AI automation for support and back-office work

Problem

Customers wait in a queue for questions the last hundred people already asked. After hours, nobody answers. Staff spend the day copying a ticket from one form to another: leave request, invoice check, access grant, status ping. Each hop is a person, a delay, and a chance to drop the ball.

Buying a public chatbot puts transcripts and process data on someone else's cloud. Building every integration by hand does not scale when the next department wants the same thing. The organization needed AI that can talk to customers and move internal work — on hosts it already operates, with flows it can change without rewriting the product.

System Design

This project is a self-hosted automation layer with two faces.

Support. A conversation flow sits in front of knowledge the company already has: docs, FAQs, ticket history. The model answers when it can, asks for what is missing, and hands off to a human when it should not guess. The transcript stays inside the perimeter.

Back office. Workflows watch events — a form submit, a new mail, a status change — then call the next system, fill the record, notify the owner, or wait for approval. AI is used where language or messy input would otherwise need a clerk: classify, extract, draft, route. Deterministic steps stay deterministic.

Both run as stacks on the internal network. Apps and channels inject the bot or fire a webhook. Secrets and models stay under the same control as the rest of the platform.

Architecture Decisions

• Conversation flows for customers; event workflows for staff — one platform, two jobs
• Self-hosted so support chat and HR data do not leave the organization
• Hand-off to a human when confidence is low; automation is not a wall
• Knowledge from internal sources, not a generic public model with no context
• Webhooks and APIs into the tools people already use, instead of a new inbox
• Repeatable deploy so staging can try a flow before production customers see it

Tradeoffs

• You own prompts, model cost, and the quality of the knowledge base
• A bad flow can spam a department or answer a customer wrong — review and limits matter
• AI classification is probabilistic; money and access still need a hard rule or a person
• More integrations mean more credentials to rotate
• Self-hosting the orchestration is operational work; a SaaS bot would hide that

Production Behavior

Point the support widget at the conversation stack. Point forms and mail at the workflow stack. A customer question is answered or escalated. A staff request is classified, routed, and closed without a copy-paste chain.

Watch failed runs, hand-off rate, and time-to-first-answer. Built for teams that want AI in the path of work — support and administration — without handing the process to a public chatbot vendor.