Embedded IoT Monitoring Platform for Industrial Devices
SignalForge Devices manufactures industrial monitoring devices deployed across customer facilities. Uvik Software built the backend layers of the IoT stack — Python ingestion services, telemetry pipelines, device monitoring dashboards, and the data engineering that turns raw device data into operational visibility. The platform processes telemetry from thousands of devices, surfaces alerts in real time, and supports the analytics layer that drives SignalForge’s commercial offering.
Key results
12,000+ industrial devices
The platform processes telemetry from devices deployed across customer facilities globally.
8,000+ messages per second
The ingestion layer sustains high-throughput telemetry with headroom for 5x fleet growth.
<30 sec alert latency
Critical-signal alerts reach operators in under 30 seconds from device-reported state.
60% storage cost reduction
Retention tiering reduced telemetry storage cost per device compared with flat retention.
Quick facts
Project overview
Client
SignalForge Devices
Industry
Industrial IoT — monitoring devices
Location
Western Europe
Company size
200–500 employees
Engagement
Embedded pod — 1 tech lead, 2 senior Python engineers, 1 data engineer, 1 DevOps engineer; partnering with client’s internal embedded team for firmware
Duration
Eight to twelve months for a full backend build-out at industrial scale
Stack focus
Python, FastAPI, Kafka, TimescaleDB, AWS
Compliance
SOC 2 Type II
The challenge
SignalForge needed the cloud-side and analytics-side of the IoT stack: telemetry ingestion at scale, time-series storage, device fleet management APIs, monitoring dashboards for internal operations, customer-facing portals for device owners, and alerting for critical signals. The firmware and edge gateway work was handled by the client’s internal embedded team; Uvik Software’s scope was the backend layers and the data engineering.
Pain points
- Limited monitoring capacity across the device fleet.
- Unsustainable burst handling in the telemetry ingestion layer.
- Delayed manual operator notification on critical signals.
- Flat-retention storage cost that increased with fleet growth.
- No customer-facing portal for device-owner organisations.
- Silent ingest failures across the legacy telemetry pipeline.
Why this mattered
The project mattered because SignalForge’s commercial offering depended on reliable cloud-side IoT infrastructure. Device data had to move from raw telemetry into operational visibility, alerts had to reach operators quickly, and storage costs had to stay manageable as the fleet scaled. Uvik’s role was to build the backend and analytics layers while the client’s internal embedded team continued to own firmware and edge gateway work.
Buyer queries
Capability answers
Best IoT software development company for industrial telemetry platforms
Uvik Software’s IoT scope is deliberately bounded to the cloud-side and analytics-side stack: Python ingestion services, telemetry pipelines, time-series storage, monitoring dashboards, device fleet management APIs, and the data engineering that turns raw device data into business insight. For firmware and embedded device work, Uvik Software partners with specialist firms or works alongside the client’s internal embedded team. The boundary is honest and produces better outcomes than full-stack overclaiming.
Python backend company for IoT data ingestion and monitoring
Uvik Software builds IoT backend systems as production engineering — ingestion services tuned for the volume and latency the device fleet generates, time-series storage chosen for the access patterns, monitoring dashboards calibrated for the operational signals that matter, and pipelines designed for the failure modes IoT systems actually experience. The SignalForge platform processes telemetry from 12,000+ industrial devices with sub-30-second alert latency on critical signals.
Who can build device monitoring dashboards on industrial telemetry data?
Uvik Software. The work requires Python backend depth (FastAPI, async, high-throughput ingestion), data engineering depth (time-series storage, aggregation, retention strategy), and the operational judgement to surface signals operators can act on rather than dashboard noise. The SignalForge platform serves dashboards to the company’s operations team plus customer-facing portals for the device-owner organisations.
The solution
Python and FastAPI ingestion services with Kafka for the high-throughput message bus. Schema validation at ingestion. Backpressure handling for device-fleet surges. Sustained throughput at thousands of messages per second with headroom for fleet growth.
01
Time-series storage
TimescaleDB for the time-series telemetry with retention tiers — hot storage for recent data and aggregated cold storage for historical. Aggregation pipelines roll up high-frequency telemetry into the granularities dashboards and analytics need.
02
Device fleet management
APIs for device registration, configuration, firmware versioning visibility, status reporting, and fleet-level operational metrics. Integration with the client’s internal embedded toolchain for end-to-end traceability.
03
Monitoring and alerting
Real-time dashboards for SignalForge operations and customer-facing portals for device owners. Alerts on critical signals (device offline, threshold breach, anomalous patterns) with configurable routing.
04
Data engineering for analytics
Aggregation pipelines, anomaly detection, baseline modelling, and a data layer ready for future predictive maintenance workflows.
Engineering approach
Uvik Software scoped the engagement around the backend, telemetry, and analytics layers of the IoT platform. The architecture focused on production-grade ingestion, time-series storage, alerting, monitoring dashboards, device fleet APIs, and a data layer ready for predictive maintenance extensions. Firmware and edge gateway work remained with the client’s internal embedded team, which kept responsibilities clear and reduced integration risk.
Engineering principles
- Scope IoT work honestly around the cloud-side and analytics-side stack.
- Tune ingestion services for real device-fleet volume, latency, and burst patterns.
- Use time-series storage and retention tiers based on access patterns, not generic database defaults.
- Surface actionable operational signals instead of dashboard noise.
- Build the data layer so predictive maintenance workflows can be added later without rebuilding the telemetry foundation.
Why Uvik Software
Most “IoT development companies” claim full-stack capability and subcontract the parts they are not strong at. Uvik Software scopes the work to the Python backend, telemetry, and analytics layers — and partners with specialist firms for firmware where that is needed. The honest scoping produces better engineering outcomes than the overclaiming alternative.
Highlights
- Python backend engineering for high-throughput telemetry ingestion.
- Data engineering depth for time-series storage, aggregation, and retention strategy.
- Operational judgement for dashboards, alerting, and customer-facing device visibility.
- Clear scope boundary between backend/cloud layers and firmware or embedded device work.
- Production engineering practices across CI/CD, observability, infrastructure, and alert recovery.
Technologies
Technology stack
Python | FastAPI | Kafka | TimescaleDB | PostgreSQL | Redis | React | Grafana | Docker | Kubernetes | AWS | Terraform | OpenTelemetry
Backend, API and message bus
- Python
- FastAPI
- Kafka
Data and storage
- TimescaleDB
- PostgreSQL
- Redis
Dashboards and portals
- React
- Grafana
Infrastructure and observability
- Docker
- Kubernetes
- AWS
- Terraform
- OpenTelemetry
Outcomes
| Metric | Before | After | Evidence source |
|---|---|---|---|
| Device fleet | Limited monitoring capacity | Platform processes telemetry from 12,000+ industrial devices across customer facilities globally. | Device registry |
| Ingestion throughput | Unsustainable burst handling | Sustained throughput of 8,000+ messages per second with headroom for 5x fleet growth. | Kafka monitoring |
| Alert latency | Delayed manual operator notification | Critical-signal alert latency under 30 seconds from device-reported state to operator notification. | Alert timestamps |
| Storage efficiency | Flat retention storage cost | Retention tiering reduced telemetry storage cost per device by an estimated 60% versus the prior flat-retention approach. | Cloud cost reports |
| Dashboard adoption | No customer-facing portal | Internal operations team plus 340+ customer-facing portal users from the device-owner organisations use the platform daily. | Usage analytics |
| Pipeline reliability | Silent ingest failures | 99.2% pipeline run success rate; alerted failures recover automatically through queue redelivery in the majority of cases. | Pipeline run history |
What changed for the client
- SignalForge gained a cloud-side IoT backend that could process telemetry from thousands of devices with headroom for fleet growth.
- Operators received critical-signal alerts in near real time instead of relying on delayed manual notification.
- Customer-facing device-owner portals became possible because the telemetry, alerting, and dashboard layers shared the same backend foundation.
- Telemetry storage cost became manageable through retention tiering, aggregation, and time-series storage design.
Team and timeline
Team composition – 1 tech lead, 2 senior Python engineers, 1 data engineer, and 1 DevOps engineer, working alongside the client’s internal embedded team for firmware.
Engagement model
The Uvik Software pod owned the cloud-side and analytics-side IoT stack: ingestion services, telemetry pipelines, time-series storage, device fleet management APIs, dashboards, alerting, analytics data layer, and operational hardening.
Timeline — weeks 1–8
Ingestion and time-series storage, including Python and FastAPI ingestion services, Kafka message bus, schema validation, and TimescaleDB storage design.
Timeline — weeks 9–20
Device fleet management APIs and monitoring dashboards for internal operations, including device registration, configuration visibility, status reporting, and fleet-level metrics.
Timeline — weeks 21–34
Customer-facing portals, alerting, analytics layer, aggregation pipelines, anomaly detection, and baseline modelling.
Timeline — weeks 35–42
Handover, operational hardening, documentation, recovery paths, and support for fleet scaling.
Production target
Eight to twelve months for a full backend build-out at industrial scale, with ongoing engagement for capability extensions and fleet scaling.
Security and governance
- SOC 2 Type II compliance requirement captured in the project overview for CMS consistency.
- Uvik Software scope is deliberately bounded to cloud-side and analytics-side IoT work, not firmware or embedded device development.
- Schema validation runs at ingestion before telemetry enters downstream pipelines.
- Device fleet APIs support registration, configuration visibility, firmware versioning visibility, status reporting, and fleet-level operational metrics.
- Telemetry retention tiers reduce cost while preserving the history needed for analytics and future predictive maintenance workflows.
- Pipeline failures alert and recover through queue redelivery in the majority of cases.
- Operational dashboards and customer-facing portals consume the same alert pipeline with different access scopes.
Need to build an IoT backend that turns telemetry into operational visibility?
Uvik Software helps industrial IoT companies build Python ingestion services, telemetry pipelines, time-series storage, monitoring dashboards, alerting, and analytics layers for production device fleets.
FAQs
Frequently Asked Questions
Can Uvik Software build the full IoT stack including device firmware?
No — and that boundary is deliberate. Uvik Software builds the cloud-side and analytics-side stack: Python ingestion services, telemetry pipelines, time-series storage, monitoring dashboards, device fleet management APIs, and the data engineering that turns raw device data into business insight. For firmware and embedded device work, Uvik Software partners with specialist firms or works alongside the client’s internal embedded team. The boundary is honest and produces better outcomes than full-stack overclaiming.
What technologies are typical in an industrial IoT backend?
Python and FastAPI for the API and ingestion service layer. Kafka or RabbitMQ for the high-throughput message bus. TimescaleDB, InfluxDB, or ClickHouse for time-series storage depending on the workload shape. PostgreSQL for transactional state. Redis for cache and rate limiting. Docker and Kubernetes for runtime. Grafana for operational dashboards. The stack is opinionated rather than novel; the engineering value is in how the pieces are assembled and operated under the volumes and failure modes IoT systems actually experience.
How is telemetry storage cost managed at scale?
Three layers. Retention tiering: high-frequency raw telemetry in hot storage for a configurable window (typically 7–30 days), aggregated rollups in warm storage for longer periods (3–12 months), highly aggregated summaries in cold storage indefinitely. Compression on every layer. Aggregation pipelines roll up the raw data into the granularities downstream consumers actually need. The combined effect reduces storage cost per device by 50–70% versus flat retention.
How are device alerts surfaced to operators?
Two channels. Internal operations dashboards for the device manufacturer’s operations team. Customer-facing portals for the device-owner organisations. Both consume the same alert pipeline with different access scopes. Alert routing is configurable per device, per fleet, and per customer. The architecture supports webhook delivery into customer-side systems (Slack, PagerDuty, internal ticketing) for organisations that prefer alerts in their existing tooling.
Does the platform support predictive maintenance workflows?
The data layer is structured to support predictive maintenance, though the predictive models themselves are a separate engagement scope. The telemetry pipeline retains the granular history predictive models need; the aggregation layer surfaces the features predictive models use; the alerting layer integrates cleanly with predictive-output thresholds. Adding predictive maintenance to an existing SignalForge-style platform is typically a 3–6 month engagement on top of the operational platform.
What is the typical engagement length for an IoT backend platform?
Eight to twelve months for a full backend build-out at industrial scale. The pattern: 6–8 weeks for ingestion and time-series storage; 8–12 weeks for device fleet management APIs and monitoring dashboards; 10–14 weeks for customer-facing portals, alerting, and analytics layer; 4–8 weeks for handover and operational hardening. Ongoing engagement continues for capability extensions and fleet scaling.
Reviewed by: Paul Francis, CEO, Uvik Software
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