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You can build the core of a Gong-style conversation intelligence product on three PyAI APIs: Hear batch jobs transcribe every call with speaker separation, Recap turns each finished call into a summary with action items and structured fields, and Trace scores eligible calls against compliance rule packs. You keep the transcript, the metrics, and the customer relationship in your own product, and you pay per call instead of per seat. Gong is the better buy when you want a finished revenue-intelligence suite: deal boards, coaching workflows, forecast rollups, a polished UI your sales team lives in. Building on PyAI makes sense when conversation data is a feature inside your product (a CRM, a dialer, an agency platform, a vertical tool) rather than a tool your team logs into.

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What you’re building

Each enabled path produces a separate artifact: a diarized transcript (with SRT/VTT if requested), a structured Recap, and, for an eligible Trace path, a compliance scorecard with a tamper-evident audit hash.

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The PyAI stack for this build

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Build order

1

Transcribe your call archive

Submit recordings to POST /v1/transcription/jobs and receive results by signed webhook or polling. Stereo telephony recordings (one party per channel) give the cleanest speaker split; model-based diarization covers mono. Compare batch and realtime metering on the pricing page before planning a large archive. The full, runnable pipeline is in the conversation-intelligence guide.
2

Compute the metrics your users ask for

Talk ratio and call signals ship on the typed Recap record. Do not recompute them from the transcript unless you are building a metric Recap does not return.
3

Turn on Recap for summaries

Recap fires on call end: summary, action items, and structured fields you define, with no bot to invite to anything. For custom extraction (deal stage, qualification fields, your own schema), follow the post-call extraction guide.
4

Add Trace if your users are regulated

Sales calls in healthcare, finance, or collections need disclosure and PII handling. Trace scores eligible calls against TCPA, HIPAA, PII, and brand-voice packs and gives you an exposure dashboard. Async Hear jobs can request trace: true, but that single-pass path cannot be combined with channel: true or diarize: true. If an archive requires both speaker separation and Trace scorecards, confirm the supported pipeline before promising both artifacts. See the Trace guide.

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When to buy Gong instead

Buy Gong (or a similar suite) when the buyer is a sales leader who wants coaching dashboards, deal inspection, and forecasting out of the box, and when per-seat pricing is acceptable. Build on PyAI when you are embedding call intelligence in a product you sell, when you need the raw transcripts and metrics in your own database, or when per-seat pricing breaks your margins at scale.

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FAQ

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Is PyAI a Gong alternative?

PyAI is not a finished revenue-intelligence suite; it is the API layer you build one on. If you want the finished suite, buy it. If conversation intelligence is a feature inside your product, PyAI gives you the transcription, summarization, and compliance scoring primitives.

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Can I analyze calls that did not happen on PyAI?

Yes. Hear batch jobs accept recordings you upload or point to by URL, so calls from any carrier, dialer, or conferencing tool can be transcribed and analyzed.

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Do I need my own LLM?

Not for summaries and action items: Recap produces those natively. For fully custom metrics and free-form extraction you define, the pipeline hands you a clean diarized transcript and you run your own model over it.

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What does it cost to build this on PyAI?

Every piece is usage-based (per minute of audio, per call), with no seats and no platform fee. Current rates are on the pricing page.

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Start building

Get an instant sandbox key (no signup) and run the full pipeline in the conversation-intelligence guide, or scaffold it in one command: