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SaaS Industry Solution

Autonomous AI Support Copilot

L1 and L2 support queues frequently bottleneck, leading to high resolution times and decreasing Net Promoter Scores (NPS). Focus20 deploys sophisticated autonomous AI agents that seamlessly integrate into Jira/Zendesk workflows to auto-resolve tier-1 issues, orchestrate deep troubleshooting, and dramatically arm human agents.

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Business Value

Deflecting Tickets at the Source

By automating context-heavy debugging (like tracking down CloudWatch tracebacks or querying Datadog logs), we reduce the Mean Time To Draft (MTTD) fixes, enabling engineering to focus on feature-building rather than interrupt-driven bug squashing.

4 Mins
Mean Time to Draft Fix
35%
Less Context Switching

The Agentic Workflow

1. Agentic Stack

Log Agent: Datadog/CloudWatch API tool.
Code Agent: GitHub API tool.
Reasoning Engine: Bedrock Claude 3.5 Sonnet.

2. Reasoning Loop (ReAct)

> PagerDuty config alert fires (Memory leak in ECS)
> Log Agent queries CloudWatch for traceback
> Reasoning Engine evaluates ECS Terraform parameters
> Code Agent branches repo and edits configuration
> Opens automated Pull Request targeting `main` branch

Enterprise Technical Architecture

Designed to securely sit between your user portal and your backend developer tools.

Autonomous Issue Resolution

  • Event Ingestion: Amazon EventBridge parses real-time incoming webhook data from Zendesk or Jira Service Management.
  • RAG Backend: AWS Bedrock Knowledge Bases indexes vast Confluence spaces, past tickets, and Slack histories for semantic reference.
  • Automated Action: LangChain tooling binds the AI Agent directly to scriptable Lambda functions (e.g., "reset tenant cache", "restart pod").
  • Supervised Handoff: Smooth escalation protocols map complex issues immediately back to human L3 queues, retaining all AI-gathered context.

Cloud Migration Highlight

Migrated a high-growth SaaS platform's multi-tenant core from Azure SQL to Amazon Aurora PostgreSQL, achieving 40% improved DB performance with zero downtime cutover.

graph LR USER[User Query] --> ZEN[Zendesk] ZEN -->|Webhook| EB[EventBridge] EB --> L[AWS Lambda] L --- KB[(Knowledge Base)] KB --- AG[Copilot Agentic Loop] AG -->|API Action| SRV[User Server Reset] AG -->|Reply/Resolve| ZEN AG -->|Escalate| HMN[L3 Human Support]

Deployment Timeline

Training an AI Copilot that acts like your best Senior Engineer.

Phase 1: Weeks 1 - 2

Knowledge Base RAG

We index your entire internal documentation (Confluence, Jira, Notion, GitHub issues) into an AWS Bedrock Vector Database, training the foundation model on your specific SaaS architecture and historical resolutions.

Phase 2: Weeks 3 - 6

L1 Auto-Resolution

The agent is given write access to your ticketing system. It begins auto-replying to redundant tier-1 inquiries (e.g., password resets, basic configuration questions), successfully deflecting ~30% of incoming volume.

Phase 3: Weeks 7 - 10

Infrastructure Tooling

The Copilot is equipped with "Tools" via LangChain. For complex tickets, the AI now autonomously queries server logs, proposes code-level fixes, and drafts the GitHub Pull Request before routing the ticket to an L3 engineer for one-click approval.

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