AI-Powered Spacecraft Operations with InfluxDB 3

When a satellite is drifting toward a fault, operators don’t need another dashboard full of disconnected charts. They need to know what changed, what it means, and what to check before the next ground pass closes. That’s the idea behind our satellite telemetry demo: a live mission-control experience built on InfluxDB 3.

The demo monitors a simulated fleet of 12 satellites, continuously ingesting telemetry such as onboard computer temperature, battery voltage, solar current, reaction wheel RPM, power draw, RF link margin, ground station, orbit pass, and attitude status. One satellite is actively degrading, while historical incidents provide useful context for comparison.

You can check out the live demo here or watch the video to see it in action:

Demo overview

From Telemetry Stream to Operator Signal

The main dashboard provides a fleet overview showing which satellites are online, which are currently in contact, and where anomalies are occurring. Operators can then drill into individual spacecraft to get a more specific view and look at historical data.

Detecting Anomalies as Data Arrives

Behind the scenes, the InfluxDB 3 Processing Engine observes incoming data as it is written to the database and automatically creates alerts based on conditional thresholds or sensor states.

That means anomaly detection happens without waiting for a separate pipeline to catch up. Another processing engine task incorporates NOAA data for environmental context alongside vehicle behavior.

This pattern can be extended beyond satellites. Any connected system with high-volume and time-sensitive data, such as industrial equipment, energy infrastructure, production systems, or logistics networks, will benefit.

Accessing Operational Truth Using AI

This demo also includes an AI agent powered by the InfluxDB 3 MCP server. Instead of generating answers based on generic knowledge, the agent can access data stored in InfluxDB. You can ask about fleet health, a specific satellite, or the root cause of a suspected issue. The agent can then query InfluxDB throughout a conversation and summarize what it found.

Under the hood: From raw telemetry to operational context

This demo goes beyond the dashboard; it illustrates how InfluxDB 3 can transform live data into enriched operational data without external tools, as well as how to integrate InfluxDB into LLM-powered workflows.

Alerting on Real-Time Data

Each incoming batch of satellite telemetry data triggers a processing engine plugin that evaluates data, such as:

• Onboard computer temperature
• Reaction wheel RPMs
• Power draw
• Attitude status

If an anomaly is found, an alert is written to a separate InfluxDB table. The row contains all relevant information for managing an incident, both in the short term and for historical tracking. Instead of the AI agent burning tokens querying and searching raw telemetry, it works from a dedicated alert stream that preserves the signal behind the alert.

Enriching Telemetry with Third Party Data

Another way the demo uses the Processing Engine collecting NOAA planetary Kp index data with an external API call. This data is then stored in InfluxDB where operators can analyze how satellites are being impacted by external conditions and surface any correlated relationship between them.

Scheduled Processing Engine jobs help enrich operational data. Depending on your use case, this could include weather, energy prices, traffic, or financial market data. It can also calculate rollups, forecast, or downsample data at a scheduled interval.

Integrated AI Agent Using the InfluxDB 3 MCP Server

The AI agent is connected to InfluxDB via the MCP server. Rather than relying on a static prompt or blindly summarizing a dashboard, it can query recent telemetry and alerts, inspect the result, and explain its conclusion.

The UI exposes that query/tool trace so operators can see what evidence informed the answer. The agent effectively becomes an interface to your data model.

Putting the processing engine to work

InfluxDB 3’s Processing Engine runs Python inside the database and supports triggers on data writes, schedules, and HTTP requests. That allows it to do far more than simple threshold alerts. You can check the documentation for more detail on the three trigger types and how plugins can retain state between executions.

Here are a few interesting ways you can take advantage of the Processing Engine:

If you don’t want to start from scratch building with the Processing Engine, there are a number of plugins built and supported by the InfluxDB team. Some plugin examples:

• Anomaly detection
• Schema validation
• Notifications
• Iceberg export
• Integrations for Kafka, MQTT, and AMQP

The bigger opportunity is to compose these pieces. You could ingest data, validate it on write, detect drift against a baseline, write a contextual alert, enrich it with scheduled external data, and let an MCP-enabled AI agent explain the event in plain language.

Check out the demo and start building

While this demo is built specifically for satellites, the architecture is designed for any use case where time series data is used to take action and direct important decisions.

InfluxDB 3 stores the live signal. The Processing Engine detects, enriches, and routes meaningful events. The MCP server gives an AI agent a grounded way to investigate the data and show its work. Together, they turn telemetry from data teams merely collect into something they can understand and act on.

Resources

• Satellite Telemetry demo
• Download InfluxDB 3
• InfluxDB 3 Explorer
• InfluxDB 3 MCP Server
• InfluxDB 3 official processing engine plugins

FAQs

How does the demo detect anomalies in real time?

The InfluxDB 3 processing Engine watches incoming data as it is written and automatically generates alerts based on conditional thresholds or sensor states. This removes the need for a separate processing pipeline.

What is the InfluxDB 3 Processing Engine and what can it do beyond alerting?

The Processing Engine allows InfluxDB 3 to run custom Python code inside your database. This code can be activated by 3 different trigger types: data writes, schedules, or via HTTP request. Beyond threshold alerts, it can be used for schema validation, deadman checks, downsampling, data transformation, and many other use cases.

How can InfluxDB be used with an AI agent?

This demo uses the InfluxDB MCP server to connect to an AI agent, giving the LLM context directly from InfluxDB to answer questions about the current state of the satellite fleet. InfluxDB also has a CLI and REST API that can be utilized by AI agents.

Is this demo specific to satellites or can the architecture be reused elsewhere?

The architecture is designed for any use case built on time series data that is used to drive decisions. It could be used for monitoring industrial equipment, energy infrastructure, or logistics networks. The same combination of InfluxDB for storage, Processing Engine for anomaly detection and enrichment, and MCP for AI-powered investigation generalizes for all of these use cases.

What is InfluxDB 3

InfluxDB 3 is a time series database used to store and analyze time series data for high performance workloads. It has a built-in Python VM for low latency data analysis and an MCP server for integration with AI agents.