Turn every call
в signals you can trust.

A different approach to conversation intelligence.

Conversation intelligence software analyzes customer conversations, sales calls, and support calls to surface what happened and why it matters: deal risk, customer sentiment, competitor mentions, and compliance gaps that would otherwise sit unreviewed in a recording. Revenue, customer experience, and RevOps teams use it to coach reps, protect renewals, and catch the calls a 2% QA sample would never reach.

Why conversation intelligence stalls at the platform level

Platforms like Verint, Clarabridge, NICE, CallMiner, and TalkIQ built real category-defining tools here, and each does enterprise-scale call analysis well: sentiment scoring, keyword spotting, compliance flags. The limit for most teams is not the analysis, it is the deployment. These platforms are built for call centers with dedicated analytics teams and long implementation cycles, not a revenue team that wants its own signals live this quarter. A manager with forty calls a week and a real pipeline runs out of patience long before the rollout finishes.

Reading the call, not just the transcript

Speak AI reads every call the way an experienced sales manager would, at machine speed. Each call is transcribed in your language, with 100+ supported, and then the delivery itself is read alongside the words: tone, pace, and hesitation, not just the transcript text. Your deal-risk criteria, your competitor list, and your buying-signal taxonomy are applied consistently across every call, and the results land as structured fields your CRM and dashboards can use.

Then the questions start. Ask across your entire call library with AI chat, running the same follow-up questions a sales manager once asked one call at a time, now native across recordings with ChatGPT, Claude, and Gemini built in.

What teams ask their call data

• “Which calls this week mentioned a competitor by name?”
• “Show me every call where the prospect raised a pricing objection.”
• “Which renewal calls this month show churn language?”
• “What is the most common reason deals stall after discovery?”
• “Summarize every feature request mentioned on sales calls this quarter.”

From a folder of recordings to a signal feed

The result is a live signal feed instead of a folder of recordings nobody has time to replay. Deal risk and churn language surface the day the call happens instead of at the next QBR, and панели, которые вы можете настраивать и белой этикеткой track those signals over time, so this month’s pipeline risk is measured against last month’s. One legal intelligence firm put its call volume through this workflow and обработано более 5 100 часов и сэкономлено $700K.

And because conversation intelligence rarely stops at the call, the same engine, running on Claude, ChatGPT, and Gemini, connects your calls to оценка звонков и коучинг во всех беседах вашей команды.