Customer support AI that answers, assists and escalates
Resolve the repetitive tickets automatically, give agents a grounded draft for the rest, and escalate the hard ones with full context, without inventing a single policy.
Most support queues are dominated by a few dozen intents: password resets, invoice copies, "where is my order", plan changes. The answers sit in help-centre articles, macros and thousands of resolved tickets. Agents still type them out, one at a time, while the genuinely hard cases wait.
Rule-based chatbots failed because they matched keywords, not meaning. The fix is not a cleverer bot script. It is a retrieval layer over the knowledge you already have, a model that drafts from it, and a review step whose strictness you control.
How it works
From inbound message to closed ticket
Every reply is grounded in retrieved content and carries a confidence score. The score decides whether it goes out directly or waits for an agent; both paths are logged.
1. Classify and protect
Each message is tagged with an intent, urgency and language. Personal data is masked before any model call, and prompt-injection attempts in customer text are filtered.
2. Retrieve and draft
Hybrid search over help articles, policies and resolved tickets returns the few passages that matter. The model drafts a reply from them only, with citations and a confidence score.
3. Decide and record
Safe intents above the threshold are sent. Everything else lands in the agent's queue as a draft. Ticket, CRM and audit log are updated through the agent's own audited identity.
What you get
Deliverables, not a demo
Omnichannel answer service
One API serving email, chat and help-centre search, so the same grounded answer appears everywhere the customer asks.
Knowledge pipeline
Ingestion from your help centre, ticket history and policy documents with permissions preserved, chunked and embedded on a schedule, built on our support copilot architecture.
Agent assist inside the helpdesk
Drafts, suggested macros and a summary of the customer's history appear in the agent's existing tool. No new screen to learn.
Eval suite and dashboard
A test set built from your real tickets, scored for groundedness and tone, plus a live dashboard of deflection rate, agent edits, latency and token cost.
Escalation with context
When the AI hands over, the agent receives the intent, the retrieved sources and what was already tried. Customers never repeat themselves.
Guardrails we build in
Accuracy thresholds: auto-reply only for intents where the eval set shows groundedness above the level you agree, typically starting with the five safest intents. Human-in-the-loop: every other draft is approved by an agent, and any customer can ask for a person. Audit trail: message, retrieved sources, draft, reviewer action and final reply are stored together. PII handling: detection and masking before model calls, retention limits, regional deployment. Injection defence: customer text is treated as untrusted input, following our prompt injection defence practice. The full control set is described under AI security and governance.
Typical rollout
Assisted first, automatic second
Discover (2 weeks)
Ticket analysis to find the top intents, content audit of the help centre, integration and data-residency decisions, KPI baseline.
Build and evaluate (3–4 weeks)
Knowledge pipeline, retrieval, drafting and the eval set. We iterate until groundedness on real tickets meets the threshold.
Assisted pilot (3–4 weeks)
Agents see drafts for every ticket and edit or approve. Edit rates tell us which intents are ready for automation.
Automate and hand over (2 weeks)
Auto-reply switched on per intent, monitoring live, runbooks and training delivered. Optional ongoing tuning under LLMOps.
Illustrative example
What a first release usually looks like
Illustrative scenario, not a client case
A subscription software company receives a few thousand tickets a month across email and in-app chat. Analysis shows that six intents, led by billing questions and access problems, make up roughly half of the volume. In the pilot, agents receive a grounded draft for every ticket and approve most billing drafts with minor edits. After the pilot, those billing and access intents move to automatic replies with a one-click "talk to a person" option, while refund requests and complaints always route to an agent with the AI's summary attached. The team keeps the same headcount and spends the recovered time on the complex cases that used to wait days.
For a real deployment, read how we built the Eventify support AI bot for our own event platform.
It can, which is why every answer is grounded in retrieved articles and tickets, shown with citations, and only sent automatically above a confidence threshold you set. Below it, a human agent approves or edits. We measure groundedness with evals on your real tickets before launch, as explained in our guide to evaluating LLM outputs.
Which channels can it handle?
Email, web chat, in-app chat and help-centre search are typical. Voice is possible with our voice AI service. The same retrieval and guardrail layer serves every channel, so answers stay consistent.
Does it work with our existing helpdesk?
Yes. We integrate with the helpdesk and CRM you already run rather than replacing them, including Salesforce Service Cloud, where Agentforce may be the right engine. The AI reads and writes tickets through their APIs with its own audited identity.
How do you handle customer personal data?
PII is detected and masked before anything reaches a model, retention is limited to what the audit trail needs, and the deployment region is chosen with your data residency rules. See AI data privacy.
How long until it is live?
Eight to twelve weeks is typical: two for discovery and content audit, four to six for an assisted-mode pilot with your agents, and two to four to switch on auto-replies for the safest intents and hand over.
Ready to make AI work in production?
Tell us about your systems, your data and your constraints. We reply within one business day with a straight answer and a suggested next step.