Audio to Structured Data with AI - API: Turn Conversations into Actionable Intelligence

Audio is valuable business data

Businesses create valuable information in conversations every day:

• Customer support calls
• Sales discovery calls
• Supplier phone calls
• Team meetings
• Voice notes
• Interviews
• Field reports
• Voice-based inspections
• Medical or operational dictation
• Call-center recordings
• Compliance conversations
• Incident reports
• Delivery confirmations
• Procurement discussions

Most of that information remains operationally inaccessible. A person may say:

The customer wants to move forward next month.
The invoice is F-2026-00456.
The agreed amount is €1,284.50.
Please send the revised contract by Friday.
The delivery arrived damaged.
This request is urgent.

A human listener can understand the meaning. A database, CRM, ERP, ticketing system, agent, or automation cannot reliably act on an MP3 file.

Traditionally, teams have three imperfect options:

1. Listen manually and type notes.
2. Generate a transcript and search it later.
3. Ask an AI assistant for a summary.

None of these options creates dependable operational data by default. A transcript is better than raw audio, but it is still unstructured text. A summary is useful for a human reader, but it can omit identifiers, dates, amounts, owners, commitments, or exceptions that another system needs.

AI agents need more than “what was discussed.” They need structured facts they can validate, route, store, compare, query, and use in a workflow.

From spoken language to structured business data

Claix Audio-to-JSON converts an audio file into the exact structure defined by your schema. For example, a finance team may define:

{
  "client": "string",
  "invoice_number": "string",
  "issue_date": "date | null",
  "total_amount": "number | null",
  "currency": "string | null",
  "payment_status": "string | null",
  "urgency": "boolean | null"
}

A customer call or voice note can then become:

{
  "client": "Suministros Industriales del Ebro S.L.",
  "invoice_number": "F-2026-00456",
  "issue_date": "2026-03-14",
  "total_amount": 1284.5,
  "currency": "EUR",
  "payment_status": "pending confirmation",
  "urgency": true
}

The result can immediately enter a system:

Audio recording
        ↓
Claix structured extraction
        ↓
Validate required fields
        ↓
Create CRM record, ticket, task, invoice workflow, or alert
        ↓
Route exceptions to a human reviewer

The key idea is simple: audio is not only something to transcribe. It is a source of structured business data.

Why transcripts are not enough for agents

A transcript answers: what words were spoken? An agent-ready structured record answers who the customer is, what was agreed, which amount was mentioned, which invoice is being discussed, what action is needed, who owns that action, whether the request is urgent, and what should happen next.

Consider a short voice message:

Hi, this is Marta from Suministros Industriales del Ebro. I am calling about
invoice F-2026-00456 for one thousand two hundred eighty-four euros and fifty
cents. We still need confirmation that the payment will be made this week.
This is urgent because our delivery is scheduled for Friday.

A plain transcript leaves the workflow with a paragraph. Claix can turn that information into fields:

{
  "client": "Suministros Industriales del Ebro S.L.",
  "invoice_number": "F-2026-00456",
  "total_amount": 1284.5,
  "currency": "EUR",
  "payment_request": "Confirmation that payment will be made this week",
  "urgency": true,
  "delivery_date_reference": "Friday"
}

That structured response lets software take appropriate next steps:

Find invoice F-2026-00456
        ↓
Check payment status
        ↓
Create an urgent finance task
        ↓
Notify the account owner
        ↓
Log the customer communication

No one needs to replay the audio, manually copy identifiers, or ask a second system to interpret a transcript again. Structured, timestamped conversation data is increasingly the foundation for agent workflows because downstream systems need labeled decisions, actions, entities, and metadata—not just a block of conversation text.

Claix Audio-to-JSON

Claix introduces a dedicated audio extraction workflow for spoken business information.

Audio file
        ↓
Multimodal audio understanding
        ↓
Schema-guided extraction
        ↓
Typed JSON
        ↓
Automation-ready output

Claix supports audio files in these formats:

MP3
WAV
M4A
OGG

It is designed for recordings up to 10 minutes and 12 MB, making it practical for voice notes, short customer calls, support recordings, sales call segments, interviews, meeting clips, operational reports, field-service updates, dictated records, and voice messages from mobile applications.

The standard endpoint is POST https://claix.dev/api/audio-json. It is designed for secure server-to-server integrations, including backends, serverless functions, internal tools, n8n, Make, Zapier, AI-agent orchestrators, CRM automations, call-center pipelines, and mobile backends.

Audio Agent Mode

Some workflows need more than structured extraction. They need reasoning over what was said. For these workflows, Claix provides Audio Agent Mode at POST https://claix.dev/agent/audio-json.

Audio Agent Mode first extracts data according to the primary schema, then performs a second typed reasoning phase based on the schema’s agent_definition. This is useful for questions such as:

• Is the customer request urgent?
• Is the caller asking for a refund, payment confirmation, or contract change?
• Does the conversation require escalation?
• Was a commitment made?
• Is there a stated deadline?
• Is the request related to a known invoice or account?
• Should the call create a support ticket?
• Is the issue likely to affect an upcoming delivery?

Example:

{
  "success": true,
  "schema_utilizado": "Notas de llamada",
  "total_registros": 1,
  "data": [
    {
      "cliente": "Suministros Industriales del Ebro S.L.",
      "numero_factura": "F-2026-00456",
      "importe_total": 1284.5
    }
  ],
  "agent_data": {
    "urgencia": true,
    "resumen_llamada": "The customer referenced invoice F-2026-00456 for €1,284.50 and requested payment confirmation before an upcoming delivery."
  },
  "log_id": "7c2e1a90-4b3d-4f8a-9e21-6d5c8b0a1f34"
}

This gives an agent both structured facts and typed operational interpretation. The output remains machine-readable, predictable, and ready for downstream automation.

Source verification for audio

Every critical fact should be traceable

Spoken data is often harder to validate than text in a spreadsheet. Names can be unclear. Numbers can be pronounced quickly. A caller may correct themselves. Dates can be relative. Audio quality can vary. A meeting may contain several possible commitments or amounts.

That is why Claix supports optional source verification for audio extraction and Audio Agent Mode. When enabled, every extracted field becomes:

{
  "value": "...",
  "source": "..."
}

For audio, source can identify the exact second or time range where the relevant information was spoken.

{
  "success": true,
  "schema_utilizado": "Notas de voz de facturas",
  "total_registros": 1,
  "log_id": "7c2e1a90-4b3d-4f8a-9e21-6d5c8b0a1f34",
  "data": [
    {
      "numero_factura": {
        "value": "F-2026-00456",
        "source": "second 72, \"invoice F-2026-00456\""
      },
      "importe_total": {
        "value": 1284.5,
        "source": "seconds 220-235, total amount"
      },
      "moneda": {
        "value": null,
        "source": "requires_human_revision"
      }
    }
  ]
}

This makes it possible to move from “the AI says the invoice amount is €1,284.50” to “the amount is €1,284.50, mentioned between seconds 220 and 235.” A reviewer can jump directly to the relevant moment in the recording.

requires_human_revision is a safety feature

When Claix cannot identify a precise, reliable source for a field, it returns requires_human_revision. This is not a generic error. It is a trust signal.

{
  "currency": {
    "value": null,
    "source": "requires_human_revision"
  }
}

The result tells the workflow not to assume the currency, invent a value, or silently continue with an unsupported answer. Ask for review, clarification, or a better recording. A robust automation can use this signal directly:

If a required field has source = requires_human_revision:
    Create a human-review task
    Pause an irreversible action
    Notify the responsible team
    Request clarification if appropriate

This is essential for workflows involving payments, contracts, customer records, compliance, expense claims, legal commitments, service-level agreements, escalations, operational incidents, and supplier instructions. For high-impact agent workflows, structured outputs, source grounding, validation, and human oversight gates are recommended before downstream systems act on uncertain information.

Practical use cases

Customer support calls

Support teams receive valuable information through calls: the customer cannot access their account, the order did not arrive, the invoice was charged twice, the customer wants a refund, the incident affects production, or the request needs escalation. A transcript forces an agent or support worker to read through the call. Claix can extract fields such as:

{
  "customer_name": "string",
  "account_id": "string | null",
  "issue_type": "string",
  "product": "string | null",
  "urgency": "boolean",
  "requested_action": "string",
  "deadline": "string | null",
  "requires_escalation": "boolean"
}

Then a workflow can create a support ticket, set priority, assign a team, and notify the account owner if escalation is required. The structured layer is what makes the call useful to systems—not only readable to humans.

Sales calls and lead qualification

Sales calls contain important operational details: company name, contact details, role, budget, timeline, pain points, current tooling, buying process, decision-maker, next step, objections, and follow-up commitment. Instead of storing a recording and a generic meeting summary, a sales workflow can create structured CRM data:

{
  "company_name": "string",
  "contact_name": "string",
  "job_title": "string",
  "use_case": "string",
  "budget_range": "string | null",
  "timeline": "string | null",
  "decision_maker": "string | null",
  "next_step": "string | null",
  "follow_up_date": "date | null",
  "lead_priority": "string"
}

An AI agent can then update the CRM, create a follow-up task, draft a relevant email, route high-intent deals to sales, and track common objections across calls. The agent does not need to repeatedly reread a transcript. It receives the fields it needs.

Voice notes from field operations

Many operational teams work away from a desk: delivery drivers, site inspectors, maintenance technicians, warehouse teams, construction supervisors, insurance adjusters, sales representatives, healthcare or care workers, logistics teams, and procurement staff. Typing structured reports on a mobile device can be slow, inconvenient, or unsafe.

A worker may simply record: “Delivery arrived at 14:20. Three boxes were damaged. The customer accepted the remaining shipment but needs replacements before Thursday. The contact was Laura Martínez at the Barcelona warehouse.” Claix can convert the voice note into:

{
  "arrival_time": "14:20",
  "damaged_boxes": 3,
  "customer_accepted_remaining_shipment": true,
  "replacement_deadline": "Thursday",
  "contact_name": "Laura Martínez",
  "location": "Barcelona warehouse"
}

Then an agent can create an incident, open a replacement order, notify operations, update delivery status, and create a deadline reminder. This turns voice into a practical input interface for operational systems. Voice-to-structured-data workflows are increasingly used in frontline operations because they reduce manual data entry and can convert spoken observations into queryable records.

Finance and invoice voice notes

Voice messages often contain finance-relevant information: a supplier called about invoice F-2026-00456, the total is €1,284.50, they need payment confirmation this week, the delivery is scheduled for Friday, and someone should check whether the purchase order has been approved. Claix can extract:

{
  "supplier_name": "string | null",
  "invoice_number": "string | null",
  "total_amount": "number | null",
  "currency": "string | null",
  "payment_request": "string | null",
  "delivery_deadline": "string | null",
  "requires_purchase_order_check": "boolean"
}

Audio Agent Mode can add:

{
  "urgency": true,
  "recommended_action": "Confirm invoice status and validate purchase order before payment confirmation."
}

A finance workflow can then find the invoice, find the purchase order, compare totals, check approval status, and create an exception or respond to the supplier. This prevents important business data from staying inside an unsearchable voicemail or an employee’s memory.

Meetings and action items

Meetings are full of decisions that disappear after the call: send the revised proposal by Friday, Ana owns the supplier review, legal needs to approve the renewal clause, the launch date moves to October 15, the client needs an updated quote. A generic meeting summary is useful, but an operational workflow needs structured objects:

{
  "decisions": [],
  "action_items": [],
  "owners": [],
  "deadlines": [],
  "risks": [],
  "blocked_items": [],
  "follow_up_requirements": []
}

Claix can help turn a short meeting recording into data that an agent can send to Jira, Linear, Asana, Notion, Slack, CRM, ERP, project dashboards, or internal databases. A meeting summary is a human-readable recap. Structured meeting output is tasks, owners, deadlines, decisions, and systems of record. Organizations increasingly treat structured meeting output as a governed operational record rather than a raw transcript, because structured actions and decisions can be routed into systems of work without manual re-entry.

Audio as persistent agent memory

Audio extraction becomes more powerful when it is not treated as a one-time event. When a Claix schema has Context Window enabled, the processed audio can be persisted. Claix stores the transcription in Markdown as markdown_content, and the response can include a document_id.

Audio processed once
        ↓
Transcription and structured data persisted
        ↓
document_id returned
        ↓
Agent can query the conversation later

This is useful for questions such as what the customer said about the delivery deadline, whether a payment commitment was made, which action item was assigned to Ana, whether the caller mentioned a specific invoice number, what the customer’s main objection was, or whether urgency was mentioned in the voice note. Instead of repeatedly uploading and processing the same audio file, an agent can use the persisted context. This creates a bridge between spoken communication and durable organizational memory.

Audio inside Knowledge Spaces

A single audio file is useful. The bigger opportunity is connecting spoken information with related business documents. A Claix Knowledge Space can group a customer call recording with an invoice PDF, purchase order, contract, support history, and price list.

Customer call recording
    + invoice PDF
    + purchase order
    + contract
    + support history
    + price list
        ↓
Knowledge Space
        ↓
Cross-document agent reasoning

Then an agent can ask whether the amount mentioned in the customer call matches the invoice, whether the payment deadline mentioned on the call is consistent with the contract, which customer calls mention invoices that remain unpaid, whether the supplier’s verbal request matches the purchase order, or what commitments were made across sales calls and contract documents. This is where audio becomes part of a larger document-intelligence system rather than a disconnected transcription feature.

Audio data must be handled carefully

Audio often contains sensitive information: names, phone numbers, account details, commercial commitments, financial information, customer complaints, health or personal information, internal business decisions, legal discussions, credentials spoken accidentally, and operational incidents.

Before processing audio, teams should define whether they have a lawful basis and appropriate notice or consent where applicable; which recordings can be processed; who can access source audio and extracted data; how long recordings and transcriptions are retained; which fields should be extracted; which fields should be redacted or excluded; which decisions require human approval; and which outputs can trigger external actions.

AI agents should not automatically treat every spoken statement as an instruction. The safe model is:

Audio content
        ↓
Untrusted business input
        ↓
Schema-guided structured extraction
        ↓
Source verification
        ↓
Business-rule validation
        ↓
Permission and policy checks
        ↓
Human approval where needed
        ↓
Allowed downstream action

For example, a caller saying “Please change the payment destination immediately” should not automatically trigger a bank-detail update. The system should instead extract a request:

{
  "requested_action": "Change payment destination",
  "source": "seconds 102-108"
}

Then route it through a verified, authorized workflow. Guidance for agent security emphasizes treating external inputs as untrusted, validating structured outputs before propagation, limiting agent authority, maintaining logs, and adding human oversight before high-risk outputs reach downstream tools or systems.

Standard extraction vs Audio Agent Mode

Claix provides two ways to process audio.

Use standard extraction when the workflow needs facts: what was said? Use Audio Agent Mode when the workflow needs typed interpretation: what does this mean operationally? Both return structured data that systems can consume.

A reliable audio workflow

A production workflow should not stop at “audio in, model answer out.” A stronger design looks like this:

1. Receive audio file
        ↓
2. Validate file type, size, and duration
        ↓
3. Extract schema-defined fields with Claix
        ↓
4. Enable source verification for decision-driving fields
        ↓
5. Check for null values or requires_human_revision
        ↓
6. Validate identifiers, amounts, dates, and business rules
        ↓
7. Route high-risk cases to human review
        ↓
8. Send approved structured data to the next system
        ↓
9. Store log_id for traceability

For example, a voice note about an invoice can extract invoice number, supplier, amount, currency, and urgency; verify source timestamps; find the invoice in the ERP; compare the amount with the purchase order; create a finance review task if there is a mismatch or missing source; otherwise send the confirmation workflow. This is how spoken information becomes safe enough to support real operations.

Build voice-native agent workflows

The next generation of AI systems will not only read text and documents. They will understand what people say in the moments when important business information is created: a customer call, a supplier voicemail, a sales meeting, a field report, a finance message, a delivery confirmation, an internal decision.

The challenge is not merely converting speech into text. The challenge is transforming spoken information into structured, traceable, policy-controlled data that agents and systems can safely use. Claix Audio-to-JSON provides that document-intelligence layer for audio:

Audio
        ↓
Structured data
        ↓
Source timestamps
        ↓
Persistent context
        ↓
Knowledge Spaces
        ↓
Reliable agent and workflow actions

Claix turns spoken business information into structured data that AI agents can understand, verify, remember, and act on.

Start building

• Create a Claix workspace: https://www.claix.dev/register
• Read the Audio-to-JSON API documentation: https://www.claix.dev/documentation/audio-to-json
• Explore Claix MCP: https://www.claix.dev/documentation/mcp
• Explore the native Claix A2A agent: https://www.claix.dev/documentation/a2a
• Learn about document intelligence for AI agents: https://www.claix.dev/blog/inteligencia-documental-api

Audio → structured JSON → source seconds → context → Knowledge Spaces → safe agent action

You can process audio with Claix Managed AI or with Bring Your Own Key (BYOK). Claix does not charge an additional processing fee for BYOK. Customers remain responsible for their AI provider's token usage and provider charges.