You now have more visibility and control over how your data syncs, at both the connector and dataset level. Whether you're managing sync schedules, adjusting how far back data is pulled, or fine-tuning chunking behavior, these controls give you the flexibility to configure data ingestion the way your accounts need it.

Here's what's new:

Connector-level configuration — From the main connector page, you now have the ability to set the chunking size ("Max Days per Request (Chunk Size)") if needed. Guardrails are in place to prevent chunk sizes that could cause issues (minimum 5 days — contact support if you need something outside that range).

Dataset-level overrides — Need something different for a specific dataset? You can now override the refresh lookback window and chunking size independently. The same guardrails that apply at the connector level apply here as well.

More flexibility in scheduling your dataset — You now have three distinct scheduling options for your dataset. You can run your dataset on the connector's existing schedule, or select "Add Schedule (Optional)" to configure an additional time. When adding a schedule, you'll have the choice to run it in addition to your connector schedule or to override it entirely — giving you full control over when your data syncs.

Targeted manual backfills — In addition to the existing manual sync functionality, you can now kick off a backfill for a specific date range targeting one or multiple accounts, giving you more surgical control when you need to re-pull data. This option can be found by clicking "Manual Sync" from the hamburger menu of your dataset on the connector page.

Improved setup workflow for custom mapping providers (Google Sheets, SQL, Email, Snowflake Share) — We've reordered the setup flow so that Date & Account Matching now comes before the Configure Columns step. This small change makes setup more intuitive and helps reduce configuration errors.

You can now control which templates each agency user has access to. From the user management screen, turn off "All Templates" for a user and pick exactly which templates they should see. That user won't see excluded templates anywhere in the product, and any reports or dashboards built on those templates are hidden from their view as well. Parent agency users, admins, and advertiser users are unaffected, and NinjaCat's starter templates remain available to everyone.

Agencies can now configure multiple SMTP servers and route email differently per whitelabel CName or per advertiser. Different brands or clients under the same agency can send reports, user invites, password resets, and NinjaTrack call notifications from different providers — each with its own verified sender and visible status. Each configuration can be verified with a test email and classified error messages if setup fails. Available to agencies on request — reach out to your CSM to enable.

Setting up AI Insights in the Template Builder just got easier. Click the new "Pick Widget from Page" button and select the data widget you want the AI Insights widget to read from — no more hunting through dropdowns. A new eye icon next to the selected widget jumps you straight to that widget on the canvas whenever you need to review or edit it.

AI Insights and Executive Summary now support GPT-5.4 Thinking, OpenAI's latest reasoning model. Select it from the model dropdown when you want deeper analysis on a specific widget. Your existing default model remains the same.

AI Insights now supports Anthropic Claude. Choose between Claude Opus 4.6 and Claude Sonnet 4.6 in the model selector for more flexibility in how your insights are generated.

If a dataset referenced in a SQL Transform gets deleted, the UI now surfaces a clear, helpful error message explaining what happened — instead of throwing a generic "unknown error". You'll now know exactly what's wrong and can take action to fix the transform config.

You now have more visibility and control over how your data syncs, at both the connector and dataset level. Whether you're managing sync schedules, adjusting how far back data is pulled, or fine-tuning chunking behavior, these controls give you the flexibility to configure data ingestion the way your accounts need it.

Here's what's new:

• Connector-level configuration — From the main connector page, you now have the ability to set the chunking size ("Max Days per Request (Chunk Size)") if needed. Guardrails are in place to prevent chunk sizes that could cause issues (minimum 5 days — contact support if you need something outside that range).

• Dataset-level overrides — Need something different for a specific dataset? You can now override the refresh lookback window and chunking size independently. The same guardrails that apply at the connector level apply here as well.

• More flexibility in scheduling your dataset — You now have three distinct scheduling options for your dataset. You can run your dataset on the connector's existing schedule, or select "Add Schedule (Optional)" to configure an additional time. When adding a schedule, you'll have the choice to run it in addition to your connector schedule or to override it entirely — giving you full control over when your data syncs.

• Targeted manual backfills — In addition to the existing manual sync functionality, you can now kick off a backfill for a specific date range targeting one or multiple accounts, giving you more surgical control when you need to re-pull data. This option can be found by clicking "Manual Sync" from the hamburger menu of your dataset on the connector page.

• Improved setup workflow for custom mapping providers (Google Sheets, SQL, Email, Snowflake Share) — We've reordered the setup flow so that Date & Account Matching now comes before the Configure Columns step. This small change makes setup more intuitive and helps reduce configuration errors.

You can now re-run exports across a date range in one shot, no more clicking through each day individually. A new Batched Run option lets you replay an export as if it were scheduled to run on each day (or month) within a range you define. Each execution simulates a scheduled run using the export's existing configuration, incrementing automatically from start to end.

There are two things you configure to make this work: an interval and a date range — and understanding the difference between them is the key to using this feature well:

Interval = how big is each slice of data? Choose from:

• Previous Day = each chunk covers "yesterday" relative to that step in the run
• 1 Day = each chunk covers exactly 1 day of data
• Previous Month = each chunk covers the previous full calendar month

Date Range = what overall period do you want to cover? Set a Range Start and Range End using the date pickers.

Put them together and the system does the rest. For example: pick 1 Day as the interval + Jan 1 – Jan 30 as the range → 30 individual export runs, one per day, kicked off automatically.

You can now upload any file format to an Agent conversation or Agent Knowledge. Previously, only a select list of formats were allowed; we've removed the restriction on supported file types entirely.

One caveat: image file uploads are still only supported in agent conversations, not in an Agent's Knowledge.