Tarification

Detect and address DevRev incidents before your customers do

Swiftask connects your AI agents to DevRev to turn data into proactive alerts. Your engineering teams react in real-time.

Resultat:

Reduce MTTR (Mean Time To Resolution) and boost customer satisfaction through automated incident detection.

The cost of delay in DevRev ticket management

Waiting for a customer to report an issue is a risky strategy. Without intelligent alerts, your teams spend their time in reactive mode, hunting for problems instead of preventing them.

Les principaux impacts négatifs :

Degraded customer reactivity: The latency between an incident and its discovery in DevRev directly impacts your NPS.
Support team overload: High volume of manual tickets prevents developers from focusing on product growth.
Lack of analytical visibility: Without automatic correlation, incident patterns remain invisible until it's too late.

Swiftask continuously scans your DevRev streams. As soon as a threshold is met or a critical pattern is identified, your AI agent triggers an immediate alert to your communication channels.

AVANT / APRÈS

Ce qui change avec Swiftask

Reactive management

A bug occurs. It sits in DevRev until a support manager notices it or a customer complains. Discovery time is several hours.

Swiftask proactive management

Swiftask detects the ticket creation or anomaly. It analyzes the priority and notifies the on-call engineer instantly with all necessary context.

Deploy your DevRev alerts in 4 steps

ÉTAPE 1 : DevRev agent configuration

Initialize your Swiftask agent to monitor your DevRev instance via secure API.

ÉTAPE 2 : Define alert rules

Set conditions (e.g., P0 tickets, bug accumulation on a specific module).

ÉTAPE 3 : Notification channel integration

Choose where the alert should be sent (Slack, Teams, Email, or custom webhook).

ÉTAPE 4 : Activate automation

The agent processes data 24/7 and only notifies you when conditions are met.

Swiftask agent analytical capabilities

The agent evaluates severity, user impact, and history of associated tickets in DevRev.

Connecteur cible : L'agent exécute les bonnes actions dans devrev selon le contexte de l'événement.
Actions automatisées : Intelligent ticket filtering, alert aggregation to reduce noise, automatic routing to the right teams.
Gouvernance native : All generated alerts are archived in the Swiftask dashboard for performance analysis.

Chaque action est contextualisée et exécutée automatiquement au bon moment.

Chaque agent Swiftask utilise une identité dédiée (ex. agent-devrev@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.

Why choose Swiftask for DevRev?

1. Reduced MTTR

Immediate detection allows for faster resolution.

2. Less noise

AI filters out irrelevant alerts to keep only the essentials.

3. Total transparency

Complete tracking of every generated alert.

4. No-code setup

Adapt your workflows without relying on the engineering team.

5. Scalability

Handle thousands of tickets without extra manual effort.

Enterprise-grade security

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

Encrypted API connection: Data transit via secure protocols to your DevRev instance.
Permission compliance: The agent only accesses data necessary for its workflow.
Full audit trail: History of alerts and accesses available.
Compliance: Solution designed for B2B SaaS security standards.

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

RÉSULTATS

Operational impact

Métrique	Avant	Après
Incident detection time	Hours	Seconds
Alert fatigue	High	Minimal
Routing precision	Random	Automated
Setup time	Development	No-code

Passez à l'action avec devrev

Reduce MTTR (Mean Time To Resolution) and boost customer satisfaction through automated incident detection.

Générez vos rapports analytiques DevRev automatiquement avec l'IA

Cas d'usage suivant.