Augmented Customer Support
Accelerate and improve customer request processing through intelligent analysis of messages, documents, and history.
A customer request rarely arrives alone: it often bundles several issues, points back to a history scattered across different tools, and has to be handled without losing track of the case.
Fasfox builds systems that analyse incoming requests, retrieve the linked history and documents, and draft a reply the agent reviews and sends.
The ticket that tangles three issues and no order number
A customer writes in about a late delivery, a disputed invoice, and a warranty question, without including an order number. The agent has to work out which case this is, reopen the CRM, dig through the history of past exchanges, and check which policy applies before replying. Reconstructing the context often takes longer than answering it, and it happens dozens of times a day across the team.
The chatbot that invents a refund policy
An assistant wired directly into the customer channel, without oversight, sometimes answers confidently but wrongly against the actual contract terms: it promises a refund the company does not offer, or cites a warranty that has already expired. The customer walks away with a promise nobody will honour, and the human agent inherits the complaint. Most pilots that fail share this flaw, the AI replies straight to the customer before anyone has tested it against ambiguous cases.
Without an organised knowledge base, there is nothing to automate
This system depends on an up-to-date knowledge base, policies, procedures, known exceptions, that the AI can query directly, rather than a pile of past email threads to comb through every time. If that base does not yet exist in usable form, building it is its own project, and comes before any support automation. Feeding a model on scattered email threads instead only teaches it to repeat whatever inconsistencies already exist.
Only the agent sends
We build a system that reads the incoming ticket, retrieves the linked history and documents, and drafts a reply grounded in the knowledge base. The agent reviews, edits if needed, and sends. On requests touching a financial or contractual commitment, the draft is flagged as such, and the agent must approve it explicitly before anything goes out.