained to operate, retrain, and extend it.
Output: a production system & a team that owns it
Most engagements reach production in 4–8 weeks, payment is tied to the ROI we agreed at kickoff, and full work-for-hire IP assignment is signed before we start.
A system that decides whether a line keeps running has to be right, and stay right — which is exactly the production discipline Silicon Prime is built on.
To be straight about it: our deepest case studies are not in manufacturing. What transfers directly is the engineering rigor a factory-floor system demands, and we’ll show it through honest adjacent examples rather than a manufacturing case we don’t have.
The clearest proof of that rigor is BJ’s Restaurants — a 200+ location operation whose software is critical to daily operations. Over four years we moved their release cadence from every two weeks to twice a week while sustaining zero critical defects, through evals before release, staged rollout, and continuous production monitoring. That is a different industry, but it is precisely the “ships fast, never breaks the thing operations depend on” standard a production-monitoring or predictive-maintenance system has to meet.
Our closest work to the industrial world itself is YardClub, the heavy-construction-equipment marketplace we built end to end — it processed $120M+ in transactions and was acquired by Caterpillar in 2017, so the machinery domain and its data are familiar ground, even though that engagement was a marketplace, not a factory system.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, including multimillion-dollar systems for one of the world’s largest automobile manufacturers, and personally accountable for every engagement. If your problem is a genuine stretch for what we’ve shipped, we’ll tell you, scope a contained pilot to prove it before you commit, and put the accountability in writing.
The software layer, honestly scoped. We build the intelligence on top of your floor — analytics, models, applications — not the hardware, PLCs, or controls. That boundary means a faster, lower-risk engagement and no overpromising on a controls integration we don’t do.
Production discipline first. A plant-floor system is judged on uptime and trust. The same evals-before-release, staged-rollout, monitor-after discipline that holds a 200+ location operation at zero critical defects is what we bring to a system your line depends on.
Models you can trust, validated on your data. We validate predictive and quality models against your historical record before they go live, with human-in-the-loop review where a wrong call is costly — because the fastest way to kill a factory-floor system is a flood of false alarms the floor learns to ignore.
Founder-led, built to transfer. One accountable lead, not a relay of account managers; and the code, models, and pipelines are assigned to you with your team trained to run them when we step back.
What plants want to know before they build software on the floor data they already have.
No — we build the software and intelligence layer on top of your floor: production analytics, predictive maintenance, quality systems, supply-chain visibility, and custom applications. We read from your PLCs, SCADA, and historians through governed connections, but we don’t program controllers or modify the line itself. Keeping that boundary clear is part of why our engagements are fast and low-risk; for the controls work we’ll point you to the right specialist.
No. We integrate with the MES and ERP you already run rather than replacing them — most of our manufacturing work sits alongside those systems, reading their data and adding the analytics or application that’s missing. Where a legacy plant system is genuinely too brittle to build on, we’ll modernize that piece without ripping out what works, and we’ll be honest about which approach your situation actually needs.
Independent research is consistent here: predictive maintenance typically reduces machine downtime by 30–50% and extends machine life by 20–40%, per McKinsey. Your result depends on your data — you need enough sensor and failure history for a model to learn the pre-failure signature, which is exactly what we confirm in the discovery phase before promising anything. We validate every model against your historical breakdowns so the alerts are trustworthy, not noise.
We’ll be straight: our named case studies are in restaurants (BJ’s, a 200+ location operation held at zero critical defects for four years) and an industrial-equipment marketplace (YardClub, acquired by Caterpillar), not in factory operations. What carries over is the production-reliability engineering a plant-floor system demands and deep familiarity with the machinery domain. For a first manufacturing engagement we scope a contained pilot on one line or asset to prove the value before you commit — the accountability is the founder’s, in writing.
The software runs in your own cloud or on-prem environment under your access controls; data pipelines off the floor are read-only and scoped to what the use case needs; and every engagement starts with an NDA and a security review. We document every data path so your IT and OT teams can verify rather than trust, and we keep operational-technology and IT boundaries intact rather than bridging them carelessly.
You do — completely. The applications, trained models, data pipelines, and dashboards transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to operate, retrain, and extend them. Keep us on a reduced retainer or take the keys; the engagement is built around the handover, not around locking you in.
Most engagements reach production in 4–8 weeks under a fixed-scope, ROI-tied model with one accountable lead, and we typically prove the metric on a single line or asset first before scaling. Build cost depends on scope — our AI development cost guide gives real ranges — and we set the target metric at kickoff so the value is measured against a baseline, not assumed. If you want to understand why so many factory-AI pilots stall before production, our analysis of why enterprise AI projects fail is the honest version.
Thirty minutes · No pitch deck
Bring the loss you want to attack — downtime, scrap, lead time, a stuck workflow — and we’ll tell you honestly whether the data supports it, what it takes to build, and what it costs to run.