Service · IoT
IoT development that turns telemetry into decisions.
We build the software that makes connected devices useful — ingestion at scale, edge-to-cloud pipelines, dashboards and alerts your team acts on, and fleet management that keeps thousands of devices healthy. In your own cloud, with full IP transfer. We build the IoT software — not the hardware or firmware.
The line we draw
What we build
The real problem
Why most IoT projects collect oceans of data and act on almost none of it.
Because the hard part was never connecting the device — it was the software underneath. Telemetry starts flowing and lands in a store nobody queries. Dashboards show raw readings instead of decisions; an alert that should fire in seconds surfaces in a weekly report; a firmware fleet drifts out of date because there's no safe way to push updates.
The devices work. The system around them doesn't. That gap between data collected and value captured is the entire job of IoT development — and it's a software problem: ingestion that holds up under load, pipelines that decode and validate, storage built for time-series, and a layer that turns readings into action.
Of the data from a typical offshore rig's ~30,000 sensors is actually used — and mostly to detect anomalies, not to optimize or predict.
McKinsey, 2015 ↗
Connected IoT devices globally by end of 2025, growing 14% year-over-year — the scale ingestion has to be engineered for from day one.
IoT Analytics, State of IoT 2025 ↗
Where it pays off
Where IoT development actually pays — and what each delivers.
IoT software isn't one product; it earns its keep in a handful of specific, high-volume operations. (Examples are illustrative, not client results.)
Equipment & asset monitoring
Ingests sensor data — vibration, temperature, current, pressure — and flags the patterns that precede failure. Fewer breakdowns, lower maintenance cost.
A motor's vibration trends toward a known failure mode and the platform raises a work order while the line still runs — turning a shutdown into a scheduled ten-minute swap.
Fleet & device management
Tracks the health, connectivity, configuration, and firmware of every device, and pushes updates safely. Thousands stay current without a truck roll.
A security patch ships to 10,000 field gateways in a staged rollout that halts automatically when the first batch errors — not a manual update that misses half the fleet.
Remote operations & telemetry dashboards
Turns live device data into operational dashboards and real-time threshold alerts. Faster response; the exception finds the operator.
A cold-storage unit drifts above safe temperature and an alert reaches the on-call tech in seconds — saving inventory a daily manual check would have lost.
Connected-product & consumer IoT
Builds the cloud backend, app, and device APIs behind a connected product so customers control it and you can support it. A product people can actually use remotely.
A customer adjusts a connected appliance from their phone, and when they call support the agent sees the same live state — diagnosed in one call.
Energy & utilities telemetry
Turns meter, grid, and environmental data into consumption analytics, anomaly detection, and demand signals. Faster fault detection across distributed assets.
A sudden consumption drop across a feeder is flagged as a likely fault the moment it happens — not when complaints come in.
Supply-chain & logistics tracking
Tracks location, condition, and chain-of-custody across goods in transit. Fewer losses and a verifiable record of condition.
A refrigerated shipment that warms past its threshold triggers an alert en route — so the load is rerouted or salvaged, not written off on arrival.
As of June 2026 · revisit quarterly
What IoT does to those operations — the measured impact.
Independent, named-source findings — cited as third-party evidence, never Silicon Prime's own client results.
The prize is the software. $5.5–12.6T in IoT economic value globally by 2030, with ~65% in business/B2B settings — but capturing it depends on the software and integration layer, not the sensors.
McKinsey, 2030 outlook ↗
Acting beats storing. 10–20% higher equipment uptime from predictive maintenance, with 5–10% lower maintenance cost and 20–50% less planning time — the payoff of acting on sensor data, not warehousing it.
Deloitte Insights, 2017 ↗
Scale compounds. The connected-device base grows 14% a year toward 21.1B devices — which is why ingestion and fleet management have to be engineered for scale from day one, not bolted on later.
IoT Analytics, State of IoT 2025 ↗
What's included
What IoT development covers — and the line we draw.
We build the IoT software platform: the layer that sits above the device and below the decision. We don't design hardware or write firmware, and the AI that reasons over the data is our separate Physical AI work.
Device connectivity & ingestion
Ingestion over the protocols devices actually speak — MQTT, CoAP, HTTP, and the gateway between — built to hold up when the whole fleet reports at once, not just in a demo.
Edge-to-cloud pipelines
We split edge versus cloud — acting on data near the device where latency or bandwidth demands it — and build the pipelines that decode, validate, and move the rest.
Time-series storage & data layer
A store built for high-volume sensor data — time-series, fast to write, cheap to retain — so a year of readings stays queryable without crushing cost.
Telemetry dashboards & alerting
Live device data turned into operational dashboards and rules-based alerts — so the exception reaches the right person in seconds, not buried in raw data.
Fleet & device management
Device registry, health monitoring, configuration, and over-the-air updates — with staged, reversible rollouts — so a fleet of thousands stays current and a bad update can't take it down.
Security, provisioning & integration
Secure device identity and provisioning, data encrypted end to end, and integration with your existing systems (ERP, asset management, analytics) through governed interfaces.
AIoT — putting models on the data
Where it pays, we add the ML layer — anomaly detection, predictive maintenance, forecasting — tying the platform to our machine-learning and MLOps work so models run on live device data.
What you get — all assigned to you under full work-for-hire IP
How it runs
How an IoT development engagement runs.
The same delivery model behind all our software and AI work, tuned for connected devices — one accountable lead, fixed scope, no handoffs.
STEP 01
Connect
Scope the fleet, the protocols, the data each device emits, and the operations the platform must drive.
Output: an architecture & the success metrics
STEP 02
Ingest
Build the connectivity and pipeline layer, validated against simulated device load so it holds at full fleet scale before a real device depends on it.
Output: a layer that survives the peak
STEP 03
Operate
Stand up the dashboards, alerting, and fleet management in your own cloud tenant, wired to your systems through governed interfaces.
Output: a platform your team watches and runs
STEP 04
Act
Turn telemetry into action: rules, then where it pays, predictive models — measured weekly, your team trained to operate and extend it.
Output: a production platform & a team that owns it
Straight talk
The reliability discipline an always-on IoT platform actually needs.
An IoT platform runs unattended, in the field, around the clock — so the question that decides whether it's worth building isn't "can it ingest data?" but "will it stay correct and online when no one is watching?" That production discipline is what we're known for.
The same process that holds a 200+ location restaurant business at twice-a-week releases with zero critical defects over four years (BJ's Restaurants) is what a telemetry platform needs at 3 a.m.: load-tested before launch, staged and reversible rollouts, monitored after — exactly what an over-the-air firmware update across a fleet demands.
We'll tell you plainly which parts of an IoT initiative are worth building and which aren't — including when the honest answer is that you don't need a custom platform at all.
On longevity: a production platform we've run continuously since 2012 — now used by USC, the LA Rams, and MLB and MLS teams — carried through 12+ years of re-platforming without going offline (Bridge Athletic). Neither is an IoT engagement; they're the production-reliability track record we'd apply to one. Silicon Prime is a Stanford-rooted Responsible AI lab, founded 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement.
Why build your IoT platform with us.
We build software, and we say so. No hardware upsell, no firmware we don't write — we build the platform layer and integrate cleanly with your device and electronics partners. The boundary is the honesty.
Production reliability is the whole game. An IoT platform is judged on uptime and correctness when unattended; the release-and-rollout discipline behind a zero-critical-defect track record is what we bring to it.
Cloud- and platform-agnostic. We build on AWS IoT, Azure IoT, or open-source components based on your fleet and your cloud — not a partnership quota. The recommendation follows your workload.
Built to transfer. The platform, pipelines, dashboards, and code are assigned to you under full work-for-hire IP, and your team is trained to run and extend it when we step back.
Where it lands first
Where IoT development earns its keep first.
Manufacturing & industrial
Equipment monitoring and predictive maintenance on production lines, where acting on sensor data before failure is the documented payoff.
Manufacturing software →Energy & utilities
Meter, grid, and environmental telemetry turned into consumption analytics and fault detection across distributed assets.
Energy software →Logistics & supply chain
Location and condition tracking for goods in transit, so a cold-chain or chain-of-custody problem is caught while it's still fixable.
Logistics software →Connected products
The cloud backend, app, and device APIs behind a consumer or commercial connected product, with the support visibility to operate it.
Product engineering →Questions buyers ask before they build.
Thirty minutes · no pitch deck
Ready to make your device data actually do something?
Bring the fleet and the operation you're trying to improve — we'll tell you honestly what the software platform takes to build, where the edge-versus-cloud line should fall, and what it costs to run.