IoT

Connected devices, real-time analytics.

Acerti builds IoT applications for manufacturing, healthcare, and logistics. AWS IoT Analytics as the default stack, AI-driven dashboards as the rule, custom firmware where the off-the-shelf stops fitting.

View delivery models →

1// AI-reviewed PR · senior LATAM engineer

2async function checkout(({ cart, user }) {

3 const total = price(cart);

4 if (!user.verified) throw new AuthError();

5 return stripe.charge({ total, user });

8// ✓ 0 defects · 12/12 tests · merged in 2h

Reviewed · React 18 · Stripe~30% fewer defects

Default platform

AWS IoT Core + Analytics

Verticals

Manufacturing · Healthcare · Logistics

0k+ endpoints

Avg. sensor scale

<400ms

Telemetry latency

0.0%

Device fleet uptime

~0%

Cost savings vs. onshore

WHAT WE DELIVER

Services we deliver.

IoT application development

End-to-end: edge agent, ingest, processing, dashboard. AWS IoT Core, IoT Analytics, IoT Greengrass.

Sensor integration

Modbus, OPC UA, MQTT, LoRaWAN. Industrial protocol bridges and gateway design.

Edge compute

AWS Greengrass, custom edge agents. Local inference where bandwidth or latency rules out the cloud round-trip.

AI-driven analytics

Anomaly detection, predictive maintenance, demand forecasting on time-series data.

Dashboards and operator UX

Real-time dashboards built for the people who actually run the plant or the fleet, not for the board deck.

Security and compliance

Device identity, certificate rotation, TLS, regulatory compliance for healthcare and industrial environments.

How AI shows up here

AI on time-series anomaly detection

Catches drift and failure precursors that threshold-based alerting misses.

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AI on predictive maintenance

Forecast component failure windows; turn unplanned downtime into scheduled maintenance.

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AI-matched IoT engineers

Senior LATAM consultants with prior industrial / healthcare / logistics IoT deployments.

✓

RECENT WORK

What it looks like in production.

Predictive maintenance · industrial manufacturer

“Acerti put predictive maintenance on our extrusion lines. Unplanned downtime dropped 36% in the first six months — the model is paying for itself, not for our IoT vendor.”

Henrik J. · Plant Operations Director, industrial manufacturer

-36%

Reduction in unplanned downtime

12 wks

Time from kick-off to first model

OUTCOMES

What clients see in the first 90 days.

-36%

Unplanned downtime

Typical reduction on predictive-maintenance engagements.

10k+

Endpoints supported

Production scale on past industrial deployments.

12 wks

Kick-off to first model

Median on predictive-maintenance projects.

CONTINUE EXPLORING

Related practice areas.

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