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AssemblyAI Integration Guide

Connect AssemblyAI’s REST transcription APIs, webhook callbacks, file ingestion, and real-time streaming workflows with enterprise applications.

AssemblyAI integration options at a glance

AssemblyAI provides a REST API for uploading audio, submitting media URLs, creating asynchronous transcription jobs, retrieving transcript status, and accessing derived results such as words, utterances, chapters, entities, sentiment, and summaries when enabled. It also supports webhook callbacks for selected transcription lifecycle events and WebSocket streaming for real-time transcription. API access uses an API key in the Authorization header, with temporary tokens available for relevant streaming scenarios. Martini can orchestrate submissions, persist transcript IDs, receive callbacks, poll outstanding jobs, map JSON responses, apply business rules, and deliver results to enterprise applications or data stores.

Integration pointSupported by AssemblyAI?Common use casesHow Martini supports it
REST APIsYesAssemblyAI’s primary integration mechanism for file upload, media URL submission, asynchronous transcript creation, status retrieval, result retrieval, and supported transcript management operations.Martini can consume the REST API, configure API-key authentication, map JSON requests and responses, apply validation and business rules, and expose reusable APIs around AssemblyAI operations.
Webhooks / outbound callbacksYesAssemblyAI can call a supplied webhook URL when an asynchronous transcription reaches a terminal lifecycle state such as completed or error.Martini can expose a REST API endpoint or workflow trigger, validate the callback, correlate the Transcript ID, retrieve authoritative results, and route them downstream.
File / media ingestion APIsYesAssemblyAI accepts uploaded local audio files and media URLs that it can retrieve. This is media ingestion rather than a general-purpose attachment repository.Martini can read files or media references, call the upload or transcription endpoint, manage signed URL considerations, and persist source-to-transcript correlation data.
Asynchronous batch orchestrationLimitedMultiple independent asynchronous transcription jobs can be coordinated as a batch, but a separate general-purpose bulk transcription endpoint was not confirmed.Martini can queue or schedule submissions, persist job state, control concurrency, poll outstanding jobs, process callbacks, and retry transient failures.
Real-time WebSocket streamingYesAssemblyAI provides real-time transcription by receiving incremental audio and returning partial and final results over a WebSocket connection.Martini can orchestrate surrounding services and process finalized results where the deployment supports the required streaming connection; a dedicated streaming component may be appropriate for long-lived sessions.
AuthenticationYesStandard API access uses an API key in the Authorization header. Temporary authentication tokens are available for relevant real-time streaming scenarios.Martini can store keys and configuration as secrets or protected environment settings and use controlled authentication when calling AssemblyAI or exposing callback endpoints.
GraphQL APIsNot confirmedNo official AssemblyAI GraphQL API was identified in the supplied research.Martini should use the confirmed REST API rather than assuming GraphQL support.
SOAP APIsNot confirmedNo official AssemblyAI SOAP API was identified in the supplied research.Martini should use the confirmed REST API or streaming mechanism rather than assuming SOAP support.

How AssemblyAI exposes data and business events

AssemblyAI REST APIs

AssemblyAI’s REST API supports file upload, media URL submission, asynchronous transcript creation, status retrieval, and access to transcript-derived data. The response shape depends on the transcription and analysis options enabled for the job.

Martini implementation pattern

Martini implementation pattern: a workflow receives a recording reference, validates media accessibility and business metadata, calls the appropriate AssemblyAI endpoint with the API key from protected configuration, stores the returned Transcript ID, and later retrieves the completed resource and derived results.

Implementation sequence

Receive a recording reference or media URL
Validate source metadata and media accessibility
Upload the file or submit the media URL
Persist the AssemblyAI Transcript ID
Retrieve the transcript status and result
Map transcript data to the target model

AssemblyAI Webhook Callbacks

AssemblyAI supports webhook-style callbacks for asynchronous transcription lifecycle events, primarily when a transcript completes or fails. This is selective lifecycle notification rather than a universal event stream for every AssemblyAI resource.

Martini implementation pattern

Martini implementation pattern: expose a controlled REST endpoint or workflow trigger, validate the configured callback authentication or signature mechanism, correlate the notification to a known Transcript ID, and retrieve the authoritative transcript through the REST API before processing it.

Implementation sequence

Receive the AssemblyAI callback
Validate callback authentication or signature
Check the Transcript ID against persisted state
Retrieve the authoritative transcript resource
Apply terminal-state and retry rules
Deliver the normalized result downstream

AssemblyAI File and Media APIs

AssemblyAI accepts local audio through a file upload endpoint or media through an accessible URL. The URL approach requires attention to signed URL lifetime, redirects, content type, file size, and network accessibility.

Martini implementation pattern

Martini implementation pattern: obtain a file or URL from a source system, validate the media reference, choose upload or URL submission, call AssemblyAI, and persist both source metadata and the returned Transcript ID for safe resumption.

Implementation sequence

Read the source file or media reference
Validate access, content type, and expiration
Upload the audio or submit its URL
Store source and Transcript identifiers
Start asynchronous processing
Record submission errors for retry handling

AssemblyAI Real-Time Streaming

AssemblyAI provides real-time transcription over WebSocket, returning incremental partial and final results as audio is sent. Streaming has different connection, timeout, reconnection, and finalization requirements from asynchronous REST transcription.

Martini implementation pattern

Martini implementation pattern: use Martini where the deployment can support the required streaming connection, or place a dedicated streaming component alongside Martini that forwards finalized results to a Martini API. Temporary authentication tokens may be used for relevant streaming scenarios.

Implementation sequence

Obtain the appropriate temporary streaming token
Establish the WebSocket session
Send audio frames incrementally
Process partial and final results
Apply an explicit finalization rule
Forward finalized data to Martini for downstream processing

Common AssemblyAI integration patterns

Pattern 1: Create CRM activity from customer-call transcription

When to use this pattern

Use this pattern when recorded sales or customer calls need to become searchable CRM context. The workflow should associate the recording with a business object before submission and should not create a second transcript when a retry reuses the same source event.

Integration direction
Recording storage or Salesforce
Martini
AssemblyAI
Salesforce
Example Mapping
AssemblyAI FieldCanonical FieldTarget Field
audio_urlsourceMediaUrltranscriptSource
idtranscriptIdexternalTranscriptId
texttranscriptTextactivityDescription
utterancesspeakerSegmentsconversationDetails
Martini implementation pattern

Martini receives or retrieves the recording reference, checks persisted source and Transcript identifiers, submits the media URL or uploaded file to AssemblyAI, and starts a callback or polling workflow. Once completed, it retrieves transcript analysis, maps the selected fields to a Salesforce activity, applies routing rules for sentiment or follow-up actions, and retries transient downstream failures without duplicating the activity.

Martini capabilities used
  • workflows
  • API consumption
  • data mapping
  • business rules
  • secrets management
  • error handling

Pattern 2: Enrich support tickets with transcript analysis

When to use this pattern

Use this pattern when support calls or voice interactions should enrich an existing ticket with a transcript, speaker segments, sentiment, entities, or action items. It is suitable for escalation and quality-assurance workflows.

Integration direction
Zendesk or ServiceNow
Martini
AssemblyAI
Zendesk or ServiceNow
Example Mapping
AssemblyAI FieldCanonical FieldTarget Field
statusprocessingStatustranscriptionStatus
utterancesconversationSegmentscallTranscriptSegments
sentimentconversationSentimentprioritySignal
entitiesdetectedEntitiesticketMetadata
Martini implementation pattern

A Martini workflow correlates a support ticket with its recording, creates the AssemblyAI job, and persists state. A callback or scheduler then retrieves the completed Transcript, validates optional analysis fields, applies escalation thresholds, and updates the ticket. Idempotency checks prevent repeated callback delivery from creating duplicate notes or escalations.

Martini capabilities used
  • workflow triggers
  • API consumption
  • JSON handling
  • data mapping
  • validation
  • retry handling

Pattern 3: Publish meeting summaries to collaboration channels

When to use this pattern

Use this pattern when meeting recordings should be transcribed and summarized before being distributed to a collaboration channel or document repository. It separates long-running media processing from concise notifications.

Integration direction
Microsoft Teams or Amazon S3
Martini
AssemblyAI
Microsoft Teams or Slack
Example Mapping
AssemblyAI FieldCanonical FieldTarget Field
chaptersmeetingTopicssummarySections
summarymeetingSummarymessageBody
idtranscriptIdsourceReference
statusprocessingStatusnotificationState
Martini implementation pattern

A scheduled workflow or source event starts the process, validates the recording reference, and submits it to AssemblyAI. After completion, Martini retrieves chapters and summaries, formats a concise message, applies recipient and privacy rules, and sends the result to the selected collaboration application. Large transcript content can be stored separately while the notification contains only approved fields.

Martini capabilities used
  • scheduling workflows
  • workflow orchestration
  • API consumption
  • data transformation
  • business rules
  • secure configuration

Pattern 4: Orchestrate controlled batch media processing

When to use this pattern

Use this pattern for manifests or queues containing many independent audio files. It provides persistent job state, controlled concurrency, and recovery when a submission or result retrieval fails.

Integration direction
Amazon S3 or queue
Martini
AssemblyAI
Snowflake
Example Mapping
AssemblyAI FieldCanonical FieldTarget Field
object_keysourceObjectIdmediaObjectKey
idtranscriptIdassemblyTranscriptId
statusprocessingStatusjobStatus
wordswordResultstranscript_words
Martini implementation pattern

Martini reads eligible items from a manifest, queue, or database, checks for an existing source-to-Transcript association, and submits jobs with controlled concurrency. A scheduler polls outstanding jobs or processes callbacks, retrieves completed results, normalizes large nested structures, and writes them idempotently to Snowflake. Backoff, rate-limit handling, and terminal error recording support safe recovery.

Martini capabilities used
  • scheduled workflows
  • queue or batch orchestration
  • API consumption
  • database or data-store integration
  • idempotency rules
  • error handling

Applications commonly integrated with AssemblyAI

AssemblyAI can be incorporated into enterprise architectures where recorded calls, meetings, or other audio need to become searchable, actionable, or analyzable business data. The following are practical integration targets; the exact media source, access model, and downstream API depend on the organization’s application landscape.

Application Scenario Direction Martini Pattern
Salesforce Add call transcripts, summaries, sentiment, entities, and follow-up actions to Accounts, Contacts, Leads, Opportunities, or activity records. Salesforce → Martini → AssemblyAI → Salesforce Martini receives or retrieves a recording reference, submits the media URL to AssemblyAI, persists the Transcript ID, receives or polls for completion, retrieves the authoritative result, and updates the relevant Salesforce activity or business record.
ServiceNow Enrich incidents, cases, or customer-service records with transcripts, extracted actions, and conversation analysis. ServiceNow → Martini → AssemblyAI → ServiceNow A Martini workflow correlates a ServiceNow record with its recording, submits the media to AssemblyAI, processes the completion callback or polling result, validates optional analysis fields, and writes a normalized summary or transcript back to ServiceNow.
Zendesk Attach support-call transcripts, summaries, and sentiment information to tickets for quality assurance and escalation workflows. Zendesk → Martini → AssemblyAI → Zendesk Martini consumes the ticket or recording event, submits audio for asynchronous transcription, retrieves completed results, applies escalation rules, and updates the Zendesk ticket while preventing duplicate submissions.
HubSpot Associate sales-call transcripts and summaries with Contacts, Companies, Deals, and engagement records. HubSpot → Martini → AssemblyAI → HubSpot Martini uses a recording identifier or media URL to create an AssemblyAI job, stores correlation metadata, normalizes the returned transcript and analysis, and writes the result to the appropriate HubSpot engagement or CRM object.
Slack Notify teams when transcripts are ready and distribute summaries or escalation alerts without exposing full audio-processing logic in the collaboration platform. AssemblyAI → Martini → Slack A Martini callback workflow retrieves the completed Transcript, creates a concise notification from configured fields, applies routing rules for sentiment or escalation, and posts the appropriate message to Slack.
Microsoft Teams Process meeting recordings and distribute transcript summaries, chapters, or action items to collaboration channels. Microsoft Teams → Martini → AssemblyAI → Microsoft Teams Martini receives a recording reference from the surrounding Teams or storage workflow, submits it to AssemblyAI, waits for completion, maps chapters and action items, and publishes the selected output to Teams.
Amazon S3 Use object storage as a source for audio and a destination for transcripts, summaries, or derived analysis artifacts. Amazon S3 → Martini → AssemblyAI → Amazon S3 Martini consumes an object reference or manifest, validates media accessibility, submits a signed or otherwise accessible URL where appropriate, tracks the Transcript ID, and stores normalized results or files back in the designated bucket.
Snowflake Store normalized transcripts, utterances, sentiment, entities, and processing metadata for reporting and language analytics. AssemblyAI → Martini → Snowflake Martini retrieves completed transcript results, separates large or repeated structures such as Words and Utterances into suitable tables, applies schema validation, and writes idempotently to Snowflake through the organization’s database integration pattern.

How to build a AssemblyAI integration in Martini

Objective

Configure AssemblyAI access without embedding credentials in workflow definitions or payloads.

Instructions in Martini

  • Store the AssemblyAI API key in Martini secrets or protected environment configuration.
  • Configure the Authorization header for REST calls.
  • Use temporary tokens only for applicable real-time streaming scenarios.
  • Keep development, test, and production credentials separated where supported.

Objective

Select the event or schedule that starts transcription processing and matches the source system’s delivery model.

Instructions in Martini

  • Use a source application event, file or object notification, queue item, or API request when available.
  • Use a scheduler for polling outstanding Transcript IDs or processing a manifest.
  • Expose a Martini API endpoint for AssemblyAI completion callbacks.
  • Treat streaming sessions as a separate connection lifecycle from asynchronous jobs.

Objective

Validate the audio source and create an AssemblyAI transcription job using the appropriate ingestion method.

Instructions in Martini

  • Validate the media URL, signed URL lifetime, content type, and source correlation ID.
  • Call the file upload endpoint for local media or submit an accessible media URL.
  • Persist the returned Transcript ID before continuing.
  • Avoid resubmitting a source that already has an active or completed Transcript ID.

Objective

Manage the asynchronous lifecycle until the transcript reaches a usable terminal state.

Instructions in Martini

  • Receive and authenticate callbacks or poll the Transcript resource on a controlled schedule.
  • Persist processing state and business correlation metadata.
  • Treat completed and error states explicitly.
  • Apply backoff and concurrency controls for large batches or rate-limited workloads.

Objective

Convert AssemblyAI JSON and optional analysis results into stable downstream models.

Instructions in Martini

  • Map Transcript, Word, Utterance, Chapter, and Entity fields to canonical structures.
  • Handle optional analysis fields defensively because response shape depends on enabled options.
  • Separate large nested results when target systems have payload limits.
  • Apply redaction, normalization, and formatting rules before distribution.

Objective

Deliver approved transcript content and analysis to enterprise applications, repositories, or data stores.

Instructions in Martini

  • Update the target CRM, support, collaboration, repository, or warehouse record.
  • Use the Transcript ID and source identifier as external correlation keys.
  • Apply escalation, routing, and privacy rules before writing or notifying.
  • Make downstream writes idempotent so callback retries do not duplicate results.

Common AssemblyAI data objects used in integrations

ObjectTypical UseCommon target systemsMartini handling
TranscriptCentral asynchronous transcription resource containing status, source audio reference, text, timestamps, language information, processing options, and links to derived analysis.Salesforce, ServiceNow, Zendesk, HubSpot, Snowflake, document repositoriesMartini persists the Transcript ID and correlation metadata, retrieves the authoritative resource after notification or polling, validates status, and maps the result to downstream models.
WordWord-level transcription result with text and timing information for audio synchronization, search, or detailed analysis.Snowflake, analytics stores, document platforms, quality-assurance applicationsMartini can normalize Words into child records or structured JSON, manage large responses carefully, and avoid unnecessary logging of sensitive transcript content.
UtteranceSpeaker-segmented portion of a transcript used for diarization, speaker labels, and conversation-oriented processing.Salesforce, ServiceNow, Zendesk, data warehouses, review applicationsMartini maps speaker, text, and timing fields, applies optional-field validation, and routes selected utterances according to business rules.
ChapterTopical transcript segment containing headings, summaries, and timestamps when summarization or chapter generation is enabled.Microsoft Teams, Slack, document repositories, knowledge platformsMartini extracts chapters and summaries, formats notifications or documents, and preserves source Transcript correlation.
EntityDetected named entity such as a person, organization, or location, including text and position in the transcript.CRM systems, case-management platforms, Snowflake, compliance workflowsMartini maps entity types and positions, applies enrichment or redaction rules, and writes only the fields required by downstream systems.
Webhook eventOutbound callback identifying an asynchronous transcription lifecycle event, primarily completion or failure.Martini APIs, workflow triggers, queues, operational storesMartini authenticates and validates the notification, checks the Transcript ID against persisted state, retrieves the full resource, and makes callback handling idempotent.

Authentication and security considerations

API keys and protected configuration

AssemblyAI standard API access uses an API key in the Authorization header. Store the key in Martini secrets or protected environment configuration rather than workflow definitions, source-controlled mappings, logs, or public API payloads.

Callback and streaming security

Validate the configured webhook authorization or signature mechanism before processing callbacks. Temporary authentication tokens can be used for relevant real-time streaming scenarios to avoid exposing a permanent API key to a client.

Audio and transcript privacy

  • Restrict access to audio, transcript text, and derived analysis according to business sensitivity.
  • Avoid writing raw audio or full transcript content to diagnostic logs.
  • Define retention, redaction, data residency, and contractual requirements for the applicable AssemblyAI plan.

Operational considerations for AssemblyAI integrations

Asynchronous state

Persist the source recording identifier, AssemblyAI Transcript ID, processing state, and business correlation key. Use callbacks or scheduled polling and treat completed and error states explicitly.

Idempotency and retries

Check for an existing Transcript ID before submitting a recording. Make callback and downstream processing idempotent, and use backoff for transient failures. Do not assume callback delivery is unique.

Rate limits and payload size

Confirm current AssemblyAI request and concurrency limits. Use throttling or queue-based orchestration for batches. Words and Utterances can create large responses, so avoid unnecessary logging and use suitable storage or target structures.

Media accessibility

For media URLs, account for signed URL expiration, redirects, content type, file size, and network accessibility. Direct upload may be more reliable for private media.

Schema variability and testing

Analysis fields depend on the options enabled when the job is submitted. Use defensive mappings, validate optional fields, and regression-test changes to transcript schemas and downstream payload limits.

Streaming lifecycle

Real-time WebSocket processing requires explicit handling for partial results, finalization, timeouts, reconnection, and session failure. Do not treat partial results as final business records without a defined finalization rule.

Why use Martini instead of scripts or point-to-point integrations?

Reusable orchestration

Martini separates media submission, asynchronous state tracking, callback handling, result retrieval, and downstream delivery into maintainable workflows rather than embedding the entire process in a one-off script.

Controlled transformation

Martini maps AssemblyAI’s Transcript, Word, Utterance, Chapter, and Entity structures into canonical and target-specific models. Business rules can determine which analysis fields are stored, notified, redacted, or escalated.

Operational resilience

Workflows can persist correlation data, handle retries, control concurrency, validate callbacks, and support polling when inbound callbacks are unsuitable. This reduces the risk of duplicate transcription jobs and incomplete downstream updates.

API-led integration

Martini can consume AssemblyAI APIs and expose controlled APIs to internal applications, allowing enterprise systems to use a stable integration façade without coupling directly to AssemblyAI request and response details.

Frequently asked questions

How can AssemblyAI be integrated with enterprise systems?

AssemblyAI can be integrated through its REST API for file upload, media URL submission, asynchronous transcription, status retrieval, and transcript analysis. It also supports webhook callbacks for selected asynchronous lifecycle events and WebSocket streaming for real-time transcription. Enterprise workflows typically persist the Transcript ID, retrieve authoritative results, and map them into CRM, support, collaboration, storage, or analytics systems.

Can Martini integrate with AssemblyAI?

Yes. Martini can consume AssemblyAI’s REST API, submit files or media URLs, store and track Transcript IDs, receive webhook callbacks through an exposed API, retrieve completed results, transform JSON, and route transcript data to downstream systems. Real-time streaming may require a deployment capability or surrounding service that can maintain the WebSocket connection.

Do I need a connector to integrate AssemblyAI with Martini?

No. A dedicated AssemblyAI connector is not required. Martini can integrate using AssemblyAI’s confirmed native mechanisms, including REST APIs, file or media ingestion, webhook callbacks, API-key authentication, and an appropriate surrounding pattern for WebSocket streaming.

Is there any extra Lonti cost to integrate AssemblyAI with Martini?

Lonti does not charge an additional per-connector or per-vendor fee to integrate AssemblyAI. The integration is subject to the provisioned capacity of the Martini environment. Separate costs may apply from AssemblyAI, cloud infrastructure, storage, or other third-party systems depending on subscription, usage, and deployment model.

Which AssemblyAI integration methods should architects use?

The REST API is the primary method for asynchronous transcription, media submission, status tracking, and result retrieval. Webhook callbacks are useful for completion and failure notifications, while scheduled polling is an alternative. WebSocket streaming is the relevant method for real-time transcription. No official AssemblyAI GraphQL or SOAP API was confirmed.

Are AssemblyAI webhooks available for every event?

No. AssemblyAI webhook coverage is selective and is primarily associated with asynchronous transcription lifecycle events such as completion or failure. A callback generally identifies the Transcript resource; Martini should validate it and retrieve the authoritative result through the REST API. Real-time incremental results are delivered over WebSocket rather than ordinary webhooks.

How does synchronization with AssemblyAI work?

AssemblyAI uses asynchronous job-status tracking rather than conventional modified-date synchronization or change-data-capture tokens. Martini should persist the source identifier, Transcript ID, processing state, and business correlation key. It can then use callbacks or scheduled polling to resume processing safely and avoid duplicate submissions.

How does Martini handle AssemblyAI mapping, errors, and retries?

Martini can map AssemblyAI JSON into canonical and downstream models, validate optional fields, apply business rules, and separate large nested results such as Words or Utterances. Workflows can use persisted state, idempotency checks, backoff, throttling, and error handling to manage rate limits, transient failures, duplicate callbacks, and terminal transcription errors. Martini can also expose an API façade around AssemblyAI operations when an organization needs a controlled internal endpoint.

Related Martini documentation

Martini APIs

Workflows

Data handling

Security

Operations

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