Industry Scenario — SaaS Customer Support

The team: a SaaS company's support + product team. The goal: deflect L1 tickets with an on-brand assistant, cut the per-token bill, and hot-swap models without downtime.

The journey

Build · 1 — connect your data

• Connect the support catalog and documentation (or export tickets) as the assistant's grounding.
• Wire the agent stack to the data via MCP so support agents answer from your content.

Build · 2 — train the model

• Fine-tune a Support assistant preset on historical tickets + resolutions (instruction/output JSONL).
• Post-train with ORPO on "helpful, brand-voiced, escalate-don't-hallucinate" pairs.
• GPU smoke + confidence cache keep each run cheap and predictable.

Ship — a hot-swappable assistant

• Deploy the model; roll it out via canary at 10% traffic so a tone regression is caught before it hits everyone.
• New fine-tunes attach as adapters — one endpoint, many versions, instant rollback.

Ship — cut the per-token bill

• Point the existing OpenAI-style integration at your own model via the Inference Gateway — a URL + key swap. Fixed infrastructure cost instead of a meter that grows with usage.

Monitor — ops without firefighting

• Drift schedule on the assistant's input (ticket language drifts as your product evolves).
• Self-heal retries a bad deploy automatically; escalated failures surface with the real error and one-click approve/stop.

Own it — export the weights

• Export to GGUF and run on cheaper CPU-only servers for internal tools — same model, your hardware, no per-call fees.

What you own at the end

• An on-brand assistant deflecting a meaningful share of L1 tickets.
• A predictable infrastructure cost line instead of per-token spend.
• Models you can hot-swap and export.

Key docs

Fine-tune · Post-train · Ship & canary · Gateway · Monitor & self-heal · Export · MCP