Mach-1 by AionCX
The end-to-end audio-to-analytics pipeline for developers. Send audio. Get speaker-labeled transcripts, structured intelligence, evaluations and analytics through one integrated pipeline. Mach-1 handles the processing so you can build the products that use it.
Build a quality platform that explains its scores. A customer intelligence product that reveals recurring problems. An AI assistant that can work with what was promised. Mach-1 turns conversations into the structured information those products depend on.
Carry audio through transcription, understanding, evaluation and analytics in a connected processing system.
Work with structured findings, criteria and source references instead of parsing a different prose answer at every step.
Define the classifications and evaluations your use case needs. Apply the relevant analysis to each conversation.
Illustrative example
Follow a conversation from recording to an individual finding, and then into the patterns across a collection of calls.
00:04 · Customer: “My delivery is booked for Monday. Can you move it to Thursday?”
00:12 · Representative: “I can request Thursday, but the delivery team has to confirm it.”
00:25 · Representative: “I’ve submitted the request. I’ll email you an update by 3 p.m. tomorrow.”
00:04 · Customer: “My delivery is booked for Monday. Can you move it to Thursday?”
00:12 · Representative: “I can request Thursday, but the delivery team has to confirm it.”
00:25 · Representative: “I’ve submitted the request. I’ll email you an update by 3 p.m. tomorrow.”
Contact reason: delivery-date change. The customer requested Thursday instead of Monday. The result is pending confirmation, not a confirmed reschedule.
| Field | Illustrative value |
|---|---|
| Requested date | Thursday |
| Original date | Monday |
| Commitment | Email an update by 3 p.m. tomorrow |
| Business outcome | Pending delivery-team confirmation |
The display is illustrative output, not a confirmed public API schema.
The representative met the sample criterion by explaining the confirmation dependency and giving a specific next step. The delivery date remains unconfirmed. Customer outcome and representative performance are separate.
Group applicable findings across calls, then inspect individual records and their supporting words. Counts retain their period, coverage and definitions. This example supplies no performance benchmark or production service commitment.
Classification establishes what happened. Evaluation applies the relevant standard. Analytics brings the results together. Mach-1 carries the conversation’s meaning and evidence through that path.
The reason for contact, the customer’s request and the stated outcome remain available to the stages that need them.
A delivery change and a cancellation can require different evaluations. Applicability determines what gets assessed.
Move from an aggregate finding to the conversation and the supporting words.
No. The output is illustrative. Service availability, interface schemas, integration details and performance commitments are confirmed when your use case is scoped.
No. A representative can meet an applicable criterion while the customer’s requested business action remains pending. Mach-1’s example keeps those results distinct.
Bring the audio, the questions and the product you want to create. Put Mach-1 behind it.
Build with Mach-1