A shared inbox with SLAs and a knowledge base, plus an AI that auto-resolves the repetitive tickets and drafts a reply for the rest. Your team handles the exceptions, not the queue.
Assign, collaborate, internal notes & collision detection.
Priorities, escalation timers and rules that route to the right team.
Deflect repetitive tickets; draft governed replies for the rest.
Self-service KB the AI keeps current from resolved tickets.
Set the rules once. From the moment a ticket lands, it is triaged, routed and timed — so nothing waits in a shared mailbox hoping someone notices.
Priorities and escalation timers keep urgent work visible; routing rules push each ticket to the team that owns it. The shared inbox stops two people answering the same customer with collision detection, and internal notes keep the back-and-forth off the customer thread.
A built-in knowledge base turns every good resolution into a reusable answer — for your customers to self-serve, and for the AI to draft from.
Auto-resolve handles what is safe and routine; everything sensitive becomes a draft a human approves — never an autonomous reply on personal data.
Customer PII is masked before the model sees it — the AI works on client_4821, your agents see Henderson Ltd.
It matches the question to your published answers and best-model-per-job routing writes a reply grounded in your KB — not a guess.
Repetitive questions like password resets close themselves. Anything sensitive is held as a draft instead.
Sensitive replies are draft-and-approve by default. Your agent edits and sends — the human stays in the loop.
You can see exactly what the AI resolved, what it drafted and why — auditable deflection, not a black box.
The AI drafts and resolves on tokenised content, so customer PII never reaches the model. Every auto-resolution is logged, and anything sensitive waits for a human.
How SCRS works →PII masked before AI; rehydrated only for your agents.
Know exactly what the AI resolved, and why.
Sensitive replies are draft-and-approve by default.
The same shared inbox, SLAs and governed AI — tuned to how careful, client-facing teams actually work.
Client queries about filings, invoices and documents — auto-resolve the routine, keep anything sensitive draft-and-approve, and link each ticket to the right CRM record.
Legal & accountancy →Patient PII is tokenised before the model and never used to train it. The AI deflects FAQs from your KB; clinicians approve anything that touches care.
Healthcare →Handle application and policy questions at volume with SLAs you can prove. Audited deflection means every automated answer is on the record.
Mortgage & IFA →Most help desks sit outside your data and pipe customer details to the model in the clear. Ours lives inside one governed suite.
| Other Me Ticketing | Bolt-on help desk | |
|---|---|---|
| PII before the AI | Tokenised first — never reaches the model | Often sent in the clear |
| Auto-resolve trail | ✓ Every action logged | Limited or none |
| Sensitive replies | Draft-and-approve by default | Autonomous send |
| Knowledge base | Built in, AI keeps it current | Separate tool / stale wiki |
| Links to CRM & Live Chat | One dataset, zero integrations | Integrations to wire & sync |
| Models | GPT, Claude, Gemini, Grok — best per job | Single vendor model |
Tickets flow in from Live Chat, link to the CRM record, and trigger Flows — one governed dataset, zero integrations.
