Industry · Manufacturing
The intelligence layer over your production floor.
We build the software that turns your machine, line, and supply-chain data into decisions — production analytics, predictive maintenance, quality, and supply-chain visibility.
On top of the systems you already run. We do not touch the hardware, PLCs, or controls.
On top of your floor, never modifying the line
The problem
Why so much factory-floor data never turns into a decision.
The data isn't absent — it's trapped. A modern plant is already instrumented with PLCs, SCADA, MES, and ERP, yet most of it sits in silos, sampled into spreadsheets and read after the shift instead of during it.
So you learn a machine was failing or a batch was out of spec after it cost you. The gap isn't more sensors — it's the software layer that reads what the floor already knows and acts while there's still time.
a year in unplanned downtime for the largest manufacturers.
is what a single idle automotive line can cost.
Siemens Senseye, 2022 ↗
What we build
Where manufacturing software earns its keep — and what each use case delivers.
Applications that sit on top of your existing equipment and systems. For each: what it does, the benefit it produces, and how that plays out.
Production analytics & OEE
Run, downtime, scrap, and cycle data in one live OEE picture — not a spreadsheet read after the shift.
Benefit — losses become visible while you can still act.
Example: a shift lead catches a cell's creeping cycle time mid-run, before it becomes the day's scrap.
Predictive maintenance
Learns a machine's normal signature and flags drift before failure, so you service it on the next planned stop.
Benefit — less unplanned downtime, longer equipment life.
Example: a bearing's vibration drifts; flagged days out, the swap happens on Sunday's planned stop, not Tuesday's line-down.
Quality & defect detection
Catches out-of-spec drift early from process and inspection data — and classifies defects from vision data where it exists.
Benefit — lower scrap, fewer escapes, root cause found not guessed.
Example: a variable trending out of spec alerts at unit 40, not at the customer's inspection of lot 4,000.
Supply-chain & inventory visibility
Unifies supplier, inventory, and schedule data into one view, surfacing demand and lead-time signals before a line-down or overstock.
Benefit — fewer stoppages, less cash frozen in inventory.
Example: a supplier's slipping lead time surfaces a week early, so the buyer re-sources before the line runs dry.
Connected-worker & digital work instructions
Puts SOPs, changeover steps, and procedures in front of operators in plain language at the point of work, queryable on the floor.
Benefit — faster, more consistent execution; less tribal-knowledge risk.
Example: a new operator runs a changeover from tablet guidance at the line's standard time, without waiting for the one veteran who knows it.
Custom MES-adjacent & ERP-integration apps
The targeted app off-the-shelf systems don't cover — scheduling, traceability, shop-floor data collection — integrated with your MES and ERP, not replacing them.
Benefit — purpose-built software without a multi-year migration.
Example: a traceability app fills the MES–ERP gap, so a recall query that took two days resolves in minutes.
The measured impact
What this software does to the work.
Independent industry findings — not Silicon Prime's own client results.
Less machine downtime
with predictive maintenance, plus 20–40% longer machine life — the direct prize a maintenance model is built to capture.
McKinsey ↗
Labor-productivity gains
and roughly 48% shorter lead times across the lighthouse cohort of digitally transformed factories.
WEF / McKinsey Lighthouse ↗
Production-output gains
and 15% unlocked capacity, per 600 manufacturing executives surveyed on their digital initiatives.
Deloitte 2025 Survey ↗
The scope
What manufacturing software development covers.
The software layer that reads, predicts, and decides on top of your floor — we read from the hardware and controls, never modify them.
Production analytics, OEE & dashboards
Live and historical analytics over machine, line, and MES data — OEE, downtime, scrap, throughput — built on your data through our data engineering work.
Predictive maintenance & ML models
We build the machine-learning models that flag pre-failure drift, validated against your historical breakdowns so the alerts are trustworthy — not noise the floor ignores.
IoT & sensor data pipelines
The pipelines that get machine, sensor, and historian data off the floor into a usable store — our IoT and AIoT development for the edge-to-cloud plumbing.
Quality, traceability & computer vision
Process monitoring, defect detection, and lot/batch traceability — including vision inspection where camera data exists — so drift is caught early and genealogy is provable for recalls.
Supply-chain visibility & planning tools
Applications that unify supplier, inventory, and schedule data into one view, with demand and lead-time signals that warn before a stockout or line-down.
MES/ERP integration & custom shop-floor apps
Targeted apps integrated with your MES and ERP — scheduling, data collection, traceability — plus modernization of legacy plant systems too brittle to build on.
What you get — all yours under full work-for-hire IP transfer
How it runs
One accountable lead, fixed scope, no handoffs.
The same delivery model behind all our work, tuned for the plant floor. Most engagements reach production in 4–8 weeks, full IP assignment signed at kickoff.
Discover
Scope the use case and the loss it targets, and confirm the data exists — run as our AI readiness assessment, honest "not ready yet" call included.
Output: a ranked plan & the metric we'll be judged on
Connect
Pull machine, MES, ERP, and historian data into a usable store through governed, read-only pipelines — we never touch the line or controls.
Output: a trusted data foundation
Build
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
Deploy & enable
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
The track record
The reliability bar a plant-floor system has to clear.
Straight up: our deepest case studies are not in manufacturing. What transfers is the engineering rigor a factory-floor system demands — and a track record of holding exactly that kind of system live without breaking it.
At BJ's Restaurants, a 200+ location operation, we moved releases from biweekly to twice a week over four years while sustaining zero critical defects — the same "ships fast, never breaks operations" standard a floor system must meet.
Our closest industrial work is YardClub — the heavy-equipment marketplace we built end to end. It processed $120M+ in transactions and was acquired by Caterpillar in 2017, so the machinery domain is familiar ground.
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 automakers. If your problem is a stretch, we'll say so and scope a contained pilot first.
Why build it with us.
The software layer, honestly scoped. We build the intelligence on top of your floor, not the hardware or controls — a faster, lower-risk engagement with no overpromising.
Production discipline first. The same evals-before-release, staged-rollout, monitor-after discipline that holds a 200+ location operation at zero critical defects.
Models you can trust, validated on your data. Validated against your historical record before going live, with human-in-the-loop review where a wrong call is costly.
Founder-led, built to transfer. One accountable lead; the code, models, and pipelines are assigned to you, your team trained to run them.
Where this connects
The floor's intelligence layer rarely stands alone.
It rests on the same engineering we bring to neighboring work.
Data & analytics engineering
The clean foundation OEE, analytics, and every model run on — so decisions rest on data you trust.
Data engineering →IoT & AIoT development
The edge-to-cloud plumbing that gets machine, sensor, and historian data off the floor into a usable store.
IoT development →Machine learning development
The predictive-maintenance, quality, and vision models — validated against your history before they go live.
ML development →Questions buyers ask before they build.
Thirty minutes · No pitch deck
Ready to turn floor data into decisions while there's still time to act?
Bring the loss you want to attack — unplanned downtime, scrap, a blind supply chain — and we'll tell you honestly whether your data supports it, what it takes to build, and what it costs to run.