Where A.S.T.R.A. Gets the Call Transcript From

From whichever tool already turns a customer call into text inside your stack: a conversation-intelligence platform like Gong, a video tool like Zoom that generates its own recording and transcript, or product and usage context from a tool like PostHog that fills in what happened around the call. A.S.T.R.A. reads that transcript once it exists. It is the last step of the post-call execution loop's input side, not the first: something upstream of Mindlyft has to capture the conversation before the commitment taxonomy can classify anything in it.

No. Mindlyft is not a call-recording or transcription product, and it does not join calls, dial in, or capture audio on its own. That job stays with whatever tool your team already uses for it. A.S.T.R.A.'s job starts after the transcript exists: reading it, classifying the commitments in it against the taxonomy, and drafting the CRM updates, follow-ups, tickets, and handoffs those commitments imply. Keeping recording and execution as separate concerns means a team can change its call-recording vendor without touching how post-call execution runs on top of it.

Gong, Zoom, and PostHog are the confirmed context sources feeding A.S.T.R.A. today. That list is not necessarily exhaustive of what a sales or CS team might already run, Chorus, Fireflies, and Otter are common tools in the same conversation-intelligence category, but connecting one of them is a question of current integration coverage rather than something this page can promise. If your team runs a transcript source outside the confirmed list, the direct way to find out whether it is already supported, or whether adding it is a reasonable request for the queue, is to raise it on a Talk to us call rather than assume either way.

Post-call execution can still run as long as a transcript exists somewhere, the specific vendor matters less than having one at all. Plenty of teams already have a transcript sitting in a tool nobody thought of as a transcript source, a video-conferencing tool's own recording, a dial-in service's call log, a notetaking app a few reps use informally. The practical question to answer first is not which named tool shows up on this list, but whether any tool in the stack is already capturing calls as text. If the answer is yes, confirming whether that specific tool connects is worth a direct conversation; if the answer is no, the gap is upstream of anything A.S.T.R.A. can read.

Then there is nothing for the execution loop to classify commitments from, because the loop starts from a transcript, not from a rep's memory of the call or a quick line typed into a CRM note afterward. A commitment taxonomy applied to "the rep said it went well" has nothing concrete to work with. This is a real limitation worth stating plainly rather than working around: post-call execution automates the drafting that follows a captured conversation, it does not reconstruct a conversation that was never captured in the first place. A team with calls that mostly go unrecorded has a recording gap to close before post-call execution has anything to run on.

No. A.S.T.R.A. reads the transcript where it already lives, inside the conversation-intelligence or video tool's own account, the same way it reads and writes inside your Salesforce, HubSpot, or Jira. Reading a transcript to classify the commitments in it is not the same as copying that transcript into a Mindlyft-hosted database. The read path is broad, so A.S.T.R.A. can pull context from several sources to classify a commitment accurately, but the transcript's system of record does not change just because A.S.T.R.A. looked at it.

The transcript is the input to the commitment taxonomy, the fixed set of obligation types A.S.T.R.A. looks for: a CRM update owed, a follow-up owed, a ticket owed, a handoff owed, a risk flag. Each commitment it finds carries the line of the transcript that justifies it, which is what shows up later as the evidence behind a drafted action in the review queue. The transcript itself is never the thing a reviewer approves, the drafted action is, but every drafted action traces back to a specific line in a specific transcript, which is what makes the resulting write auditable rather than a black-box guess.

Yes, and it should. A clean, well-labeled transcript from a dedicated conversation-intelligence tool generally produces clearer commitment classification than a rough auto-caption with no speaker labels. That does not mean a lower-quality transcript gets skipped outright, but it does mean the confidence behind a given commitment, and therefore what gets drafted versus flagged for closer review, tracks the quality of the source material. What a messy or ambiguous transcript specifically does to classification and drafting, including when A.S.T.R.A. holds back from drafting at all, is covered on its own page rather than here, since it is a big enough question to answer properly.