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Morada.ai is an AI sales agent for the real estate market. Its agent, Mia, talks to leads over WhatsApp, qualifies them, and moves them through the sales pipeline. The Data Export API exposes the conversations, messages, contacts, and deals of your workspace, the WhatsApp delivery failures, and the aggregated numbers behind the Conversations and Talk dashboards, so they can be analyzed alongside the rest of your data.

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1. Add your Morada.ai access

  1. In the Sources tab, click on the “Add source” button located on the top right of your screen. Then, select the Morada.ai option from the list of connectors.
  2. Click Next and you’ll be prompted to add your access.
  • API Key: Your workspace API key, in the mk_... format. Generate it inside Morada.ai under Integrações → Configurar API.
  • Start Date: (Optional, advanced) The earliest change timestamp to sync on the first run. Leave it empty to export the full history of your workspace — the message errors and analytics streams then cover the last 365 days, since those endpoints always require a time window.
The API key grants read access to every record in the workspace it belongs to. Treat it as a secret.
  1. Click Next.

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2. Select your Morada.ai streams

  1. The next step is letting us know which streams you want to bring. You can select entire groups of streams or only a subset of them.
    Tip: The stream can be found more easily by typing its name.
If you don’t see a stream you were expecting to find, please check if your access key has access to it. If that’s not the issue, then it’s probably because we still haven’t implemented it. Feel free to get in touch and request it!
  1. Click Next.

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3. Configure your Morada.ai data streams

  1. Customize how you want your data to appear in your catalog. Select the desired layer where the data will be placed, a name for each table (which will effectively contain the fetched data) and the type of sync.
  • Layer: choose between the existing layers on your catalog. This is where you will find your new extracted tables as the extraction runs successfully.
  • Table name: we suggest a name, but feel free to customize it. You have the option to add a prefix to all tables at once and make this process faster!
  • Sync Type: depending on the data you are bringing to the lake, you can choose between INCREMENTAL and FULL_TABLE. Read more about Sync Types here.
  1. Click Next.

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4. Configure your Morada.ai data source

  1. Describe your data source for easy identification within your organization. You can inform things like what data it brings, to which team it belongs, etc.
  2. To define your Trigger, consider how often you want data to be extracted from this source. This decision usually depends on how frequently you need the new table data updated (every day, once a week, or only at specific times).
  3. Optionally, you can define some additional settings (if available).
  • Configure Delta Log Retention and determine for how long we should store old states of this table as it gets updated. Read more about this resource here.
  • Determine when to execute an Additional Full Sync. This will complement the incremental data extractions, ensuring that your data is completely synchronized with your source every once in a while.

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Check your new source!

  1. Click Next to finalize the setup. Once completed, you’ll receive confirmation that your new source is set up!
  2. You can view your new source on the Sources page. Now, for you to be able to see it on your Catalog, you have to wait for the pipeline to run. You can now monitor it on the Sources page to see its execution and completion. If needed, manually trigger the pipeline by clicking on the refresh icon. Once executed, your new table will appear in the Catalog section.
If you encounter any issues, reach out to us via Slack, and we’ll gladly assist you!

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How the data is replicated

The contacts, conversations, deals, messages, and message errors streams are change feeds keyed by id, and every record carries a cursor_at timestamp that marks when the record last changed. Incremental syncs ask Morada.ai for everything that changed after the highest cursor_at already extracted, so records that were updated in the source reappear and are updated in your table.
Morada.ai does not emit deletion events. When a record is deleted in the source, it simply stops appearing in the feed, and no signal marks the removal. To reconcile deletions, schedule an Additional Full Sync so the table is rebuilt from a complete export.
Some fields hold workspace-specific attributes whose structure varies per account, such as props, metadata, and content. They are delivered as JSON strings, so you can parse them in a Query when you need the individual keys.

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Analytics streams

The analytics streams are different: Morada.ai recalculates them with a job that runs every six hours, and that job can restate numbers that were already extracted. They are therefore replicated as FULL_TABLE — each run brings the whole period again — and every record carries the freshness of the response that produced it, in the as_of and stale fields. Each record is one calendar day (in the America/Sao_Paulo timezone, the same one the platform dashboards use) combined with the dimensions of the stream, and all metrics are additive components: counts, and sum and count pairs for the service-level metrics.
No rate, average, or percentage is delivered ready to use. Always rebuild them as SUM(numerator) / SUM(denominator) over the period you want — for example, the response rate is SUM(conversations_answered) / SUM(conversations_started). Averaging the daily values gives the wrong number, because it weighs a quiet day the same as a busy one. Weekly and monthly figures are obtained the same way, by summing the days.
The service-level metrics of the tickets stream arrive as a sum in seconds plus a count, so the average is SUM(first_response_sum_seconds) / SUM(first_response_count) — divide by 60 for minutes.

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Rate limits

Morada.ai allows around 120 requests per minute per workspace. The connector stays below that limit on its own and automatically waits and retries when the API asks it to, so no configuration is needed on your side. Very large first extractions simply take longer to complete.

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Streams and Fields

Below you’ll find all available data streams from Morada.ai and their corresponding fields. The change-feed streams — contacts, conversations, deals, messages, and message errors — share the id, workspace_id, and cursor_at fields.
People your workspace has interacted with.Key Fields:
  • id - Unique identifier of the contact
  • workspace_id - Identifier of the Morada.ai workspace the contact belongs to
  • name - Full name of the contact
  • email - Email address of the contact
  • phone_number - Phone number of the contact, typically the WhatsApp number used in conversations
  • banned_at - Timestamp when the contact was blocked, when the contact has been blocked
  • props - Additional workspace-specific attributes of the contact, as a JSON string
  • cursor_at - Change watermark of the record, used as the incremental replication key
Conversations handled by the AI agent (Mia).Key Fields:
  • id - Unique identifier of the conversation
  • workspace_id - Identifier of the Morada.ai workspace the conversation belongs to
  • person_id - Identifier of the contact this conversation is with
  • agent_id - Identifier of the AI agent (Mia) responsible for the conversation
  • deal_id - Identifier of the deal associated with the conversation, when there is one
  • target - Target of the conversation, such as the channel address the contact was reached at
  • started_at - Timestamp when the conversation started
  • finished_at - Timestamp when the conversation was closed, when it already ended
  • last_message_at - Timestamp of the most recent message exchanged in the conversation
  • props - Additional workspace-specific attributes of the conversation, as a JSON string
  • cursor_at - Change watermark of the record, used as the incremental replication key
Sales opportunities tracked in your workspace.Key Fields:
  • id - Unique identifier of the deal
  • workspace_id - Identifier of the Morada.ai workspace the deal belongs to
  • person_id - Identifier of the contact related to the deal
  • agent_id - Identifier of the AI agent (Mia) responsible for the deal
  • owner_id - Identifier of the user who owns the deal
  • status - Current status of the deal in the sales pipeline
  • title - Title of the deal
  • props - Additional workspace-specific attributes of the deal, as a JSON string
  • cursor_at - Change watermark of the record, used as the incremental replication key
Individual messages exchanged inside conversations.Key Fields:
  • id - Unique identifier of the message
  • workspace_id - Identifier of the Morada.ai workspace the message belongs to
  • conversation_id - Identifier of the conversation this message belongs to
  • actor - Author of the message, such as mia for the AI agent or human for a person
  • type - Type of the message, such as text or image
  • content - Body of the message. Non-textual payloads are delivered as a JSON string
  • metadata - Additional message metadata, as a JSON string
  • created_at - Timestamp when the message was created
  • delivered_at - Timestamp when the message was delivered, when delivery was confirmed
  • read_at - Timestamp when the message was read, when a read receipt was received
  • cursor_at - Change watermark of the record, used as the incremental replication key
WhatsApp delivery failures reported by Meta for messages sent from your workspace. Useful to investigate why an active message never reached the lead.Key Fields:
  • id - Unique identifier of this delivery error log entry
  • workspace_id - Identifier of the Morada.ai workspace the record belongs to
  • message_id - Identifier of the message the delivery error refers to
  • conversation_id - Identifier of the conversation the failed message belongs to
  • deal_id - Identifier of the deal associated with the conversation, when there is one
  • agent_id - Identifier of the agent responsible for the conversation
  • agent_name - Name of the agent responsible for the conversation
  • external_id - Identifier of the message on WhatsApp (wamid)
  • recipient_id - Phone number of the end customer the message was addressed to, in E.164 format
  • status - Status of the delivery log entry, always failed for this report
  • error_code - WhatsApp delivery error code reported by Meta, such as 131049
  • error_title - Short title of the delivery error
  • error_message - Message describing the delivery error
  • error_detail - Additional detail about the delivery error, when available
  • message_created_at - Timestamp when the failed message was created
  • conversation_started_at - Timestamp when the conversation of the failed message started
  • conversation_link - Deep link to the conversation on the Morada.ai platform
  • metadata - Payload of the WhatsApp delivery status webhook that produced the error, as a JSON string
  • cursor_at - Timestamp of the error log entry, used as the incremental replication key
Daily conversation counters behind the Conversations dashboard, broken down by agent type, agent, and source. The default scope matches what the dashboard shows: the conversations of your AI agent on WhatsApp.Key Fields:
  • period - Calendar day of the record, in the timezone reported by the tz field
  • agent_type - Type of the agent that handled the conversations
  • agent_id - Identifier of the agent that handled the conversations
  • source - Origin of the conversations
  • conversations_started - Conversations started in the day, the denominator of the response and appointment rates
  • conversations_answered - Conversations with at least one reply from the contact, the numerator of the response rate
  • conversations_no_response - Conversations with no reply at all from the contact
  • appointments - Conversations with at least one appointment scheduled, the numerator of the appointment rate
  • granularity - Bucket size of the record, always day
  • tz - Timezone used to group the records by calendar day
  • as_of - Timestamp of the last successful Morada.ai analytics job that produced these numbers
  • stale - Whether the analytics data was behind schedule when it was extracted
Daily Talk ticket counters and service-level components behind the Talk dashboard, broken down by queue, attendant, status, and source.Key Fields:
  • period - Calendar day of the record, in the timezone reported by the tz field
  • queue - Identifier of the distribution queue of the tickets
  • queue_name - Name of the distribution queue of the tickets
  • agent - Identifier of the user who attended the tickets
  • agent_name - Name of the user who attended the tickets
  • status - Status of the tickets at the time of the snapshot: waiting, in_service, redistributed, or closed
  • source - Origin of the tickets
  • tickets_created_count - Tickets created in the day
  • tickets_closed - Tickets closed in the day
  • tickets_in_service - Tickets under service at the time of the snapshot
  • redistributed_timeout - Tickets redistributed because the attendant did not answer in time
  • total_messages - Total number of messages sent by human attendants
  • time_to_assignment_sum_seconds - Sum, in seconds, of the time between ticket creation and assignment
  • time_to_assignment_count - Number of tickets contributing to the time to assignment sum
  • first_response_sum_seconds - Sum, in seconds, of the first response time
  • first_response_count - Number of tickets contributing to the first response sum
  • handle_time_sum_seconds - Sum, in seconds, of the ticket handle time
  • handle_time_count - Number of tickets contributing to the handle time sum
  • granularity - Bucket size of the record, always day
  • tz - Timezone used to group the records by calendar day
  • as_of - Timestamp of the last successful Morada.ai analytics job that produced these numbers
  • stale - Whether the analytics data was behind schedule when it was extracted
When each analytics dataset was last refreshed by Morada.ai. Use it to know how up to date the analytics tables are before reporting on them.Key Fields:
  • metricset - Name of the analytics dataset, such as conversations or tickets
  • etl_job - Name of the Morada.ai job that feeds the dataset
  • as_of - Timestamp of the last successful run of the job
  • stale - Whether the dataset is behind schedule or its freshness is unknown