Emerging Tech
IoT
We build the software side of IoT — ingestion, platforms, device management and dashboards — for teams who need their devices to produce data they can actually act on.
Overview
The Internet of Things means connecting physical devices — sensors, meters, machines, vehicles, products in the field — to software, so they can report data and, often, be controlled remotely. That is the whole promise: a pump tells you its vibration is climbing before it fails, a building reports its energy use room by room, a shipping container reports its location and temperature across an ocean. The hard part is almost never the sensor. It is everything between the sensor and a decision: getting readings off thousands of intermittently-connected devices, storing enormous volumes of time-stamped data affordably, turning it into something a person or another system can use, and keeping the whole fleet secure and updatable for years.
That software side is where we work. We do not manufacture hardware or design circuit boards. We build the ingestion pipelines that receive device messages, the platforms that manage a fleet — provisioning new devices, pushing firmware updates over the air, monitoring which devices are healthy and which have gone dark — the time-series storage and stream processing that handle the data volume, and the dashboards and integrations that put device data in front of people and inside your existing business systems. Most IoT projects that stall do so on this layer, not on the electronics, and it is the layer that determines whether the deployment is useful or just a pile of readings nobody trusts.
The IoT architecture is layered, and each layer is a real engineering decision: devices and sensors at the edge; a connectivity choice (cellular, LoRaWAN, Wi-Fi or Bluetooth Low Energy) carrying messages over a protocol such as MQTT; optional edge computing that processes data near the device to cut latency and bandwidth; an ingestion and messaging tier; time-series data storage; stream processing and analytics; dashboards; and a device-management plane running underneath all of it. We assemble these deliberately, usually on a cloud IoT platform such as AWS IoT or Azure IoT, and we are blunt about the two realities that sink naive projects — IoT security is a serious, well-documented weakness, and the data volume is larger and more expensive to handle than teams expect.
Best for — Organisations with physical assets or connected products that need reliable ingestion, secure fleet management, and data they can trust — built by people who take IoT’s security and data-scale problems seriously.
Why teams choose IoT
Data you can actually act on
A pipeline that turns raw device messages into clean, queryable, time-stamped data — with the gaps, duplicates and out-of-order readings that field devices always produce handled honestly, so what reaches your dashboards and alerts is trustworthy rather than noise.
A fleet you can operate for years
Provisioning, over-the-air firmware updates, health monitoring and remote configuration built in from the start, so a growing fleet stays maintainable and secure across the long device lifecycles IoT deployments live with — not something that becomes unmanageable at a few hundred units.
Devices joined to your business
Device data does not sit in an isolated portal. We integrate it with the systems that run your operation — maintenance scheduling, ERP, alerting, analytics — so a reading in the field triggers the work order, the reorder, or the notification it should.
Why businesses choose IoT
- You have devices producing data — or soon will — and need the software to make that data reliable, secure and useful rather than a portal nobody trusts.
- You want IoT’s security and data-scale problems treated as first-order design decisions, by engineers who will say plainly when a deployment is not safe to ship.
- You need device data inside your existing business systems, not stranded in an isolated vendor dashboard.
- You value senior judgement on the layered architecture — where to put edge processing, which connectivity and protocol fit, how to store time-series data affordably — made once, up front, by people who will operate the result.
What we build with IoT
The capabilities this technology is genuinely strong at — and what we most often build with it.
Ingestion and messaging pipelines
We build the tier that receives device messages at volume — typically over MQTT — validates and de-duplicates them, and feeds them into storage and stream processing. This is the load-bearing layer of any IoT system, and we design it to absorb bursts, reconnecting fleets, and the malformed messages real devices send.
Device management and OTA updates
Provisioning devices with unique identities, pushing firmware over the air in controlled, staged rollouts that can be paused and rolled back, remote configuration, and fleet health monitoring so you know which devices are reporting and which have gone dark. This is what keeps a fleet secure and maintainable across years.
Time-series storage and retention
Device data is time-stamped and relentless, and general-purpose databases handle it badly at scale. We use time-series stores and deliberate retention and downsampling policies — full resolution recently, aggregated further back — so queries stay fast and storage costs stay sane.
Edge computing and filtering
Where latency or bandwidth matters, we push processing to the device or a local gateway: filtering, aggregating, and acting on readings near the source, so only what matters travels to the cloud. This cuts cost, cuts latency for control loops, and keeps the system working when the connection does not.
Stream processing and alerting
Readings are evaluated as they arrive — thresholds, anomaly detection, rate-of-change rules — so a rising temperature or a stalled sensor raises an alert in seconds, not after a batch job. We build the streaming layer that turns a flood of messages into a small number of things worth a human’s attention.
Dashboards, digital twins and integration
The output layer: live dashboards and fleet views, digital twins that mirror a device or asset’s current and modelled state, and the integrations that push device data into maintenance systems, ERPs and analytics — so device state drives action across the business rather than sitting in a screen someone occasionally opens.
Use cases
Industrial monitoring and predictive maintenance
Machines and equipment reporting vibration, temperature and load, with stream processing that flags the early signature of a failure — so maintenance happens before the breakdown, not after it, and unplanned downtime falls.
Smart buildings and energy
Occupancy, temperature, air quality and energy sensors across a building or estate, feeding dashboards and controls that cut consumption and surface where energy is actually being spent, room by room and hour by hour.
Asset tracking and logistics
Location and condition (temperature, shock, humidity) reported from vehicles, containers and shipments over cellular or LoRaWAN, integrated with logistics systems so a cold-chain excursion or a delayed asset triggers a response automatically.
Connected products and environmental sensing
Products in customers’ hands reporting usage and health, or distributed environmental sensors measuring air, water and conditions across a wide area — both needing the same reliable ingestion, fleet management and analytics, at the scale of a whole deployed base.
When IoT is the right choice
- Right when you have physical assets or products whose real-world state — temperature, location, usage, vibration, energy, occupancy — is worth knowing continuously, and a decision or an alert genuinely depends on that data.
- Right when you already have devices (or a hardware partner producing them) and need the software to receive, store, manage and make sense of what they send — the ingestion, the fleet management, the dashboards, and the integration into your existing systems.
- Right when the value is in scale or continuity: hundreds or thousands of devices, or a handful reporting constantly over years, where manual reading or spreadsheets have stopped being viable.
- Wrong for a problem that a single reading or an occasional manual check already answers. If you do not act differently because data arrives every minute rather than every month, a connected device is cost and attack surface you do not need.
- Wrong for teams that treat security and device management as things to add later. Insecure, unmanageable fleets are the single most common way IoT projects become liabilities — if that cannot be designed in from the start, the project is not ready.
IoT: pros and cons
Strengths
- Continuous visibility of physical assets and environments that were previously invisible between manual checks — the basis for predictive maintenance, energy optimisation and real-time tracking.
- Mature cloud IoT platforms (AWS IoT, Azure IoT) provide device identity, secure messaging, fleet management and OTA-update primitives, so we build on proven foundations rather than inventing them.
- Edge computing lets you process and filter data near the device, cutting latency for time-critical control and dramatically reducing the bandwidth and cloud cost of shipping every raw reading.
- Once ingestion and storage are right, the same data feeds dashboards, alerting, analytics and digital twins — a single well-built pipeline serves many uses across the business.
Trade-offs
- Security is a serious, well-known weakness of IoT. Devices are often under-resourced, physically accessible, and rarely patched, which makes insecure fleets a major attack and botnet surface. This has to be designed in — device identity, encryption, least privilege, signed firmware — not bolted on, and it is real, ongoing work.
- Device and fleet management at scale is genuinely hard. Provisioning thousands of devices, rolling out over-the-air firmware updates without bricking units, and monitoring a fleet where some devices are always offline is a whole discipline, not a feature you switch on.
- Connectivity in the field is unreliable. Devices drop off, batteries die, cellular coverage fades, and messages arrive late, duplicated or out of order. The software must assume intermittency and handle offline behaviour and store-and-forward — assuming a clean connection is how deployments fail quietly.
- The data volume is large and the cost is easy to get wrong. Thousands of devices reporting frequently produce far more data than teams estimate; without deliberate edge filtering, retention policies and storage design, ingestion and storage bills climb fast and queries slow to a crawl.
Architecture
IoT architecture is layered, and each layer is a decision we make on purpose. At the edge sit the devices and sensors, connected over cellular, LoRaWAN, Wi-Fi or Bluetooth Low Energy — a choice driven by range, power budget, data rate and cost, not fashion. Messages typically travel over MQTT, a lightweight publish-subscribe protocol designed for constrained devices and unreliable networks. Where latency or bandwidth demands it, an edge-computing layer on the device or a local gateway processes and filters data before it leaves the site, so the cloud receives decisions and summaries rather than every raw reading.
In the cloud, an ingestion and messaging tier receives device traffic and hands it to durable storage and stream processing. We lean on managed IoT platforms — AWS IoT or Azure IoT — for the primitives that are genuinely hard to build well: device identity and authentication, secure messaging, and the device-management plane that handles provisioning, over-the-air updates and fleet monitoring. Time-series data lands in a store built for it; a streaming layer evaluates readings in flight for alerting; and dashboards, analytics and digital twins sit on top. Running underneath everything is device management and security, which are not a layer you add at the end but the spine the whole system hangs from.
Performance
Performance in IoT is really two problems: latency at the edge and throughput in the pipeline. When a device must react — a control loop, a safety cut-off, a local alarm — the round trip to the cloud and back is too slow and too fragile, so that logic belongs on the device or a nearby gateway. Edge processing is how you get millisecond responses and how the system keeps working when connectivity drops. We decide, per use case, what must run locally and what can wait for the cloud.
In the cloud the challenge is throughput and query speed under relentless, bursty load. A fleet reconnecting after an outage can hit the ingestion tier with a spike many times its steady-state volume, and it must absorb that without dropping messages. We size the pipeline for the peaks, not the average, filter and aggregate aggressively at the edge to reduce what has to be processed at all, and structure time-series storage with retention and downsampling so that dashboard queries stay fast years into a deployment rather than degrading as data accumulates.
Security
Security is the part of IoT that most deserves your attention and most often gets none. Devices are cheap, physically accessible, under-resourced and rarely patched, and history is full of fleets conscripted into botnets or used as a soft entry into a corporate network. We treat this as a design constraint from the first day, not a hardening pass at the end. Every device gets a unique cryptographic identity rather than a shared password; all traffic is encrypted in transit; each device is granted the least privilege it needs and no more, so a compromised unit cannot impersonate the fleet or reach systems it has no business touching.
Firmware is a first-class risk, so over-the-air updates are signed and verified, and the update mechanism itself is protected — an unauthenticated update channel is a remote-code-execution path to every device you own. On the platform side we lean on AWS IoT and Azure IoT for device authentication, certificate management and policy, because these are exactly the primitives that are dangerous to hand-roll. We are honest that security here is ongoing: long device lifecycles mean you are defending hardware for years, and a fleet that was secure at launch needs monitoring, revocation and patching to stay that way. If those realities cannot be resourced, we will say so before you deploy.
Scalability
IoT scales along two axes at once — more devices, and more data per device over time — and both have to be planned for from the start, because retrofitting scale into an ingestion pipeline that assumed hundreds of devices is a rewrite. We design the messaging and ingestion tier to grow horizontally, so adding devices adds capacity rather than adding fragility, and we lean on the managed platforms’ ability to handle device identity and connection at fleet scale rather than building a connection broker ourselves.
The quieter scaling problem is data and cost. A fleet that produces a trickle at pilot scale produces a torrent in production, and undisciplined ingestion turns that into a storage bill and query times that both climb without limit. We control it with edge filtering so raw noise never travels, retention and downsampling policies so old data shrinks gracefully, and storage chosen for time-series access patterns. Fleet management scales too: provisioning, monitoring and staged over-the-air rollouts have to work the same at ten thousand devices as at ten, which is why we build those capabilities in early rather than discovering their absence when the fleet has already grown.
IoT integrations & ecosystem
The technologies we most often pair with it — each links to how we work with it.
How we work
We start with the decisions that are cheap to make well early and painful to change later: the connectivity and protocol the devices will use, where the edge/cloud boundary sits, which cloud IoT platform fits, how time-series data will be stored and for how long, and — before anything else — how the fleet will be identified, secured and updated. These choices shape everything downstream, so we settle them with you up front rather than discovering them mid-build.
Then we build in thin, real slices: a small number of actual devices reporting through the full pipeline into a real dashboard, in a real environment, so the architecture meets field conditions — dropped connections, malformed messages, bursty reconnects — while the fleet is small enough to fix cheaply. The engineers who design the ingestion and device-management layers are the ones who write and operate them, so decisions are made by people who will live with the consequences. That is what we mean by operating what we build, and in IoT it matters most for the two things vendors gloss over: security and the true cost of the data.
The service behind it
Delivered throughIoT DevelopmentWhat we build with IoT
The disciplines this technology most often shows up in — from a first build to taking over and stabilising an existing one.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves — including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Industries we use IoT in
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
Why teams choose us for IoT
We take IoT security seriously
The most common way an IoT project becomes a liability is an insecure, unmanageable fleet. We design device identity, encryption, least privilege and signed updates in from the start, and we will tell you plainly if a deployment is not safe to ship.
We build the software, honestly about the hardware
We do not pretend to make devices. We build ingestion, platforms, data and dashboards, and integrate fleets with your business systems — the layer where most IoT projects actually stall — and we work with your hardware or hardware partner rather than around them.
We plan for the data and the cost
Fleets produce far more data than pilots suggest. We size pipelines for the peak, filter at the edge, and set retention and storage deliberately, so the deployment stays fast and affordable as it grows rather than becoming a runaway bill.
We operate what we build
The engineers who design your ingestion and device-management layers run them. That means decisions made for the multi-year reality of a fleet — long device lifecycles, ongoing patching, offline behaviour — not just a demo that works on a good connection.
Typical timeline
- 01
Discovery and architecture
One to two weeks agreeing connectivity and protocol, the edge/cloud split, platform choice, data storage and retention, and the security and device-management model — the load-bearing decisions, settled before build.
- 02
First end-to-end slice
A small set of real devices reporting through the full pipeline into a working dashboard, proving the architecture against real field conditions — dropped connections, bad messages, reconnect bursts — while it is cheap to change.
- 03
Fleet and platform build
Building out ingestion at volume, device provisioning and over-the-air updates, time-series storage, stream processing and alerting, and integration into your business systems, each piece shippable as it lands.
- 04
Hardening and handover
Security review of the fleet, load-testing ingestion against peak and reconnect scenarios, retention and cost tuning, and documentation so your team can operate and grow the deployment.
How pricing works
- Fixed-scope builds for a well-defined system — an ingestion pipeline, a device-management platform, a dashboard and integration layer — quoted once the fleet size, data volume and connectivity are understood.
- Monthly senior engagement for evolving IoT products, where the fleet grows and requirements shift, and you want continuity and someone who will operate the platform rather than a one-off deliverable.
- Assessments and rescues for existing IoT deployments — a security review of a fleet, a pipeline that cannot keep up with the data, or a stalled platform — priced by the scope of the review.
Hire IoT engineers
Need IoT capacity on your own team? We embed named senior engineers into your existing team — reporting to your leads, working in your rituals — so you add capacity without a hiring cycle.
Hire IoT engineersCommon questions
Do you make the hardware or the devices?
No. We build the software side of IoT — ingestion pipelines, device-management platforms, time-series storage, stream processing, dashboards, and the integrations that connect device data to your business systems. We work with the hardware you already have or with your hardware partner. That software layer is where most IoT projects actually stall, and it is where our senior engineers add the most value.
Why is IoT security such a big concern?
Because IoT devices are cheap, physically accessible, under-resourced and rarely patched, which makes an insecure fleet a serious attack and botnet surface — there is a long history of connected devices being conscripted into attacks or used to get into a corporate network. Security has to be designed in from the start: unique device identities, encryption in transit, least-privilege access, and signed over-the-air updates. Bolting it on afterwards does not work, and we will be honest with you about the ongoing effort it takes across a fleet’s long life.
What is edge computing and do we need it?
Edge computing means processing data on the device or a nearby gateway instead of sending every raw reading to the cloud. You need it when latency matters — a control loop or safety response cannot wait for a cloud round trip — or when bandwidth and cloud cost matter, because filtering and aggregating at the edge dramatically reduces how much data you ship and store. It also keeps the system working when connectivity drops. We decide, per use case, what belongs at the edge and what belongs in the cloud.
How do you handle devices that keep going offline?
We assume it, rather than hope against it. Field connectivity is unreliable — batteries die, cellular coverage fades, and messages arrive late, duplicated or out of order. So the software is built to tolerate intermittency: devices buffer and store-and-forward when offline, the ingestion tier de-duplicates and handles out-of-order readings, and fleet monitoring tells you which devices have genuinely gone dark. A system that assumes a clean, constant connection is a system that fails quietly in the field.
Should we build on AWS IoT or Azure IoT rather than from scratch?
Almost always, yes. The managed platforms provide exactly the primitives that are hard and dangerous to build yourself — device identity and authentication, secure messaging at fleet scale, certificate management, and the device-management plane for provisioning and over-the-air updates. We build on those foundations rather than reinventing them, and we choose between AWS IoT and Azure IoT based on where the rest of your systems live and which fits your data and integration needs, not on preference.
Building on IoT?
A technical conversation with the engineers who would do the work. If we are not the right fit, we will say so on the call.