IoT App Development Company

IoT projects stall in one of two places: at the hardware boundary, where the device sends data but there is no reliable pipeline to receive it, or at the dashboard layer, where data arrives but nobody built the alerting, the context, or the view that tells an operator what to do.
RaftLabs builds the software layer: device integration, real-time data pipelines, monitoring dashboards, alert engines, and the ops tools your team actually uses. Industrial equipment monitoring, connected asset tracking, smart building systems, energy metering. We handle everything from the MQTT broker to the React dashboard, with the infrastructure to scale.

See our work

Real-time device dashboards with sub-second latency, built on MQTT, WebSockets, and time-series databases
Alert and threshold logic with email, SMS, and in-app notifications when a sensor crosses a limit or a device goes offline
End-to-end data pipeline from device to cloud to dashboard: AWS IoT Core, Azure IoT Hub, or self-hosted MQTT broker
Remote device management with firmware update triggers, device provisioning, and fleet health views

Recent outcomes

Voice AI · Research

Text-based interviews converted to automated phone calls

6× deeper insights

AI Automation · Ops

Manual invoice OCR across 40+ gas stations

20k+ txns day one

Loyalty · Retail

SuperValu & Centra loyalty platform with receipt validation

1,062 users in 4 weeks

SaaS · Logistics

Multi-carrier shipping hub for Indonesian eCommerce

2,000+ shipments yr 1

4.9 / 5 on ClutchSee all work

Recognition

Sound familiar?

Hardware ships next month but there is no software layer to receive, store, or display what it sends?
Device data arriving in a time-series store but no dashboard your ops team can read without SQL?
Alert logic living in spreadsheets because nothing was built to fire notifications when a sensor reads out of range?

In short

RaftLabs is an IoT app development company that builds custom software platforms for connected device deployments. We handle end-to-end IoT software: MQTT and WebSocket data pipelines, time-series database integration, real-time monitoring dashboards, threshold alert engines, remote device management, and the ops tools that make raw sensor data actionable. We work across industrial monitoring, smart building systems, logistics fleet tracking, retail analytics, and energy metering applications. Most IoT software projects run 10-16 weeks at a fixed cost with full source code ownership transferred to the client.

Trusted by

The hardware sends data. The software turns it into decisions.

Most IoT projects have the hard part figured out before they call us. The sensors are chosen. The hardware design is done. The devices will ship. The part that stalls is the software side: where does the data go, who sees it, what happens when something goes wrong, and how does an operations team manage hundreds of devices without a database admin on every shift.

We build the software layer that sits between the device and the person responsible for responding to what it sends.

Capabilities

What we build

Real-time monitoring dashboards

React dashboards that display live device data with sub-second latency, built over WebSocket connections so the view updates as readings arrive rather than on a polling interval. Time-series charts for continuous sensor streams (temperature, pressure, humidity, power draw, flow rate), gauge and indicator panels for discrete state (on or off, open or closed, in range or out), and map views for geographically distributed assets.

Dashboard layout is built around what an operator needs to see at a glance: current device status, active alerts, and the readings that require attention, without noise from healthy devices operating inside their thresholds. Role-based views so a plant manager sees fleet-level health and a technician sees the individual device they are responsible for, both from the same underlying data.

Data resolution configurable per view: high-frequency raw data for live monitoring, down-sampled aggregates for trend views, configurable lookback windows from 1 hour to 12 months. All chart interactions (pan, zoom, cursor scrub) operate on the time-series data without round-tripping to the server, so the view feels instant even when the dataset is large.

MQTT and data pipeline infrastructure

End-to-end data pipeline from device to storage to dashboard. MQTT broker deployment (AWS IoT Core for managed cloud scale, Mosquitto for on-premises or private cloud) with TLS 1.3 for transport security, device certificate authentication, and per-device topic namespacing so a compromised device cannot publish to another device's data channel.

Ingestion service that validates, transforms, and writes each message to time-series storage within 100ms of receipt. Validation catches bad packets at the boundary before they reach the database: missing fields, out-of-range values, malformed payloads, and duplicate messages from devices that retry on network interruption. Transformation normalises units, applies calibration offsets, and enriches each reading with the device metadata needed downstream.

Storage layer matched to the data profile: InfluxDB or TimescaleDB for high-frequency sensor writes (both handle millions of data points per day without manual partitioning), PostgreSQL for device registry, user accounts, and alert configuration. Redis for the last-known-value cache so dashboards load instantly without querying the time-series database for current state on every page open.

Alert and threshold engine

Configurable alert rules evaluated in real time against every incoming reading. Threshold alerts (reading above X or below Y), rate-of-change alerts (reading changing faster than Z over N seconds), and absence alerts (no reading received within a configurable window). Alert suppression during known maintenance windows so planned downtime does not generate noise.

Multi-channel notification delivery: email with the device name, reading value, threshold, and a direct link to the dashboard view for the affected device. SMS for alerts that require immediate response. In-app notifications in the dashboard for operators who are already logged in. Slack or Teams webhook integration for teams that work from those tools. Escalation rules that page a second recipient if the first does not acknowledge within a configurable time.

Alert history with acknowledgement tracking: who received it, when they acknowledged it, and what action they logged against it. Useful for compliance reporting and for identifying devices or locations that generate repeated alerts, which usually signals a device configuration issue or an environment outside the sensor's rated range.

Device fleet management

Device registry that records every device in the fleet: serial number, firmware version, deployment location, assigned alert rules, calibration parameters, and the full reading and alert history for each unit. Search and filter across the fleet by location, device type, firmware version, or alert status so a technician can find the 14 devices running old firmware or the 6 devices with open alerts without manually scanning a list.

Provisioning flow for onboarding new devices: certificate generation and signing, topic namespace assignment, and first-communication confirmation. Deprovisioning flow for retiring or replacing devices: certificate revocation, topic namespace cleanup, and archival of historical data. Both flows available via the admin UI for non-technical operations staff and via the management API for automated provisioning at scale.

OTA firmware update triggers for device types that support remote update over MQTT. The platform sends the update command and tracks acknowledgement, download, installation, and reboot events from each device so you have a confirmed fleet firmware version status rather than an assumed one. Rollback trigger available if a firmware update causes a device to go into an error state.

Historical analysis and reporting

Time-series query interface for looking back at device data across configurable windows (last hour, last 24 hours, last 7 days, custom range). Aggregate views: min, max, mean, and percentile summaries per device and per site for the selected window. Anomaly review: all readings that triggered an alert over a time period, with the values, the thresholds at the time, and the acknowledgement history.

Scheduled reports delivered by email: daily site summary, weekly fleet health report, monthly energy usage report (for energy monitoring deployments). Reports generated from the same data as the dashboard, not a separate data export, so the numbers match.

Data export in CSV and JSON for teams that want to run their own analysis in Excel, Python, or a BI tool. Export filtered by device, date range, and reading type. Bulk export available for compliance audits or for migrating data to a new system.

Edge computing and on-premises deployments

Edge node software for deployments where sending every reading to the cloud is too slow (latency-sensitive control applications), too expensive (high-frequency sensors on metered connections), or not permitted (data residency requirements that prohibit cloud transmission). The edge node runs locally on an industrial PC or gateway, processes readings against alert rules at the source, stores a local buffer, and syncs summaries or flagged events to the cloud on schedule.

On-premises deployment option for the full platform stack (MQTT broker, ingestion service, time-series database, dashboard) in a customer-managed environment. Docker Compose or Kubernetes manifests, documented deployment runbook, and a migration path to cloud if requirements change later.

Hybrid architecture for deployments that combine both: edge processing at the site for low-latency alerting and local buffering during network interruptions, with cloud aggregation for fleet-wide views, historical analysis, and notifications. The edge and cloud layers sync automatically when connectivity is restored after a dropout so no readings are lost.

Related services

Tell us what the devices send. We will build the software that makes it useful.

Device type, data frequency, who needs to see what, and what should trigger an alert. We scope it in a week and give you a fixed cost.

Talk about your IoT project

Frequently asked questions

: The hardware half (sensors, actuators, device firmware) is usually already scoped or handled by a hardware team. The software half is what we build. The data pipeline that receives what the devices send. The database that stores it at scale. The real-time views that show what is happening now. The alert engine that tells the right person when something is wrong. Historical analysis that shows trends over time. The admin layer for managing the device fleet. A typical IoT software stack has five layers. Connectivity (MQTT broker, WebSocket server, or HTTP ingestion endpoint depending on device constraints). Ingestion and storage (time-series databases like InfluxDB, TimescaleDB, or Amazon Timestream for sensor data, PostgreSQL for device registry and user data). Real-time dashboard (React frontend pulling live data via WebSockets). Alerting (threshold logic, escalation rules, notification delivery via email, SMS, or Slack). Device management (provisioning, health monitoring, OTA firmware triggers). We scope which layers need to be built, which can be handled by managed services like AWS IoT Core or Azure IoT Hub, and which already exist in your infrastructure, then price the work that remains.
: MQTT is the dominant protocol for battery-constrained devices and unreliable networks because of its low overhead and publish-subscribe model. We deploy Mosquitto or AWS IoT Core as the broker depending on scale, with QoS level 1 or 2 for message delivery guarantees when data loss is unacceptable (industrial sensors, energy metering). WebSockets work well for devices that already run a full TCP stack (gateways, embedded Linux boards, PLCs with Ethernet), where the bidirectional channel enables both data ingestion and device control from the dashboard. HTTP is fine for devices with reliable connectivity that send data on a schedule rather than continuously. CoAP and AMQP are supported when the existing device fleet already uses them. If you are in the hardware design phase, we give you a recommendation on protocol choice before the first board spins, because changing the protocol after deployment means updating every device in the field.
: Offline detection uses heartbeat messages. Each device sends a ping on a defined interval, and the platform marks it offline and triggers an alert if the heartbeat misses. The alert delay is configurable per device type (a factory floor sensor and a remote water pump warrant different thresholds). Bad data handling has two layers. Validation at ingestion rejects messages that are structurally invalid (missing required fields, wrong data type) or statistically impossible (a temperature sensor reading 1,000 degrees). Rejected messages are logged with a reason code so the device team can diagnose firmware issues. Anomaly detection at the analysis layer flags readings that are structurally valid but out of expected range for the device's operational context. A humidity sensor reading 95% when all surrounding devices read 55% is flagged for review rather than silently treated as real. Both layers are configurable without a code deploy so your team can tune thresholds as the deployment matures.
: AWS IoT Core for managed MQTT brokers at scale (handles device authentication, message routing, and integration with Lambda, Kinesis, and S3 without running your own broker). Self-hosted Mosquitto when the deployment needs to run on-premises or in a private cloud for data residency or latency reasons. InfluxDB or TimescaleDB for time-series storage (InfluxDB for pure sensor data at high write frequency, TimescaleDB when the data model mixes time-series sensor readings with relational context like device registry or user accounts). Redis for real-time state (last known value per device, online or offline status) served to the dashboard without a database query on every WebSocket event. The frontend is React with purpose-built charting for the data density that real-time dashboards need. We pick the stack based on device count, data frequency, connectivity environment, and data residency constraints for the deployment.
: Scope varies more than almost any other project type, so costs vary accordingly. A simple single-device-type monitoring dashboard with an MQTT ingestion pipeline, time-series storage, real-time chart view, and email alerting typically runs $25,000-$45,000. A multi-device-type platform with fleet management, threshold alert engine, historical reporting, and a device admin layer runs $45,000-$80,000. Enterprise IoT platforms with multi-tenant access (managing devices across multiple client accounts), OTA firmware update pipelines, edge computing integration, and compliance requirements start from $80,000. We give you a fixed cost after a one-week discovery that covers device count, data frequency, alert requirements, user roles, and the infrastructure environment (cloud, on-premises, or hybrid). The fixed cost covers build to production deployment and does not change unless scope changes, which you approve explicitly before any out-of-scope work begins.

Work with us

Tell us what you need. We'll tell you what it would take.

We scope IoT App Development Company in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.

Scope and cost agreed before work starts. No surprises. No obligation.
Working prototype within 3 weeks of kickoff.
Pay by milestone. You see progress before each invoice.
60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
All conversations are NDA-protected.