Your agents now handle only the calls AI couldn't. Their training has to match.
AI bots and IVR are handling the routine calls. What reaches your human agents is the hardest 10% — complex escalations, frustrated customers, edge cases. The training playbooks built for high-volume, low-complexity workloads aren't shaped for this. Skolarli is.
A paradigm shift in BPO and call centers.
An agent's day was a hundred calls. Most were routine: password resets, balance checks, order status, basic troubleshooting. A handful — maybe ten — were genuinely complex.
Routine calls weren't just throughput. They were the agent's training ground. Hundreds of low-stakes interactions built pattern recognition, conversational comfort, and confidence before facing a hard one.
AI bots and IVR handle the routine. By the time a call reaches a human agent, it's been pre-screened: the bot didn't solve it, the customer is already frustrated, the issue is genuinely complex.
The agent's day is no longer ninety routine calls and ten complex ones. It's a hundred calls of escalations, exceptions, and edge cases. Same agent. Same eight-hour shift. Wholly different cognitive load.
Training designed for high-volume-low-complexity workloads doesn't shape agents for high-stakes-high-complexity ones. Process knowledge isn't enough. Agents need scenario fluency, fast in-call retrieval, and continuous adaptation as call patterns shift.
This is the gap Skolarli is built for.
Training pathways that adapt to each agent, every week.
Your QA team is already scoring calls. Your customers are already leaving feedback. Your dialer is already logging dispositions. Most LMS platforms ignore all of it — agents move through the same modules in the same order regardless of what their actual call performance shows. Skolarli closes that loop.
QA flags an empathy gap on a Tuesday call. By Wednesday morning, that agent's pathway includes targeted scenario practice on de-escalation. Customer feedback on a confusing product explanation surfaces a knowledge gap; the agent gets focused content before the next shift. Personalization isn't a marketing word here — it's the QA team's signals turning into training, automatically.
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Multi-source signal capture. QA scores, customer feedback ratings, dialer dispositions — all feed into per-agent training recommendations.
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Personalized pathway per agent. No two agents see the same training queue. Pathways adjust weekly based on what their calls show.
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Dialer integration via API and webhook. Disposition codes flow in; training recommendations flow back. Native one-click connectors for specific dialers are wired during pilot scoping.
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Operations team dashboard. Visibility into team-level patterns — which scenarios are weak across queues, which knowledge gaps recur, which agents are accelerating.
Practice the difficult call — before it's a real one.
Most "AI roleplay" in the LMS market is text-based scenario branching. Skolarli's is voice-in, voice-out. The AI plays the customer in character — frustrated, escalating, or confused depending on the scenario. The agent responds verbally, the way they will on the real call. The AI evaluates response validity, tone, escalation handling, and SOP adherence in real time.
This matters most for new hires and process-change agents — the cohort facing complex calls without the foundation that hundreds of routine calls used to provide. Voice roleplay gives them repeatable practice against the actual emotional arc of a hard call: hearing the customer's tone shift, finding the words under pressure, learning what works.
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Voice-based, not text-based. Real audio in, real audio out — matching the cognitive load of an actual call, not a typing exercise.
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Validated personas, configurable scope. Irate customer is the anchor persona, validated in production. Additional personas — confused, technical, escalating — configured per pilot.
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Real-time scoring. Empathy, de-escalation, SOP adherence, resolution path — scored per session and feeding back into the agent's pathway.
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Replayable sessions. Agents review their own sessions; supervisors review for coaching context. Patterns surface across teams.
Find the answer in seconds — without leaving the call.
When a call goes complex, the agent's bottleneck isn't motivation — it's retrieval. The right SOP exists. The right product spec exists. They're buried in a knowledge base or wiki the agent can't search fast enough while the customer is talking. SkoAI Coach grounds AI knowledge search in your KB, your SOPs, your product files — and answers in conversational language with citations.
Tenant-isolated — your KB never leaves your AWS Mumbai tenant. No consumer LLM round-trips, no model training on your operations data, no question of where a customer's account information ended up.
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Grounded in your KB and SOPs. Process docs, product specs, escalation matrices — all searchable through natural language queries.
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Citations on every answer. Agents see exactly which document section answered the query — verifiable, defensible to QA review.
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Tenant-isolated · AWS Mumbai. Customer data and operations IP never leave your tenant boundary.
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Same KB powers training and live calls. Knowledge ingested once, reused in roleplay scenarios, agent training, and in-call retrieval.
FAQ.
What does the AI voice roleplay session actually look like?
Can Skolarli integrate with our dialer or QA platform?
What languages does AI voice roleplay support?
How does this work with night-shift agents on US/UK time zones?
Pricing — we have 5,000 agents, what does this cost?
Pick one queue.
We'll show you Skolarli on it.
30-minute conversation. Walk us through one queue — the call mix, the agent count, the dialer and QA stack, the languages you operate in. We'll come back in 48 hours with a numbered pilot proposal scoped to that queue specifically. Pilots run 500–2,000 agents, one queue, one quarter.