Structured scores: intent, objections, next step

From each recorded conversation, the after-call AI extracts structured fields: lead intent, criteria captured, price band, timeframe, objections, the next step, and flags like hot or wrong person. The fields give you a scannable signal for what to review. A score is a prompt to look closer, never a prediction that a sale will close.

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Lead detail view with the AI intent badge, structured score fields and the call summary — Real product interface. Sample data, fictional people and numbers.

How it works.

4 steps, in the order they happen during a session.

1
Fields, not a paragraph
The model reads the recording and returns discrete fields: intent, geography, criteria, price band, timeframe, objections, next step and flags. Scanning a queue beats reading ten paragraphs.
2
Flags surface the edge cases
Flags include hot, wrong person, do-not-call signal and Spanish-speaking agent needed. A do-not-call signal is a prompt to check the record, and the operator's disposition remains what actually governs.
3
Score what to review
Sort and filter by the fields to choose calls worth hearing. Ask what the displayed values mean, then compare them against the test recording before anyone acts on them.
4
Keep the source close
A field is the model's reading of a conversation, not a fact checked elsewhere. Where a value decides a consequential action, open the transcript or the recording first.

The specifics.

What the product does today. Included on every plan, from Solo at $49 per seat.

Intent field: Buyer, seller or buyer and seller, read from the conversation.
Objections quoted: Stated objections come back as quoted phrases, not categories the model invents.
Next step: The agreed follow-up, such as a callback time, captured as a field.
Signal flags: Hot, wrong person, do-not-call signal, Spanish-speaking agent needed.
Engagement score: A number shown with the summary; read it as a review signal.
After the call: Scoring runs on the recording afterward, never live on the line.

What the score is, and what it is not

The structured score is a set of fields the model fills from the recorded conversation: lead intent, geography, criteria captured, price band, timeframe, objections, next step and flags. On a real production call, this looked like intent buyer, criteria of water-access single family with three beds and two baths, two objections quoted from the conversation, and a next step of a 5PM callback the same day. The values came from that call's own record.

What the score is not: a probability of closing, a measure of operator quality, or consent evidence. A high value does not make a lead more yours, and a low one does not settle an argument. Use the fields to decide what deserves your ears and your follow-up time.

The score can disagree with the operator's note or the summary. Resolve the difference against the recording rather than picking the convenient version, and keep examples of repeated inconsistencies for support to investigate.

Turning fields into reviews

The fields earn their keep at scale. A manager picking calls to review can sort by intent, filter to flagged conversations, and find the wrong-person calls or the do-not-call signals in minutes. A covering teammate inherits a queue with the next step already attached instead of a stack of unexplained audio.

Write your review questions before you open anything. Did the operator state the purpose. Was the next step actually agreed. Does the summary preserve a refusal rather than converting it into interest. The fields tell you where to look; the audio tells you what happened.

Where a score seems wrong, record the call reference and describe the discrepancy rather than overwriting it silently. Repeated examples help investigate model behavior. Scoring is included on every plan, with no per-call charge.

Questions about ai call scoring.

Straight answers. Anything else: email support.

Does a high score mean the lead will buy?

No. The score is a signal about the conversation as the model read it. It does not predict a sale, establish permission or measure the operator. Use it to choose which calls to review and which follow-ups to prioritize. What actually happened lives in the recording, the transcript and the operator's note, and that is where consequential decisions get checked.

What fields does the score include?

Lead intent, geography, criteria captured, price band, timeframe, objections, the next step, and flags for hot, wrong person, do-not-call signal and Spanish-speaking agent needed. An engagement score shows with the summary. The real-call example published on this site lists the actual values from one production call, so you can see the shape before you trust the mechanism.

Can scoring run while the call is live?

No. The AI layer works on the recording after the call ends. Nothing it produces reaches the lead, and nothing it computes decides who gets dialed next. The queue follows the list, filters and caps you configured. After-call is a deliberate design choice, so the conversation stays between two humans.

What if a field contradicts the operator's note?

Resolve it against the source. Open the transcript for the wording and the recording for the tone and timing. The operator was on the call; the model read a transcript of it. Either can be wrong, so the audio settles it. Where you see repeated inconsistencies, keep the call references and send them to support rather than working around them quietly.

Is scoring included in every plan?

Yes. Solo at $49 per seat per month, Team at $149 per month for three operator seats and Studio at $399 per month all include transcripts, structured scores and summaries. There is no AI upgrade tier and no per-call charge. Plans differ by operator seats and support, not by feature access.

Works with.

Try it on a real session.

Your 14-day trial runs on a Telnyx sandbox with test numbers; nothing reaches a real person. A human provisions the environment. The 14 days start when it is ready and we email you. No credit card is required. On conversion, you connect your own Telnyx account and real calling lists.

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Structured scores: intent, objections, next step

How it works.

Fields, not a paragraph

Flags surface the edge cases

Score what to review

Keep the source close