Thinking of building your own AutoQA? See both sides first.
With today’s AI tools, almost any team can prototype software that scores a customer conversation. Running quality management that agents trust, leaders act on and regulators accept, for years, is a different job. Here’s what each route really involves, side by side.
- For contact centre and CX leaders
- Weighing an internal build or a CCaaS add-on
Building it yourself vs buying evaluagent
Six things almost every build plan promises. On the left, what you’ll hear in the meeting. On the right, what it takes to run AutoQA properly, and how we handle it.
· the reality“Looks about right to me.”
Ask an internal build whether it’s accurate and the honest answer is that it looks right. Nobody has written down what “right” means, and nothing is measuring it.
Dave. On Tuesday.
Accurate means it agrees with your QA team
There’s no universal definition of a well-handled call, so accurate can only mean the AI agrees with your own QA team’s judgement. Proving that, and improving it, takes machinery.
- A validation workflow where your QA team confirms or corrects AI results
- Test lists of conversations with known-correct answers
- An agreement score, with the sample size behind it
- Accuracy that improves as your team corrects it, with no extra project to run
“It’s just an API call.”
model: v1 retired → v2 deprecated → v3?
LLM providers retire and replace models regularly. When they do, scores can shift overnight with no error message, and every scorecard needs re-testing before anyone can trust the numbers again.
When the model changes, we re-validate
We run the change control for you. Every scorecard is re-tested against known-correct answers before a new model goes near your scores, and drift reporting shows you what moved and why.
Your QA team carries on coaching. Nobody gives up a weekend.
“1 engineer, 3 months.”
Illustrative: a 300-seat contact centre handling around 80,000 conversations a month. Excludes transcription, which both routes need.
The scoring engine is visible. The rest of the bill isn’t.
Engineering, AI inference, security, reporting and support are shared across our whole customer base, not carried by your P&L alone.
lower cost over three years, around 75% less than building, before counting the value you lose while you wait.
Illustrative example. Your numbers will differ, and we’re happy to model them with you.
“Live by Q2 Q3 Q4?”
First value: somewhere around month 9 to 12
Live in weeks, not months
Value from week four to six, across every conversation, and better every month after. We can show it working on your own conversations within days. If a renewal, a regulatory review or an AI agent rollout is coming, time is the scarcest thing in this decision.
- AI scoring prompt
- A first scorecard
- A demo dashboard
“Ship it!”
A prototype covers what’s above the waterline. It also tends to live in one or two people’s heads. When they move teams or leave, your quality programme loses its owner overnight.
(until Sam leaves)
Most of the work sits below the waterline
QA scores drive coaching, pay and sometimes disciplinary decisions, and your QA record is what you show a regulator. All of this is in place on day one, and kept up for you.
- WorkflowsEvaluation, dispute, calibration and coaching
- Agent experienceVisibility, feedback and engagement
- ReportingFor every level, with drill-downs
- GovernanceAudit trail, version control, human override
- AI operationsAccuracy monitoring, drift, model upgrades
- IntegrationsCCaaS, CRM, helpdesk, WFM, transcription
- SecurityISO 27001, SOC 2 Type II, SSO, pen testing
- InsightConversation Intelligence: the why behind the score
- AI agentsIndependent evaluation of your bots
- PeopleSupport, training and change management
“How hard can it be?”
With a couple of spare engineers and an LLM key, it can look easy. “We have spare engineers” is a cost argument you can win on paper. The ongoing work is what wins it back.
Fridays, mostly.
Funded specialists are stepping back
Well-funded companies whose whole job is building QA software are stepping back from AutoQA, because it’s complex. If they can’t sustain it as their core product, a side project won’t.
evaluagent is doing the opposite. AutoQA is what we build, every day, and decades of contact centre quality experience go into the hidden problems your team would otherwise discover one at a time.
If it started failing your customers tomorrow, who would tell you?
And would they have any incentive to? An internal build that marks its own homework rarely raises its hand.
Pressure-test your planIs your build plan ready? Ten questions to find out.
If any of these doesn’t have a confident, costed answer, the build case isn’t ready yet. Mark each one and watch the bubble.
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Time to valueCan you be live, with 100% coverage, in weeks? What does waiting cost meanwhile?
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AccuracyHow will you know if it starts missing complaints, vulnerable customers or breaches?
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Total costHave you costed engineering, inference, security, reporting and support beyond year one?
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OwnershipWho owns the roadmap and writes the specs for the next five years?
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GovernanceCan you show a regulator how each score was reached, who reviewed it and what changed?
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SecurityWill it pass your own security review: SSO, permissions, audit logs, pen testing?
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AdoptionWho trains and supports QA analysts, team leads and agents, and keeps doing it?
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IndependenceHow will you evaluate your own AI agents without marking your own homework?
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Opportunity costWhich customer-facing projects will your engineers drop to build and run this?
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Early warningIf it started failing your customers tomorrow, who would tell you?
Questions we hear in build vs buy conversations
Can’t we use the QA tool that came with our CCaaS platform?
You can, and plenty of teams start there. Bundled QA tools are built to fill a gap in a wider suite, so teams often end up working around them and drifting back to spreadsheets. You only get one chance to launch AutoQA with your agents, so pick the platform you trust rather than the one you’re handed.
We’ve already built a prototype. Is that work wasted?
No. A prototype is the best requirements document you’ll get. Bring it, along with your build estimate, and we’ll map it against the platform and model the true cost of both routes side by side.
How quickly could we see evaluagent working on our own conversations?
Within days. We can score a sample of your own conversations against your own scorecard, so you can judge accuracy for yourself rather than taking our word for it.
Are the cost figures on this page realistic for us?
They’re an illustrative example for a 300-seat contact centre handling around 80,000 conversations a month, assuming £95k fully loaded per engineer and £110k per AI specialist. Your numbers will differ, and we’re happy to model them with you.
How to burst the bubble of an unrealistic self-build promise
Ten promises that turn up in almost every self-build business case. For each one: what really happens, the argument to make, and the questions to put to your team before anyone commits.
- “It’ll be accurate”
- “Models are just an API call”
- “It’ll be cheaper”
- “Live by Q2”
- “We can judge our own AI agents”
- …and five more
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