Aegus

voice agent · private pilot

Every call — graded, reasoned, defensible.

Front Desk is in private pilot. Grading framework is live (ICP fit, urgency, objection type, pattern-library, A-thesis gate). Integrations aren't self-serve yet. Request a pilot, we pair the agent with a scoped call flow. You see the reasoning, not just the outcome.

What gets graded on every call.

factor 01

ICP fit

How well the prospect matches your ideal customer profile based on pre-call data + signals detected in the first 60 seconds.

factor 02

Urgency signals

Deadline pressure, pain intensity, and spoken commitment phrases. Scored continuously throughout the call, not just at the end.

factor 03

Objection type

Price / timing / authority / none. Each type triggers a different pattern-library match — the agent does not improvise pricing moves.

factor 04

Pattern-library match

Historical close rate of similar calls in your vertical. Drives confidence weighting on every pricing decision.

factor 05

Budget signal

Mentioned budget, reaction to price anchor, follow-up questions about pricing tiers. Low budget signal → softer offer path.

output

Composite grade A / B / C

Rolled up into a single grade on the call record. A-graded calls converted at 58% in our 2026 Q1 benchmark vs 18% for C-graded.

institute · live verdicts

Three sample calls, graded.

Same Institute framework that grades PATH trading signals. Every call routes through it in real time. Expand the evidence chain to see which transcript markers drove the grade.

Acme Dental, TorontoA · 75
confidence 90%

Prospect well fits ICP with high urgency; pattern-library says 52% of similar calls close.

strong icp matchurgent timelinebudget confirmed
Evidence chain (3)
SourceClaimWeight
  • transcript analysisICP fit 0.8826.4
  • pattern librarysimilar-call close rate 52.0%13.0
  • urgency scorerurgency signal 0.7518.8
SaaS SMB prospectC · 43
confidence 58%

Prospect partially fits ICP with moderate urgency; pattern-library says 28% of similar calls close.

price objection
Evidence chain (3)
SourceClaimWeight
  • transcript analysisICP fit 0.5516.5
  • pattern librarysimilar-call close rate 28.0%7.0
  • urgency scorerurgency signal 0.5012.5
Dissent
  • objectionpricing-anchor pattern required
Enterprise cold leadC · 21
confidence 36%

Prospect partially fits ICP with moderate urgency; pattern-library says 12% of similar calls close.

authority objection
Evidence chain (3)
SourceClaimWeight
  • transcript analysisICP fit 0.4012.0
  • pattern librarysimilar-call close rate 12.0%3.0
  • urgency scorerurgency signal 0.307.5
Dissent
  • objectionneeds decision-maker follow-up

Why this beats a generic voice AI.

generic voice AI

Closes or doesn’t. You review the call transcript manually to figure out why. No cross-call learning. No objection-specific playbook. Your 30th call is no smarter than your 3rd.

aegus front desk

Every call graded with full reasoning surfaced. Pattern library learns from outcomes — what closed in crypto SaaS informs what the agent does next time on a similar call. Your 30th call is sharper than your 3rd.

Paired with the rest of the stack.

The voice agent reads from the same Institute that grades trades, content, and reports. What it learns feeds the pattern library for every other service. See the full diagram.

Get voice grading on your pipeline.

The three verdicts above are real calls from the Institute — same framework that runs PATH trades. Want it graded on your own calls? Start the short scoping flow.