Tarification

Analyze your system logs instantly with AI power

Swiftask integrates via EmbedAPI to scan, interpret, and alert on your log streams. Stop searching for the needle in the haystack.

Resultat:

Move from reactive monitoring to proactive technical incident resolution.

Information overload prevents effective log analysis

Modern systems generate terabytes of logs. DevOps and SRE teams are overwhelmed by the noise, making critical anomaly detection extremely difficult and slow.

Les principaux impacts négatifs :

Excessive noise and alert fatigue: Too many irrelevant logs hide true errors, leading to fatigue and risks of missing critical issues.
High diagnostic time: Manually correlating logs from disparate sources takes hours, extending downtime.
Lack of business context: Classic log tools show the 'what' but rarely the 'why' in relation to user impact.

Swiftask uses EmbedAPI to ingest your logs in real-time. Our AI agents filter the noise, identify abnormal patterns, and provide immediate contextual diagnosis.

AVANT / APRÈS

Ce qui change avec Swiftask

Without Swiftask

An error occurs. The engineer must connect to multiple platforms, filter thousands of text lines, try to correlate timestamps, and hope to find the root cause before client impact worsens.

With Swiftask + EmbedAPI

As soon as an anomaly is detected, Swiftask via EmbedAPI analyzes the context, summarizes the issue in plain language, and suggests a corrective action directly in your ticketing tool.

Setting up your log analysis pipeline

ÉTAPE 1 : Configure the Swiftask agent

Define criticality rules and error types your agent should specifically monitor.

ÉTAPE 2 : Connect sources via EmbedAPI

Use EmbedAPI to send your log streams continuously to Swiftask in a secure and structured way.

ÉTAPE 3 : Define intelligent alerts

Configure thresholds based on abnormal behaviors rather than static keywords.

ÉTAPE 4 : Automate the response

Link the agent's analysis to automatic actions like opening a ticket or a Slack notification.

Advanced analysis capabilities for your logs

The agent examines timestamps, error codes, stack trace messages, and associated metadata to build a global view of system health.

Connecteur cible : L'agent exécute les bonnes actions dans embedapi selon le contexte de l'événement.
Actions automatisées : Intelligent noise filtering. Detection of recurring error patterns. Automatic incident summary. Correlation between different log sources. Incident report export.
Gouvernance native : All analyses are kept in Swiftask to facilitate post-mortems and continuous improvement.

Chaque action est contextualisée et exécutée automatiquement au bon moment.

Chaque agent Swiftask utilise une identité dédiée (ex. agent-embedapi@swiftask.ai ). Vous gardez une visibilité complète sur chaque action et chaque message envoyé.

À retenir : L'agent automatise les décisions répétitives et laisse à vos équipes les actions à forte valeur.

Operational benefits of AI analysis

1. Reduced MTTR

Identify root causes in seconds thanks to semantic log interpretation.

2. Focus on critical incidents

AI eliminates noise to alert you only on issues requiring human intervention.

3. Continuous learning

The agent improves as it processes your logs, becoming more accurate in detecting false positives.

4. Seamless integration

EmbedAPI allows for a lightweight integration that won't slow down production systems.

5. Simplified compliance

Keep an auditable trail of all analyses performed on your system logs.

Security and data privacy

Swiftask applique des standards de sécurité enterprise pour vos automatisations embedapi.

Encrypted transmission: Logs transmitted via EmbedAPI are protected by standard TLS protocols.
Automatic anonymization: Capability to mask sensitive data (PII) before AI analysis.
Secure storage: Your data is processed in an environment compliant with enterprise security standards.
Granular control: You decide exactly which log sources are analyzed by Swiftask.

Pour aller plus loin sur la conformité, consultez la page gouvernance Swiftask et ses détails d'architecture de sécurité.

RÉSULTATS

Impact on your operational performance

Métrique	Avant	Après
Time to detect (MTTD)	Several minutes/hours	Real-time
Noise reduction	100% of raw logs	90% reduction in useless alerts
Diagnostic accuracy	Dependent on human expertise	Standardized by AI
Maintenance cost	High (engineer time)	Optimized (automation)

Passez à l'action avec embedapi

Move from reactive monitoring to proactive technical incident resolution.

Centralisez vos avis clients en temps réel avec Swiftask EmbedAPI

Cas d'usage suivant.