Hub Customer Service Engine

A service operator answering a complaint usually cannot see what the customer bought, when it shipped, what they complained about last time, or what the marketing team promised them a fortnight ago. The CRM holds one fragment, the support desk another, the marketing platform a third. So the reply is written from the ticket alone, which is why customers repeat themselves, and why service and sales appear to the customer as two organisations that have never spoken.

• Order status, sales history and support history live in different systems, none of them the one the operator has open.
• Generic AI reply tools draft from the ticket text only, which produces fluent answers that miss the actual situation.
• Operator corrections to AI drafts are thrown away, so the same wrong draft is generated tomorrow.
• Large volumes of unstructured customer feedback are never read at all, so campaign-level problems stay invisible.

The Customer Service Engine ships with a base ontology — customer, order, case, agent feedback — that is configured to the organisation and then populated through native connectors to the systems that already hold the data: CRM platforms such as Salesforce and HubSpot, marketing platforms such as Mailchimp and Marketo, and service desks such as Zendesk and ServiceNow. Incoming cases are reasoned over against that whole picture rather than against the message body, and a response is drafted for the operator to explore, edit, approve and send. Both the operator's edits and the end customer's reaction are captured against the ontology with full traceability, so the draft quality moves in the direction the team keeps correcting it in. In parallel, unstructured feedback is parsed at volume into campaign-level insight that can be actioned and written back to the marketing platform it came from.

Two things are only possible with a model. The first is reading: cases arrive as free prose across email, forms and complaint text, and the intent has to be recovered before anything can be retrieved. The second is scale of synthesis — no team reads fifty thousand feedback comments, so the campaign-level pattern inside them is simply never found. What keeps this safe is that the model never sends: it drafts, an operator approves, and the approval or the edit is the training signal. The ontology is what stops the draft being generic, because it decides what the model is allowed to see about this specific customer.

ONTOLOGY

Customer, order, case, agent feedback — configurable per organisation

CONNECTORS

CRM, marketing and support platforms, native

DRAFTING

AI-drafted responses, operator-approved before send

FEEDBACK

Operator edits and customer reactions captured with traceability

ANALYTICS

Unstructured feedback parsed at volume into campaign-level insight

WRITE-BACK

Campaign actions returned to the originating marketing platform

1. 01Customer 360 assembled across CRM, marketing and support systems rather than a single desk.
2. 02Cases reasoned against orders, order status, sales history and stated preferences.
3. 03Operators explore the case, edit the drafted reply, then approve — nothing sends unreviewed.
4. 04Every edit captured in the ontology with traceability, so drafts converge on house voice.
5. 05Large-scale unstructured feedback parsed into actionable, campaign-level insight.
6. 06Actions written back to the marketing and support tools the organisation already runs.

Zero

Responses sent without operator approvaldesign intent

Every edit

Captured as signal, with traceabilitydesign intent

• CAMPAIGN OPERATIONSHub for Marketing Campaign Optimization→
• CLAIMS ADJUDICATIONHub for Warranty Claims Resolution→
• SERVICE OPERATIONSHub for Technical Issue Resolution→