Fordel StudiosCustomer Support Agent
Resolves L1 support tickets with your actual product knowledge — not generic scripts.
The problem
being solved
A B2B SaaS company handling 500+ support tickets daily has a three-tier support structure. L1 agents handle password resets and known issues from runbooks. L2 handles technical troubleshooting. L3 handles bugs and escalations to engineering. The problem: L1 agents resolve only 30-40% of tickets, forwarding the rest to L2 with incomplete context.
Intercom's Fin AI reports a 66% average resolution rate across 6,000+ customers. Forethought claims up to 98% resolution with well-optimized knowledge bases. Ada handles enterprise-scale multilingual support. These platforms proved AI can resolve support tickets by understanding the problem and providing accurate answers.
The gap: most SaaS companies have product-specific context (user account state, feature flags, subscription tiers, recent actions) that generic chatbots cannot access. Resolution requires integrating with the product database, not just the help center.
How this
agent works
This agent connects to your product database, knowledge base, and ticketing system. When a ticket arrives, it pulls the customer's full context: subscription tier, feature flags, recent actions, error logs, and account health signals. It understands the problem in context — not just what the customer said, but what their account state reveals.
For known issues, it resolves directly: walks the customer through steps, triggers account actions (password resets, feature toggles, data exports), and confirms resolution. For novel issues, it performs diagnostic analysis: correlating symptoms with error logs, recent deployments, and known bug reports.
When escalation is necessary, the agent creates a structured handoff: customer context, diagnostic findings, attempted resolutions, and a recommended next step. L2 agents start working the problem immediately instead of re-gathering context.
Built on Node.js with Anthropic Claude as the reasoning layer, connected to your product database (PostgreSQL), session cache (Redis), and knowledge base (Elasticsearch). A senior engineer wires the agent into your existing API surface — account state, feature flags, error logs — so it resolves tickets with real product context, not a static FAQ. Integrates with Zendesk, Intercom, or Freshdesk via their native APIs. Setup takes 2–3 weeks including knowledge base indexing, sync pipelines, and escalation routing.
- 01
Product-Aware Context Engine
At ticket creation, the agent queries your product API for account state, subscription tier, feature flags, recent actions, and error logs. Claude reasons over this full context before generating any response — so it already understands the problem before asking a single follow-up question.
- 02
Automated Issue Resolution
Handles common L1 tickets end-to-end: password resets, feature configuration, data exports, billing inquiries. Executes account actions directly through your product API — not just responding with instructions, but completing the action and confirming with the customer.
- 03
Diagnostic Root Cause Analysis
Correlates customer-reported symptoms with error logs in Elasticsearch, recent deployment diffs, and known bug reports. This goes beyond keyword matching — Claude identifies patterns across signals that a rules-based chatbot would miss entirely.
- 04
Structured Escalation Handoff
When a ticket needs a human, the agent generates a structured summary: customer context, what was already attempted, diagnostic findings, and a recommended next step. L2 agents skip the back-and-forth and resolve faster.
Build this agent
for your workflow.
We custom-build each agent to fit your data, your rules, and your existing systems.
Free 30-min scoping call