Agent onboarding at Simply Contact, a leading European customer support outsourcing company, dropped 30 percent after the company built AI call simulation into training. The gain is measurable, it happened autonomously, and nobody was replaced for it to work.
Most writing about AI in customer service centres on the front line: chatbots, deflection rates, tickets that never reach a person. Those matter. They are also the smallest part of what AI does inside a working support operation.
Simply Contact has run outsourced support since 2013. It now has more than 850 agents across eight European countries, handling over 10 million requests a year in 30-plus languages for Wizz Air, Bolt, Metro, Deloitte, Yves Rocher, and Ditto Music. The figures below come from that operation.
Five applications, ranked by return
| Application | Where it sits | Measured result |
| AI call simulation in training | Before the agent goes live | Onboarding 30% faster, FCR readiness roughly doubled |
| Automated quality review | After the interaction | 100% of contacts scored instead of a 1 to 2% sample |
| AI translation on async channels | Non-voice, documented questions | Coverage extended from 30 to 50-plus languages |
| Intent and volume monitoring | Aggregate contact data | Product problems flagged in hours, not days |
| Routing and in-conversation retrieval | During the interaction | Fewer transfers, lower handling time |
The order matters. Everything above the fourth row happens where a mistake costs nothing.
Training agents against synthetic customers
New agents used to learn difficult conversations on real ones. A trainer can roleplay, but a trainer gets tired, plays the same three personalities, and cannot do it in Bulgarian at 9 pm.
Simply Contact generates synthetic customers instead. Angry, confused, in a hurry, calling about a cancelled flight or a declined payment, in more than 50 languages.
What the simulation layer gives them:
- Unlimited repetition of the scenarios that actually go wrong
- 50-plus languages of practice without hiring a trainer per market
- Full scenario replay against the recording, so an agent can hear where the call turned
- Trainer time moved from performing the angry customer to reviewing the agent
- 20 permanent trainers covering a headcount that would otherwise need far more
Onboarding time fell 30 percent. Readiness at go-live roughly doubled. The reason is dull: repetition works, and AI made repetition cheap.
The clearest test came on a mobility and delivery account that grew from 12,000 to 110,000 monthly requests in six months, across four languages, ending at 250 agents. Hiring was never the constraint. Getting 250 people ready to handle a driver dispute at 2 am was.
Extending language coverage past the hiring plan
Simply Contact’s teams cover 30 languages with native or near-native agents. Demand does not stop at 30. For markets too small to staff, the company runs AI translation on non-voice channels with human review before anything sends.
The split is what makes it work:
| Interaction type | Handled by | Reason |
| Email and tickets, documented questions | AI translation, human review before send | High volume, low ambiguity, cheap to correct |
| Phone calls | Native or near-native agent | No post-edit loop exists in a live call |
| Regulated correspondence (KYC, claims, patient contact) | Native agent | Wording carries legal weight and needs an audit trail |
| Social and community management | Mixed, by market volume | Public, so review requirements are higher |
Read that way, translation models are not a substitute for language teams. They are the reason a language team can exist for a market that used to be written off.
The economics of multilingual customer support change once coverage stops being a hiring decision. A market sending 200 tickets a month cannot carry a dedicated agent. It can carry a translation layer and a shared reviewer, and that is the difference between serving a market and ignoring it.
Reviewing every interaction instead of one percent
Manual quality review samples one or two percent of contacts. On a large account that is statistically fine. On a six-agent language team, it is guesswork, and a systemic problem can run a month before a reviewer pulls the wrong call.
Automated review changes the denominator. Every interaction gets scored for sentiment, resolution, and policy adherence. The flagged minority goes to a human reviewer who decides what actually happened.
What that buys:
- Correction inside a week instead of inside a monthly report
- Coverage on small language teams, where sampling never worked
- Reviewer hours spent on the twelve conversations that deserve a human hour
The model is not the judge. It is the thing that finds the calls worth judging.
Catching trends before they become tickets
A product release ships on Tuesday. By Wednesday afternoon, contacts mentioning one screen are up 400 percent in three markets, all on Android. A person reading tickets finds that on Friday.
Watching intent distribution finds it in hours. Support has always held this information. What changed is extraction speed and how confidently it can be handed to a product team.
Routing and retrieval
Two smaller applications, both worth the effort.
Routing on language plus intent plus account value, decided at first contact. Transfers are the most reliable driver of customer irritation Simply Contact measures, and this removes most of them.
Retrieval during the conversation puts the relevant passage on screen with a source, so the agent stops searching. Handling time drops and new agents stop guessing.
What it looks like on live accounts
| Account | Scope | Result |
| European airline, since 2020 | Multilingual voice and non-voice, seasonal peaks | 80% of calls answered inside 35 seconds, AHT down 30%, 85% agent utilization |
| Music distribution platform | Multilingual chat support for artists | CSAT from 51% to 88%, responses per hour from 3.5 to 8 |
| Mobility and delivery platform | Four languages, scaled in six months | 110,000 monthly requests, 250 agents, 10,000 resolved daily |
The second row needs a caveat. Most of that CSAT movement came from hiring agents who were working musicians and understood the product, not from a model. AI made them faster. It did not make them credible.
Segment the reporting or none of this is visible
An operation running 30 languages will report a healthy composite CSAT while one language team sits ten points below everyone else. The average hides it.
Simply Contact reports by language, channel, and market. That segmentation is also how the AI layer gets audited:
- Translation-assisted tickets scoring two points below native-agent tickets on the same intent: fixable gap
- Eight points below: that intent belongs with a person
- Sentiment falling in one language while volume holds: usually a script problem, not a staffing problem
Where to start
The sequence recommended to teams deciding where to put effort first:
- Training. Fastest return, lowest risk. A synthetic customer having a bad experience costs nothing.
- Quality review. Cheap coverage on the teams sampling currently misses.
- Language coverage. Opens markets that were previously unservable.
- Customer-facing automation. Better built last, once agents and the knowledge base have been tested.
That is close to the opposite of how support automation usually gets funded. Chatbots go first because they are visible. The internal work returns more, and it makes the customer-facing layer better once a team gets there.
Author Bio: Zubair Rafique is a SaaS SEO Expert, Outreach Specialist and UGC Content Writer with extensive experience helping B2B SaaS, technology and AI brands grow their online authority through strategic link building, digital PR, and content-driven SEO. He specializes in SaaS link building, high-impact outreach campaigns and scalable organic growth strategies that help businesses earn authoritative backlinks, improve search visibility and generate sustainable long-term results. Drawing on hands-on industry experience and proven outreach methodologies, Zubair shares practical insights that help SaaS companies compete and succeed in organic search.
LI: https://www.linkedin.com/in/zubair-rafique9827/
