Enterprise SaaS · Use Case
Customer Service Automation: 35–45% Fewer Escalations.
Most support tickets are tier-1: password resets, plan questions, known bugs, billing lookups. A model fine-tuned on your brand voice, product docs, and support policy handles that volume directly, and routes anything with a judgment call, a refund, or an unhappy customer to a person. The gates on those actions are set by you, not by the model.
The Problem
Tier-1 volume swamps the queue your hard tickets sit in.
Support teams spend most of their hours on repetitive, well-documented questions. That volume sets the queue length, the staffing plan, and the response-time SLA that your genuinely difficult tickets, the ones that need a person, have to wait behind.
The aim is not to remove people from support. It is to take the repetitive tier-1 load off the queue so the team spends its time where judgment matters.
Deflection with a floor
The agent answers what it can answer well and hands off everything else. It never issues a refund, makes a retention offer, or closes an escalated ticket on its own.
The Approach
QLoRA on your brand, product, and policy.
We fine-tune an open-weight model with QLoRA on three things: your published brand voice, your product and help-centre documentation, and your support policy, including what the agent is and is not allowed to do. The result answers in your tone and stays inside your rules. Agentic AI covers how the handoff gates and tool access are wired.
Consequential actions, refunds, plan changes, anything customer-facing and hard to reverse, sit behind human-in-the-loop gates. The agent can propose them; a person approves.
Why fine-tune, not prompt
A prompt-stuffed generic model drifts off-tone and improvises policy. A QLoRA fine-tune on your own material holds the voice and the boundaries far more consistently, and runs in your tenant on weights you own.
Guardrails
Human-in-the-loop on every consequential action.
The agent is deployed in your cloud tenant on weights you own. It answers tier-1 questions directly, but approvals, refunds, retention offers, and customer-facing commitments all require a human in the loop. Autonomy is earned incrementally, per the boundary published across our SaaS work, and the model is red-teamed before production.
You set the gates
Which actions the agent can take, and which need sign-off, are configured by your team and enforced in the workflow, not left to the model’s discretion.
Who This Is For
Support-led SaaS with heavy tier-1 volume.
PLG and vertical SaaS, marketplaces, and any product where a large share of tickets are documented, repeatable questions, and where response-time SLAs are under pressure from that volume.
FAQ
Common questions about customer service automation.
Will it issue refunds or make retention offers on its own?
No. Those sit behind human-in-the-loop gates you configure. The agent can propose the action; a person approves it.
How does it stay on brand?
It is fine-tuned with QLoRA on your published brand voice, product documentation, and support policy, so the tone and the rules come from your own material rather than a generic assistant.
What reduction in escalations is realistic?
Around 35–45% fewer escalations, from the agent resolving documented tier-1 questions before they reach a person.
Does customer data leave our environment?
No. The model runs in your cloud tenant on weights you own, and conversation data stays inside it.
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