AI customer support agents that resolve real tickets.
Custom AI agents for customer support — triage, draft, escalate, resolve. Wired into your helpdesk (Zendesk, Intercom, Help Scout) with your knowledge base and your product data.
Generic support chatbots are why customers ask to speak to a human.
Off-the-shelf support chatbots optimize for the common case. They handle 'where's my order' and fall over on anything specific. Customers learn to type 'agent' immediately, and the bot becomes a wall to climb over rather than a help.
Vendor support agents (Intercom Fin, Ada, Forethought) are better — but locked to the vendor's data model and integrations. If you have non-standard product data, custom escalation rules, or want pass-through architecture, you're hitting their limits.
Agents that know your product and your customers.
We build custom support agents wired into your real systems — your helpdesk, your knowledge base, your CRM, your product data, your order/subscription systems. The agent sees what a senior support rep would see.
RAG over your support history and docs. Confidence-based escalation to humans with full context. Structured outputs that update your helpdesk. Observability so you can debug every conversation.
The shape of capabilities we build into agents for this use case. Yours may need a subset; we scope in discovery.
Ticket triage
Classify by category, urgency, sentiment. Route to the right queue automatically. Tag with extracted entities.
First-touch responses
Draft replies grounded in your knowledge base and order/subscription data. Reviewed and sent by humans, or auto-sent above a confidence threshold.
Knowledge base search
RAG over your docs and historical tickets. Find the relevant article, summarize, link.
Order / subscription lookup
Wire into your e-commerce, billing, or subscription platform. Real customer data, not training-data guesses.
Multi-language
Same agent handles English, Spanish, French, etc. natively without separate setups per language.
Sentiment + escalation
Detect frustration, churn risk, complex issues. Escalate with full conversation context to the right human.
Refund / RMA workflows
Handle refunds and returns within policy. Escalate exceptions. Full audit log of decisions.
Internal-facing agent
Same agent as a copilot for your support reps. Draft replies, lookup history, suggest solutions — rep approves and sends.
Continuous learning loop
Every escalation becomes a training case. The agent gets better at edges over time, with human oversight on each improvement.
How the agent ships.
Same structure for every agent build. Predictable timelines, fixed scope.
- 01
Discover
1 week. Real workflow, real data, what 'good' looks like. We pick what the agent owns and what humans keep.
- 02
Architect
1 week. Tool inventory, model choice, memory strategy, escalation rules, eval criteria — written and reviewed.
- 03
Build MVP
2–3 weeks. Working agent handles the happy path against real data. Observability and human-review UI from day one.
- 04
Harden
2–4 weeks. Edge cases, evals, monitoring, training, gradual rollout. Production-ready.
- 05
Run
Optional retainer. Model upgrades, prompt tuning, new tools, scope expansions.
The architecture you'd be getting.
If your use case fits their model, do that. Custom wins when: (a) your product data lives in non-standard places they don't integrate with, (b) you need pass-through architecture for compliance, (c) you have non-trivial routing rules they can't express, or (d) you're large enough that per-resolution pricing exceeds the build cost. See /comparisons/build-vs-buy-ai-agent.
Multi-layer: RAG grounding (the agent answers from your docs, not training data), confidence thresholds (low-confidence cases escalate), structured outputs (no creative free-form responses on critical paths), full evals against historical tickets, and observability so you can audit and improve.
Both — your choice per action type. Common pattern: read-only for the first 60 days while you build trust, then enable write actions (issue refund, update subscription, send replacement) with confidence gates. Destructive actions always have audit logs and reversibility where possible.
Zendesk, Intercom, Help Scout, Freshdesk, Front, Gladly, custom systems. We use their APIs or MCP servers we've built. Email and chat both supported.
Typical: 30–60% deflection on tier-1 tickets within 60 days. Agent handles the routine; humans handle the rest with full context. Quality goes up (faster responses, consistent tone, no agent training drift) and costs go down. We model ROI in discovery for your specific volume.
Want a customer support agent for your team?
Tell us the workflow, the volume, and the systems involved. We'll come back with what we'd build, how it would handle the edge cases, and a fixed quote.