Pricing

Smart alerting: analyze your TimescaleDB data in real-time

Swiftask connects your AI agents to TimescaleDB to monitor your time-series. Detect anomalies and receive contextual alerts instantly.

Result:

Move from reactive monitoring to a proactive strategy. Reduce alert noise and speed up your response time.

The limits of manual time-series monitoring

Monitoring massive data volumes in TimescaleDB with static thresholds generates a constant stream of false alerts. Your technical teams spend their time sorting signal from noise instead of solving real issues.

Main negative impacts:

Alert fatigue: Too many fixed thresholds create useless notifications, leading to the neglect of critical alerts.
Lack of context: A simple alert doesn't explain why a metric deviates. Manual analysis consumes precious time.
Limited reactivity: Without AI, complex correlations between multiple data streams go unnoticed until a major incident occurs.

Swiftask uses AI to analyze your TimescaleDB data continuously. It identifies abnormal behaviors, contextualizes the alert, and triggers automated responses.

BEFORE / AFTER

What changes with Swiftask

Classic monitoring

You set an alert on a fixed threshold. If CPU goes over 90%, you get an email. Often, it's just a temporary spike with no significance, but you still have to verify it manually.

The Swiftask + TimescaleDB approach

Your AI agent learns the normality of your data. It ignores normal spikes and only alerts you if the deviation indicates a real issue, including a summary of probable causes.

Setting up your smart monitoring in 4 steps

STEP 1 : Connect to TimescaleDB

Configure read-only access to your TimescaleDB instance from Swiftask to enable analysis of your time-series.

STEP 2 : Define patterns

Tell your agent which key metrics to watch. The AI analyzes historical data to establish a baseline.

STEP 3 : Configure alerts

Define alert conditions based on abnormal behaviors rather than rigid thresholds.

STEP 4 : Automate actions

Configure automated responses: Slack/Teams notifications, service restarts, or Jira ticket creation.

Advanced AI detection capabilities

The agent examines seasonality, trends, and cross-metric correlations within your TimescaleDB tables.

Target connector: The agent performs the right actions in timescaledb based on event context.
Automated actions: Real-time anomaly analysis. Automatic correlation between multiple data sources. Diagnostic report generation. Sending enriched alerts with recommendations.
Native governance: All analyses and alerts are archived, providing complete traceability of your infrastructure's health.

Each action is contextualized and executed automatically at the right time.

Each Swiftask agent uses a dedicated identity (e.g. agent-timescaledb@swiftask.ai ). You keep full visibility on every action and every sent message.

Key takeaway: The agent automates repetitive decisions and leaves high-value actions to your teams.

Why choose Swiftask for TimescaleDB

1. Noise reduction

Filter out false alerts through intelligent understanding of your data.

2. Accelerated diagnosis

Each alert comes with context explaining the observed deviation.

3. Data governance

Keep full control over access and analysis rules within your workspace.

4. No-code flexibility

Adapt your alerting models without writing any complex SQL.

5. Ecosystem integration

Connect your alerts to all your communication and task management tools.

Security and compliance

Swiftask applies enterprise-grade security standards for your timescaledb automations.

Secure connection: Use of encrypted connections and restricted access to necessary data only.
Environment isolation: Each agent operates in a secure perimeter without access to sensitive data not required.
Full audit: Logging of all alerts and actions performed by the AI agent.
Compliance: Architecture designed to respect B2B data protection standards.

To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.

RESULTS

Operational impact

Metric	Before	After
False alert reduction	High (constant noise)	80% reduction on average
Detection time	Reactive (manual)	Proactive (real-time)
Maintenance burden	Heavy manual setup	Autonomous via AI
Incident clarity	Little context	Automatic diagnostic included

Take action with timescaledb

Move from reactive monitoring to a proactive strategy. Reduce alert noise and speed up your response time.

Analyze your TimescaleDB data by simply asking questions

Next use case