Automate Chat Support Across Big Data Cloud with AI
Purpose-built AI agent for Chat Support — connects to Big Data Cloud in minutes so your team can stop doing the work by hand.
84 chats handled overnight. Sample resolution:
Customer
“Hey — my Slack agent stopped firing after I rotated the workspace token yesterday. Anything I need to do on my end?”
Agent draft · in your tone
Hi Lara — totally normal, the new token needs a quick re-auth. I've sent a one-click reconnect link to your DeskFerry inbox; once you tap it the agent will pick up where it left off (no re-training needed).
How does Big Data Cloud work for Chat Support automation?
Big Data Cloud works for Chat Support automation by powering a DeskFerry agent that runs the workflow end-to-end inside your existing tools — no code, no custom build.
- 01
Trigger fires
The agent connects to Big Data Cloud alongside the other apps your team already uses, watches for the triggers that matter for Chat Support, and takes the next step on its own while keeping a complete audit trail for review.
- 02
Answer live @Intercom chats using @Big Data Cloud in our brand voice
AI handles common questions immediately, reducing wait times to zero for routine inquiries.
- 03
Escalate the rest with full context to @Zendesk
Teams typically see instant around the clock once the agent is in production.
- 04
You approve
You stay in control: every action is logged, confidence thresholds are configurable, and anything ambiguous is queued for a human instead of being silently auto-completed.
How you tell it what to do
Built in plain English.
You write the rule the way you'd describe it to a teammate. The agent reads the rule, breaks it into the actions it'll take, and confirms the apps it'll touch — before it does anything.
- 1Answer live @Intercom chats using @Big Data Cloud in our brand voice
- 2Escalate the rest with full context to @Zendesk
How it connects
Connect Big Data Cloud. The agent does the rest.
Claude and ChatGPT are already running on our side. You connect Big Data Cloud with one click, and chat support runs inside it.
Claude and ChatGPT run on our keys. Nothing for you to configure.
- Big Data CloudConnect
- IntercomConnect
- ZendeskConnect
Runs on your data, in your apps.
Nothing to deploy. Nothing to maintain.
Actions
What Big Data Cloud + DeskFerry can do
Real Big Data Cloud actions your AI agent can perform automatically — no manual work required.
Pull report data
Extract metrics, dimensions, and time-series data from Big Data Cloud dashboards for use in downstream workflows.
Schedule automated reports
Generate and distribute periodic reports from Big Data Cloud to stakeholders via email or messaging channels.
Monitor KPI thresholds
Watch key metrics in Big Data Cloud and trigger alerts when values cross defined thresholds or show anomalies.
Combine data sources
Merge datasets from Big Data Cloud with other analytics platforms to build unified cross-channel views.
Generate narrative summaries
AI interprets charts and tables from Big Data Cloud and produces plain-language summaries of trends and outliers.
Create custom dashboards
Build tailored dashboard views in Big Data Cloud by selecting metrics, filters, and visualization types programmatically.
Export data snapshots
Capture point-in-time data exports from Big Data Cloud and store them for historical comparison and audit trails.
Forecast trends
Apply predictive models to historical data from Big Data Cloud and surface projected values for planning and budgeting.
Hi Lara — totally normal, the new token needs a quick re-auth. I've sent a one-click reconnect link to your DeskFerry inbox; once you tap it the agent will pick up where it left off (no re-training needed).
Customer reports a duplicate charge; refund queued, awaiting confirmation.
Customer asking what's included on the Growth plan vs. Pro.
Human in the loop
Approve before it sends.
Every draft lands in a review queue. You approve, edit, or reject — the agent never acts on its own unless you explicitly turn that on for a workflow you trust.
Governance
Every action, with the reasoning attached.
Each step the agent takes is logged with what it did, why it did it, and which app it touched. Audit-ready, so security and compliance can sign off without backfilling.
- Lara Knight9:14 AM
Customer marked the resolution as helpful.
- Agent9:12 AM
Sent reply on ticket #9281.
Reason: Confidence above auto-send threshold; voice match passed; SLA at-risk.
- Agent9:11 AM
Drafted reply in your team's voice.
- Agent9:10 AM
Pulled customer plan, prior tickets, and account context.
- Agent9:09 AM
Triaged #9281 as the matching topic.
How it works
Get started in three steps
Step 01
Connect Big Data Cloud
Link Big Data Cloud to DeskFerry and your data pipelines start syncing within seconds.
Step 02
Define Data Workflows
Choose which Big Data Cloud datasets, reports, or dashboards trigger AI actions — and configure transforms and delivery rules.
Step 03
Automate Insights Delivery
AI processes your Big Data Cloud data on schedule, surfaces anomalies, and distributes reports to stakeholders automatically.
Start automating Chat Support for Big Data Cloud
7-day free trial. Works with the tools you already use.
FAQ
Frequently asked questions
Can I customize which Big Data Cloud events trigger Chat Support actions?
Yes. You define exactly which Big Data Cloud events start Chat Support workflows — new records, status changes, messages, or custom triggers. Each trigger can have conditions so Chat Support actions only fire when your specific criteria are met in Big Data Cloud.
Can the Big Data Cloud Chat Support agent also work with other tools in my stack?
Yes. The Chat Support agent connected to Big Data Cloud simultaneously interacts with 1,500+ other apps — CRMs, databases, email platforms, and more. A single Chat Support workflow can pull data from Big Data Cloud, process it, and push results to multiple destinations.
Can I test Chat Support automation with Big Data Cloud before going live?
Yes. You can run Chat Support workflows in test mode using sample Big Data Cloud data before activating on live records. This lets you verify every Chat Support rule works correctly with your Big Data Cloud setup before processing real data.
How long does it take to set up Chat Support automation with Big Data Cloud?
Most users connect Big Data Cloud and launch their first Chat Support automation within 10 minutes. The guided wizard handles OAuth authorization, and you configure Chat Support-specific rules through a visual no-code builder.
Can I run multiple Chat Support workflows with different Big Data Cloud triggers?
Yes. You can create parallel Chat Support workflows that respond to different Big Data Cloud events or conditions. For example, one Chat Support flow for new Big Data Cloud records and another for updated ones — each with independent rules and actions.
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