Industry
IoT Industry Software Development — Connected Products at Production Scale
End-to-end connected product platforms — firmware, gateways, edge compute, cloud ingestion at fleet scale, OTA, and the customer-facing apps and dashboards — built on AWS IoT / Azure IoT with Terraform-managed infrastructure.
The state of iot & connected devices
Global IoT market by 2030
Connected devices and platforms continue to expand across consumer, industrial, healthcare, and infrastructure segments.
Connected devices
Projected installed base by 2030 — fleet-scale telemetry, OTA, and identity are now baseline product requirements, not differentiators.
Edge compute share
Share of enterprise data processed at the edge instead of central cloud — a major architecture shift driven by latency, bandwidth, and privacy.
What we build for iot & connected devices teams
- Firmware engineering on ESP32, ESP32-S3, STM32, Nordic nRF, and embedded Linux platforms
- Gateway architecture with MQTT, OPC-UA, BLE, Wi-Fi, Matter, LoRaWAN, and cellular bridging
- Edge compute and edge ML on AWS Greengrass, Azure IoT Edge, or custom Linux-edge frameworks
- Cloud ingestion via AWS IoT Core, Azure IoT Hub, or self-hosted MQTT (EMQX, HiveMQ) at fleet scale
- Time-series stores: Amazon Timestream, InfluxDB, TimescaleDB — with retention tiering for fleet-scale data
- Signed firmware, OTA update systems with staged rollout (1% → 10% → 100%) and rollback on telemetry signals
- Device identity, certificate provisioning, and rotation (X.509, MTLS, AWS IoT JITP/JITR)
- Fleet ops dashboards: device health, connectivity, firmware versions, and remote diagnostics
- Real-time alerts and operational pipelines that turn telemetry into action — not just visualization
- Customer-facing dashboards built in Next.js with real-time updates over WebSockets or SSE
- Operations dashboards on Grafana with custom panels and per-tenant reporting
- Multi-tenant device platforms for connected hardware brands and integrators
- Predictive maintenance: anomaly detection, vibration / acoustic ML, and degradation modeling
- Compliance: FCC, CE, and where relevant FDA (for connected medical devices) — built into the dev process, not at the end
- NestJS API tier, Next.js apps, Terraform-managed AWS, GitHub Actions CI/CD — same disciplined foundation across the device platform
- Mobile companion apps in React Native or native iOS / Android with BLE provisioning
- Data export to customer-owned warehouses (Snowflake, BigQuery) for downstream analytics
- Migration from legacy cloud platforms (Particle, ThingWorx, custom MQTT) to modern, observable IoT stacks
Why DiveScale
Domain knowledge meets engineering rigor
IoT industry projects fail at the boundaries — firmware vs. gateway, gateway vs. cloud, prototype vs. fleet, telemetry vs. action. DiveScale ships across all of them so the boundaries are engineered, not assumed. The discipline is consistent: signed firmware, OTA that survives backhaul failure, time-series tiering that holds at fleet scale, and dashboards that drive decisions instead of decorating them.
We have built connected product platforms across consumer hardware, industrial IoT, connected medical devices, smart-home / smart-building, fleet telematics, and pet wearables. Fleet sizes from hundreds to hundreds of thousands. Firmware in C, C++, Rust, and embedded Linux; gateways on Raspberry Pi-class hardware up through industrial computers; cloud platforms on AWS IoT Core, Azure IoT Hub, and self-hosted MQTT.
Our cloud stack is the same disciplined foundation we apply everywhere: NestJS APIs with typed contracts; Next.js (App Router) for customer dashboards; Terraform-managed AWS with multi-region awareness for global fleets; AWS IoT Core or Azure IoT Hub for device-side, with EventBridge or Service Bus carrying events into the application layer; GitHub Actions CI/CD with OIDC into cloud accounts.
OTA is treated as a production release — not a feature checkbox. Signed firmware, staged rollouts (1% canary → 10% beta → 100% GA), rollback triggers driven by real telemetry, kill-switch for emergent issues, and per-device version visibility. We have done OTA at fleet scales where a bad release would have been an existential incident, and we engineer accordingly.
Edge ML is increasingly part of every serious IoT build. Vision, anomaly detection, predictive maintenance, low-latency control — handled by quantized models running on ESP32-S3, ARM Cortex-M, Coral, Jetson, or Linux edge nodes. Cloud handles retraining; edge handles inference. We benchmark per workload before committing to silicon.
And we are honest about what IoT cannot do. Not every product needs cloud connectivity; not every dashboard surfaces useful signal; not every problem benefits from being made 'smart'. We design for the value the device delivers — not for the metaphor of being 'connected'. That honesty is why our IoT clients keep us around past the prototype phase.
IoT & Connected Devices solutions we deliver
How we deliver
Our iot & connected devices delivery process
- 01
End-to-end scope
Device, gateway, cloud, app, dashboards, and operations — designed together. IoT projects fail when one piece is designed in isolation.
- 02
Prototype the seams
Real device → real backhaul → real dashboard within the first milestone. We prove the system end-to-end before adding feature surface.
- 03
Firmware + gateway hardening
Secure boot, signed firmware, certificate provisioning, watchdogs, recovery paths, and a serial / OTA debug path that survives field deployment.
- 04
Cloud platform at fleet scale
AWS IoT Core or Azure IoT Hub design, MQTT broker scaling, time-series tiering, EventBridge / Service Bus eventing, and the operational dashboards behind it.
- 05
OTA + fleet ops
Staged-rollout OTA, kill-switches, per-device version visibility, and remote diagnostics — productized so non-engineers can manage releases safely.
- 06
Pilot deployment
Field-pilot a small fleet, instrument heavily, fix what the field surfaces (it always surfaces something), then scale.
- 07
Operate, observe, evolve
Fleet ops, firmware release cadence, telemetry-driven product decisions, and a roadmap that uses the data the platform now produces.
Technologies we deploy for iot & connected devices
AWS
AWS architecture, migration, and platform engineering — multi-account governance, well-architected workloads, Terraform IaC, and the operational discipline production demands.
Learn moreMicrosoft Azure
Azure architecture, App Service, AKS, Functions, and Azure OpenAI — enterprise-grade builds for Microsoft-aligned organizations.
Learn moreAWS Lambda
Lambda function design, optimization, and operations — cold-start mitigation, IAM scoping, observability, and the architectures where serverless wins.
Learn morePython
Production Python engineering — FastAPI services, async pipelines, AI/ML workloads, data engineering at scale, and the typed, tested, observable discipline production Python deserves.
Learn moreNode.js
Production Node.js engineering — NestJS, Fastify, Hono, real-time systems, job queues, and the operational discipline that single-threaded runtimes demand.
Learn moreTypeScript
End-to-end typed engineering — React, Next.js, NestJS, Node, and shared schemas — with the discipline TypeScript was built for.
Learn moreReact
Production React engineering — Server Components, design systems, performance discipline, accessibility, and the build tooling modern apps deserve.
Learn moreNext.js
Production Next.js engineering — App Router, RSC, edge runtime, ISR, SEO-first metadata, and the deployment topology that fits your workload (Vercel or self-hosted).
Learn moreTerraform
Terraform engineering — module design, state strategy, multi-account governance, policy-as-code, drift detection, and CI-driven plan / apply for multi-cloud estates.
Learn moreDocker
Production Docker engineering — small images, multi-stage builds, BuildKit caching, security scanning, and the operational discipline containers deserve.
Learn moreKubernetes
Production Kubernetes engineering — cluster design, GitOps, observability, CIS hardening, multi-tenancy, internal developer platforms, and the day-2 operations the demos skip.
Learn moreBig Data
Production big data engineering at real scale — managing trillions of rows with millisecond query times, custom sharding strategies, ETL pipelines, and lakehouse architectures on Spark, dbt, Iceberg, Snowflake, and BigQuery.
Learn moreMLOps
MLOps platform engineering — pipelines, model registries, evaluation, monitoring, and incident response for ML and LLM systems.
Learn moreSnowflake
Snowflake data engineering — warehouse design, performance, governance, and the Snowpark/Cortex stack for analytics and AI.
Learn moreIoT & Connected Devices — Frequently Asked Questions
Yes — ESP32, ESP32-S3, STM32, Nordic nRF, and Linux-based devices. We pair firmware, gateway, cloud, and app engineers so the system is designed coherently. The most expensive IoT bugs are seam bugs; we engineer the seams.
