Turn device telemetry into decisions, not just a data stream you store.
We build the software that makes connected devices useful: ingestion that takes in telemetry at scale, edge-to-cloud pipelines that move and clean it, dashboards and alerts your team acts on, and fleet management that keeps thousands of devices healthy.
Built inside your own cloud, with full IP assignment. One accountable lead, fixed scope, steady-state in 4–8 weeks. We build the IoT software — not the hardware or firmware.
Because the hard part was never connecting the device — it was the software underneath. Sensors get installed, telemetry starts flowing, and it lands in a store nobody queries. The 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instead of decisions; an alert that should fire in seconds surfaces in a weekly report; a firmware fleet drifts out of date because there’s no safe way to push updates.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 is 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.
IoT software isn’t one product; it earns its keep in a handful of specific, high-volume operations. For each: what it does, the benefit it produces, and a one-line illustration of the help.
Ingests sensor data — vibration, temperature, current, pressure — from production lines and machinery, and flags the patterns that precede a failure. Benefit — fewer unplanned breakdowns and lower maintenance cost, because you service equipment before it fails instead of after it stops the line.
Example: a motor’s vibration signature starts trending toward a known failure mode and the platform raises a work order while the line is still running — turning an unplanned shutdown into a scheduled ten-minute swap.
Tracks the health, connectivity, configuration, and firmware version of every device in the field, and pushes updates safely across the fleet. Benefit — thousands of devices stay healthy and current without a truck roll, and a bad rollout is caught before it bricks the fleet.
Example: a security patch ships to 10,000 field gateways in a staged rollout that halts automatically when the first batch reports errors — instead of a manual update that misses half the fleet.
Turns live device data into operational dashboards and threshold alerts the team watches in real time. Benefit — faster response and less time spent chasing readings, because the exception finds the operator instead of the operator hunting through raw data.
Example: a cold-storage unit drifts above its safe temperature and an alert reaches the on-call tech in seconds — saving the inventory a once-a-day manual check would have lost.
Builds the cloud backend, mobile/web app, and device APIs behind a connected product so customers can control it and you can support it. Benefit — a product customers can actually use remotely, and a support team that can see what’s happening, instead of a device that’s “smart” only on the box.
Example: a customer adjusts a connected appliance from their phone and, when they call support, the agent sees the same live state — so the issue is diagnosed in one call.
Ingests meter, grid, and environmental data and turns it into consumption analytics, anomaly detection, and demand signals. Benefit — visibility into usage and faster fault detection across distributed assets, instead of waiting for a customer to report an outage.
Example: a sudden consumption drop across a feeder is flagged as a likely fault the moment it happens, instead of being noticed when complaints come in.
Tracks location, condition, and chain-of-custody telemetry across goods in transit. Benefit — fewer losses and a verifiable record of condition, because a problem in transit is visible while it can still be fixed.
Example: a refrigerated shipment that warms past its threshold triggers an alert en route — so the load is rerouted or salvaged instead of written off on arrival.
We build the IoT software platform: the layer that sits above the device and below the decision. We do not design hardware or write firmware — if you need a sensor designed or a microcontroller programmed, that’s an electronics partner’s job, and we’ll work alongside them. And the layer above us — the AI agents and models that reason over the data — is our Physical AI work, a separate engagement.
We stand up the ingestion layer that takes in telemetry from your devices over the protocols they actually speak (MQTT, CoAP, HTTP, and the gateway in between) — built to hold up when the whole fleet reports at once, not just in a demo.
We decide what runs at the edge versus the cloud — filtering, aggregating, and acting on data near the device where latency or bandwidth demands it — and build the pipelines that decode, validate, and move the rest. This is the IoT-specific layer beneath your enterprise data platform.
We model and build the store that’s right for high-volume sensor data — time-series, fast to write, cheap to retain — so a year of readings stays queryable without crushing cost.
We turn live device data into operational dashboards and rules-based alerts, so the exception reaches the right person in seconds instead of hiding in raw data.
We build the device registry, health monitoring, configuration, and over-the-air update mechanism — including staged, reversible firmware rollouts — so a fleet of thousands stays current and a bad update doesn’t take it down.
We handle device identity and provisioning, encrypt data in transit and at rest, and integrate the platform with your existing systems (ERP, asset management, analytics) through governed, permissioned interfaces.
Where it pays, we add the analytics and machine-learning layer on top — anomaly detection, predictive maintenance, forecasting — connecting the telemetry platform to our machine learning and MLOps work so models run on live device data.
What you get when you hire us — all assigned to you
The same delivery model behind all our software and AI work, tuned for connected devices — one accountable lead, fixed scope, no handoffs.
Scope the fleet, the protocols, the data each device emits, and the operations the platform must drive.
Output: an architecture & the success metrics we’ll be judged on
Build the connectivity and pipeline layer, validated against simulated device load so it holds at full fleet scale before a single real device depends on it.
Output: an ingestion-and-storage layer that survives the peak
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
Turn telemetry into action: rules, then, where it pays, predictive models — measured weekly, your team trained to operate and extend it.
Output: a production IoT platform & a team that owns it
Most engagements reach production in 4–8 weeks, with full work-for-hire IP assignment signed at kickoff.
An IoT platform runs unattended, in the field, around the clock — so the question that decides whether it’s worth building is not “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 — software-critical, multi-site operations — at twice-a-week releases with zero critical defects across four years is the discipline we bring to a telemetry platform that has to be right at 3 a.m.: load-tested before launch, staged and reversible rollouts, monitoring after (BJ’s Restaurants). The instinct to push every change behind tests and a safe rollout is exactly what an over-the-air firmware update across a fleet demands.
(Adjacent, for the long-haul question: a data-driven production platform we’ve operated continuously since 2012 — now used by USC, the LA Rams, and MLB and MLS teams — has been carried through 12+ years of re-platforming and modernization without going offline, the same staying-power an IoT deployment is signed up for: Bridge Athletic. Neither of these is an IoT/telemetry engagement; they’re the production-reliability and longevity track record we’d apply to one.)
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement. 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.
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.
Engine- 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.
Equipment monitoring and predictive maintenance on production lines, where acting on sensor data before failure is the documented payoff. Manufacturing software →
Meter, grid, and environmental telemetry turned into consumption analytics and fault detection across distributed assets.
Location and condition tracking for goods in transit, so a cold-chain or chain-of-custody problem is caught while it’s still fixable.
The cloud backend, app, and device APIs behind a consumer or commercial connected product, with the support visibility to operate it.
What teams want to know before they commit to building an IoT platform.
No — we build the IoT software platform: ingestion, edge-to-cloud pipelines, storage, dashboards, alerting, and fleet management. We don’t design sensors or write device firmware. If your project needs hardware, we work alongside your electronics or firmware partner and own the software side cleanly. Drawing that line up front is why the integration goes smoothly rather than turning into a finger-pointing exercise later.
IoT development is the connectivity and data layer — getting telemetry off devices, moving it, storing it, and showing it. Physical AI is the decision/intelligence layer that reasons over that data and decides what to act on in the physical world. IoT is the pipes; Physical AI is the brain on top of them. Many projects need both, in that order — you can’t put intelligence on data you haven’t reliably captured yet — but they’re scoped and built as distinct engagements.
Data engineering builds your general enterprise data layer — pipelines, warehouse, BI — across all your business systems. IoT development is the device-and-telemetry-specific layer that feeds it: ingestion built for high-volume sensor streams, edge processing, time-series storage, and fleet management. We often build the IoT layer to land clean telemetry into the broader data platform, but the device side has its own engineering, which is what this service covers.
That’s the part we engineer for first. We validate the ingestion and pipeline layer against simulated device load — the whole fleet reporting at peak — before a single real device depends on it, because the failure mode of IoT platforms is almost always the moment the fleet grows past what a demo handled. Scale is a design input from day one, not a problem you discover in production.
Each device gets a managed identity and is provisioned securely; data is encrypted in transit and at rest; and over-the-air updates ship as staged, reversible rollouts that halt automatically if the first batch reports errors — so a bad update can’t take down the fleet. Every engagement starts with an NDA and a security review, and we document every data path so your team verifies rather than trusts.
You do — completely. The platform, pipelines, dashboards, and code transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to operate and extend it. Keep us on a reduced retainer or take the keys; the engagement is built around the handover.
Most IoT platforms reach production in 4–8 weeks under a fixed-scope engagement with one accountable lead. Build cost depends on scope — fleet size, protocols, edge requirements — and our AI development cost guide gives real ranges. Run cost is mostly cloud ingestion and storage economics, which we model before building so the first invoice is a forecast you’ve already seen.
Thirty minutes · No pitch deck
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.