Customer Support AI
An AI layer over the existing ticket stack — routes tickets by category and urgency, drafts initial responses for routine queries, flags edge cases for escalation, surfaces context from prior interactions and product documentation.
- Engagement
- 6–10 week build · ongoing operation
- Built for
- VP Support · CX leads · Heads of customer experience
Customer support volume scales with business growth — but support headcount can't scale proportionally without margin damage. The team's time goes to routing tickets, drafting responses to repeat questions, and escalating the edge cases that should have escalated faster.
What this is
A support-team augmentation engagement. Three layers:
- Ticket triage and routing. Classification by category, urgency, complaint pattern. Routing to the appropriate queue or agent.
- Draft-response generation. Routine questions get a draft response surfaced to the support agent. Agent reviews and sends; the AI doesn't auto-send.
- Context surfacing. Prior interaction history, product documentation relevant to the ticket, customer-account context — surfaced alongside the ticket for the agent.
How it's built
LLM layer for classification and draft generation. Embedding-based retrieval over the support knowledge base for the context layer. Integration with the existing ticket platform via API. Per-engagement fine-tuning on the business's support history and product documentation.
What you get
- The routing and triage layer integrated with your ticket platform.
- The draft-generation system with agent-review workflow.
- The context-surfacing layer.
- Quarterly accuracy review on draft-versus-sent comparison.
- Documentation for the support team handing off the system.
Engagement is shape, not list.
Length and price are functions of the data and the destination. The shape below is the typical engagement.
- Length
- 6–10 week build · ongoing operation
- Lead
- Bogdan
- Cadence
- Async, weekly
- Bar
- Production
Scoped during the discovery call against the actual data and the operation it integrates with.
Principal engineer. Architecture and most code ships through one keyboard.
Written updates between, calls when the decision needs the room.
Async correctness, capacity under burst, observability at every boundary.
Products this composes with.
Same suite, or vertical-specialized versions in another.
- Same suite · AI Implementation Suite
Internal Knowledge Assistant
An LLM-powered Q&A assistant on the business's internal document corpus — team-facing chat interface, permission-aware, with answer-with-citation so users can verify before acting.
- Same suite · AI Implementation Suite
Sales & Marketing Operations AI
An AI layer across the RevOps stack — predictive lead scoring, CRM data hygiene, outbound drafting and sequence-optimization, attribution analysis. Sits on top of the existing CRM and marketing automation.
What buyers ask about this one.
Will customers know they're talking to AI?
Configurable. Some businesses prefer explicit disclosure ('this draft was prepared by AI for review by your support agent'); others prefer transparent augmentation where the support agent is always the one signing the response. The engagement scopes the customer-facing posture during discovery.
How does it integrate with our existing ticket system?
Zendesk, Intercom, Front, Salesforce Service, custom — standard integrations for the common platforms, custom connector for the rest. The AI layer sits on top of the ticketing system rather than replacing it.
What about hallucination — answering customer questions wrong?
Routine-question drafts are grounded in product documentation and prior support history; the agent reviews before sending. For questions where the documented answer is uncertain, the engine surfaces uncertainty rather than confident-wrong responses. Per engagement we measure draft-accuracy on a held-out set.
How does the escalation logic work?
Per-business: the engagement defines what 'escalation' means (severity thresholds, customer-VIP rules, complaint-pattern detection, regulatory-trigger detection). The AI layer routes accordingly; the support team owns the escalation handoff.
Pricing?
Scoped to ticket volume and integration depth. Discovery call covers both.
If the deliverable matches the gap, the next step is one call.
We'll scope length and price against your data and the operation it integrates with. No retainer, no fishing.
Bogdan and team · async-first · OP—2026