Introducing Mach-1

Most applications that work with conversations end up rebuilding the same infrastructure:

• ingest the recording or transcript
• transcribe audio and identify speakers
• organize the conversation
• extract the information the application needs
• evaluate it against relevant criteria
• retain the supporting evidence
• normalize the result across different sources
• make the whole thing reliable and economical enough to run at scale

We needed all of that for AionCX, so we built Mach-1.

Mach-1 is the AI services layer behind AionCX, available to developers through APIs and MCP. It handles the work between a conversation and the structured intelligence an application can use.

From a conversation to something useful

A transcript is only the starting point. Different applications need different information from the conversation.

A sales application may need:

• prospect requirements
• objections
• commitments
• open questions
• next steps

A support application may need:

• reason for contact
• requested action
• resolution
• repeat-contact indicators
• escalation or grievance signals

A quality or training application may need:

• evaluation against defined criteria
• behaviors demonstrated or missed
• supporting excerpts
• areas for coaching or practice

Mach-1 brings transcription and analysis into the same pipeline. Recorded audio can produce a speaker-attributed transcript with timestamps, while an existing transcript can enter the pipeline directly.

From there, Mach-1 can extract, classify, evaluate and organize the conversation into structured results.

The evidence stays with the result. If Mach-1 identifies a commitment to send a proposal on Friday, the application can retain the part of the conversation that supports it. If an evaluation identifies a failed behavior, the criterion and evidence remain attached.

That matters when a person reviews the result, and it matters even more when another AI system consumes it.

Use the conversations you already have

Conversation data rarely starts in one place.

It may come from:

• a contact center platform
• a meeting application
• object storage
• a voice agent
• a direct upload
• an existing transcription service

Each source brings different identifiers, timestamps, participants, formats and metadata.

Mach-1 brings those inputs into a common processing pipeline while retaining where the material came from and the context attached to it.

A customer can connect the systems they already use. A developer building something new can submit audio or transcripts directly. Both receive a consistent result they can build around.

Define what matters to your product

The intelligence needed from a conversation depends on the product using it.

A sales application may care about pricing questions, buying signals and follow-up commitments. A research product may care about recurring themes, product feedback and the passages behind each finding. A voice-agent platform may care about resolution, containment, escalation and policy adherence.

Mach-1 lets developers define the information, classifications and evaluation criteria their application needs.

It also preserves distinctions that matter:

• something explicitly stated in the conversation
• something inferred by a model
• something reported by a participant
• something independently confirmed
• a criterion that passed
• a criterion that failed
• a criterion the available evidence could not establish

Those distinctions give the application better control over what can be displayed, what should be reviewed and what can drive another action.

Evidence and provenance are part of the result

AI is creating more of the data used to run businesses:

• sentiment
• intent
• QA findings
• commitments
• customer risk
• classifications
• evaluations
• summaries

That data needs the same discipline as any other operating data.

Mach-1 can retain the source material, transcript, model, version and supporting evidence behind the findings it produces. That gives developers a clear path back to where a result came from and makes it easier to investigate a result when something looks wrong.

Processing state also stays explicit. Completed findings, incomplete processing and unavailable evidence remain distinct so the application can decide how each should be handled.

The intelligence behind AionCX

Mach-1 powers conversation processing across AionCX.

Atlas uses it for:

• customer and contact intelligence
• quality and performance evaluation
• coaching and workforce development
• scenario and issue detection
• classifications
• operational reporting
• other AI capabilities across the platform

Now we are making that same infrastructure available to developers through APIs and MCP.

Mach-1 services include:

• Transcription
• Conversation Analysis
• Customer Context
• Classifications
• Evaluations
• Reporting

The idea is straightforward: developers should be able to connect a conversation source, define the intelligence their product needs and receive a result they can build with, without spending their engineering time rebuilding the processing stack underneath it.

Mach-1 gives them the intelligence behind AionCX so they can build their own experience.