Product updates

The missing piece of the AutoQA puzzle – why Context Engine changes everything

There’s a question quality teams have been asking for years: ‘Great call, but… was that the right answer they just gave the customer?’

Not whether they were empathetic. Not whether they followed the right structure or hit the right tone. Whether the information they gave was actually correct – based on company policies, products, procedures.

It sounds like a basic thing to measure. It has been, until now, almost impossible to automate.

The problem with standard AutoQA

The last few years have transformed what quality teams can do. Automation has taken coverage from a handful of sampled calls to every single interaction. AI scoring has brought a level of consistency that manual evaluation never could. Teams have more data, better insight, and more time to act on what they find.

But through all of that progress, one limitation remained.

A conversation can score well on tone, empathy, structure, and compliance… and still end with a customer walking away with the wrong information. Most QA tools assess how a conversation was handled. They don’t assess whether the answer was right, because they don’t know what the right answer was supposed to be.

In regulated industries, that gap can have real consequences. An agent who quotes the wrong policy terms, communicates incorrect renewal steps, or misrepresents a promotion isn’t failing a tone metric. They’re creating a complaint, a regulatory risk, or a customer who leaves because they’re convinced they were misled.

And with manual QA covering a fraction of interactions, most of those conversations are never reviewed at all.

Why accuracy has been hard to crack

The challenge isn’t actually the AI. Modern Large Language Models are capable of making nuanced quality judgements, but without business knowledge, they just don’t know if something is correct.

And of course, that varies. Product, policy version, customer type, or even specific timely promotions affect whether an answer is right. Generic criteria can’t tell you whether a renewal was processed correctly for a customer on comprehensive cover, or whether an agent quoted the right baggage allowance for a route.

That knowledge lives inside your business – and until it’s connected to your evaluations, the question of whether agents got the answer right stays permanently in the manual review pile.

Introducing Context Engine: your business knowledge connected to your QA

We’ve been weighing up this problem for a while. The result is Context Engine; a new capability inside evaluagent’s AutoQA that lets you upload your own internal documents so the AI evaluates conversations against your business knowledge, not generic quality criteria.

How it works

You upload documents – SOPs, policy guides, product catalogues, compliance procedures – directly into evaluagent. Some of those can be housed as relevant company information, or, they can be attached to specific line items on your scorecards via the Knowledge Vault. When the AI evaluates a conversation, it references the relevant knowledge and assesses whether the agent’s response holds up against it.

That unlocks is a category of evaluation that simply wasn’t possible to automate before:

These aren’t new questions. QA managers have always wanted to answer them. They’ve just always needed someone who knew the business well enough to do it.

What we learned in beta

Before launch, we ran a beta programme with a group of customers testing Context Engine on real interactions.

Teams identified the value almost immediately. Once documents were uploaded, evaluations that had previously needed manual review were running automatically. The feedback has been overwhelmingly positive, with customers reporting back immediate differences in accuracy of scoring – from compliance and vulnerability, to policy accuracy.

The thing that’s most interesting here is that the value compounds. Every document you add makes evaluations more accurate. Every policy update keeps the AI current. The more you put in, the more useful it becomes.

And the sentiment goes beyond the beta. On seeing Context Engine for the first time, our customer Petsure immediately saw how it would change their QA: “The Knowledge Vault is taking things to a very different level. It’s bridging the gap we’ve always had between automated and manual QA, that critical assessment of whether agents are actually doing and saying the right thing.”

What this means for QA

Quality teams have spent years building programmes that measure how agents speak to customers. The natural next step is measuring whether what they say is right – and finally, QA and knowledge bases can be easily connected with evaluagent.

For teams where one wrong answer can mean a complaint, a regulatory finding, or even customer churn, that represents far more than a marginal improvement. It’s a significant shift towards better customer experiences and more meaningful scoring.

Context Engine is available as part of evaluagent’s AutoQA from today. If you want to see how it works, join us for a live open demo on 6 May at 3pm (BST).

6th MAY, 3PM (BST)

AutoQA and your knowledge base, finally connected

See Context Engine in action! Our Product team will be on-hand to demo how this new release can bring accuracy to your QA, and get you closer to great customer experiences.

Register now