What does Auralis Audit actually score?

Every support conversation — AI chats from Autopilot, calls handled by Answer, and your human agents' tickets and calls — against criteria you define: accuracy, resolution, tone, and process compliance. Nothing is sampled; every conversation gets a score, so patterns and outliers can't hide in unreviewed volume.

How is this better than our manager reviewing tickets?

Coverage and consistency. A manager can sample a handful of conversations and apply a rubric a little differently each day. Audit scores all of them, the same way, every time — and hands your manager the flagged conversations worth human attention, with evidence attached. Review time turns into coaching time.

Does it score the AI's conversations too?

Yes — bot and human are held to the same standard. If you let AI resolve up to ~70% of requests automatically, you need proof it's answering well. Audit provides it, and when it finds a wrong or weak answer, the fix goes into the Knowledge Center once and applies everywhere.

How does QA protect our contract renewals?

Renewals ride on support experience between hardware sales. Audit surfaces souring sentiment and recurring problems on an account while there's still time to intervene — so your first warning is a flagged trend, not a cancellation notice. Managers walk into renewal conversations knowing the account's real history.

Will agents feel like this is surveillance?

It lands as fairness. Every agent is scored on all their work by the same criteria — not judged on whichever ticket a manager happened to pull. Coaching becomes specific and evidence-based, strong performance becomes visible, and the checklist anxiety of random spot-checks goes away.

AI Support QA & Audit for Dealer Teams

Manual QA means you're grading a guess

Here's how support QA works at most dealers: a manager, when time allows, pulls a few tickets or listens to a few calls, scores them against a checklist, and hopes they were representative. They almost never are.

A busy dealer desk handles far more conversations than any manager can review, so the sample is tiny and the blind spots are huge. The agent who's been misquoting contract terms for a month? Missed. The account whose tone has been souring for a quarter? Invisible until the cancellation call. For a dealer, that's not an abstract quality problem. Support is the product between hardware sales — it's what your contract renewals ride on.

How Audit works, step by step

Every conversation flows in automatically. Chats from Autopilot, calls from Answer (already transcribed and sentiment-scored), and your human agents' tickets and calls. Nothing is sampled — everything is scored.
The AI scores each one against your standards. Accuracy, resolution, tone, process compliance — the criteria you define, applied the same way to conversation one and conversation one thousand.
Bot and human are held to the same bar. One scorecard across AI and agents, so you can see exactly where automation performs and where your team shines — or slips.
Patterns and outliers get flagged. A recurring wrong answer, a coaching gap on one agent, an account trending frustrated — surfaced for your support manager, with the conversations attached as evidence.
Fixes flow back into the system. Wrong answers become Knowledge Center corrections; coaching moments become specific, evidence-based conversations instead of vague feedback.

From sampling to certainty

Manual QA tells you how a few conversations went; Audit tells you how support went. Your managers stop pulling random tickets and start acting on flagged ones — coaching from real examples, fixing knowledge gaps at the source, and stepping into at-risk accounts before the renewal conversation gets hard.

It also closes the loop on automation. If AI resolves up to ~70% of requests automatically, you should be able to prove it's doing the job well. Audit is that proof — the same scrutiny on the bot as on your people — and it's how a lean team sustains roughly 5x agent productivity without quality drifting.

The modules doing the work

Audit — automatic scoring of every conversation, bot and human, against your standards.
Answer — supplies call transcripts and sentiment for phone QA.
Autopilot — the AI chat conversations held to the same bar as humans.
Assist — turns findings into better agent replies from governed content.
Knowledge Center — where corrections land, so a fixed answer stays fixed everywhere.

Accuracy starts with your e-automate data

QA catches wrong answers; governance prevents most of them. Auralis answers from the Knowledge Center, which combines your approved content with dealer data — contracts, meter reads, supplies and device status, ticket status — synced read-only from e-automate, plus device monitoring context from Printanista and FMAudit. Audit then verifies the conversations that data powered. Details on the e-automate AI assistant integration page.

Know how every conversation went

Bring your current QA scorecard to a demo and watch Audit apply it to every conversation, not a sample.

Support QA & Audit for Dealer Teams