CoglyAI - Switch to smarter QA monitoring of your calls
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Discover CoglyAI, the leading AI-powered contact center quality assurance solution. Automatically transcribe and evaluate 100% of your customer calls.
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Cogly AI is a B2B SaaS platform for Speech Analytics and automated quality assurance (QA) for contact centres and regulated players. It automatically transcribes and analyses 100% of an organisation's calls — versus about 2% today in manual control — and returns, for each conversation, a reproducible quality score, a sentiment analysis, objection detection and compliance signals, in under 90 seconds after the call ends.
The central differentiator is the control of conversational data: the core of the audio→text processing relies on a proprietary transcription engine running on a dedicated GPU, with no third-party API; data is hosted and processed in the EU, with GDPR compliance by design (isolation through a dedicated database per client, automatic redaction of sensitive elements, audit log, tooled right to erasure); and the architecture is portable to an EU-sovereign cloud provider — full legal sovereignty is an infrastructure step activatable from the first production contract, not a rebuild.
The product is a complete, production-ready SaaS: an operational end-to-end pipeline, from ingestion to dashboards, with usage-based billing (Stripe metered, per analysed minute). The company is pre-revenue: the commercial launch is under way (founder-led prospecting active on the francophone nearshore, France / Belgium / Portugal planned for the autumn) and the absolute priority is signing the first BPO client.
The commercial strategy targets three segments: BPOs and contact centres (50–500 agents) as the entry point, then insurance and banking as regulated verticals, where exhaustive conversation analysis becomes a tool of proof (IDD, GDPR). The geographic deployment is sequenced: francophone nearshore, then France · Belgium · Portugal, then regulated Europe and international.
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One thing that would make a real difference for our QA team is built-in sentiment trend tracking across agents, so we can spot coaching opportunities before quarterly reviews instead of digging through individual call scores after the fact.
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Maker
@aytengulcaua1n Thank you very much for this insightful feedback. It is precisely because of this proactive approach to management that we designed Cogly AI. You’re absolutely right: analyzing scores at the end of the quarter is like closing the barn door after the horse has bolted. The good news is that our AI pipeline already natively captures overall sentiment and segment-specific sentiment for each call. To meet your specific needs, we can aggregate this data directly into your supervisor dashboard as a sentiment trend line for each agent. This will allow you to instantly identify, week over week, an agent whose performance is slipping or recurring tension in a campaign—even before it impacts their overall QA score. This is a feature we can prioritize and roll out very quickly as part of our proof-of-concept (POC) phase. What do you think?”
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The speech analytics angle looks solid for BPOs. One thing that would make this way more useful for supervisors is real-time agent assist that pops coaching tips or compliance warnings mid-call based on what the AI is picking up, not just post-call scoring. Anything planned on that front?
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Love the focus on turning audio streams into ROI for call centers. One thing that would make this a no-brainer for our team is a built-in coaching workflow that auto-flags rep moments to review and lets managers leave timestamped voice notes right on the transcript.
Replies
One thing that would make a real difference for our QA team is built-in sentiment trend tracking across agents, so we can spot coaching opportunities before quarterly reviews instead of digging through individual call scores after the fact.
@aytengulcaua1n Thank you very much for this insightful feedback. It is precisely because of this proactive approach to management that we designed Cogly AI. You’re absolutely right: analyzing scores at the end of the quarter is like closing the barn door after the horse has bolted. The good news is that our AI pipeline already natively captures overall sentiment and segment-specific sentiment for each call. To meet your specific needs, we can aggregate this data directly into your supervisor dashboard as a sentiment trend line for each agent. This will allow you to instantly identify, week over week, an agent whose performance is slipping or recurring tension in a campaign—even before it impacts their overall QA score. This is a feature we can prioritize and roll out very quickly as part of our proof-of-concept (POC) phase. What do you think?”
The speech analytics angle looks solid for BPOs. One thing that would make this way more useful for supervisors is real-time agent assist that pops coaching tips or compliance warnings mid-call based on what the AI is picking up, not just post-call scoring. Anything planned on that front?
Love the focus on turning audio streams into ROI for call centers. One thing that would make this a no-brainer for our team is a built-in coaching workflow that auto-flags rep moments to review and lets managers leave timestamped voice notes right on the transcript.