Flatly Flatly Flatly
Sources Destinations FAQ Pricing
Log in
CRM Basic Authentication

Sync Freshsales Data to Flat Files & Spreadsheets

Freshsales

Automatically sync your Freshsales data to cloud storage and spreadsheets with Flatly's no-code, triggerless, turnkey integration service.

Historical & Dynamic Data
One-way Sync
For Non-technical Users

About the integration: Freshworks CRM is an intuitive CRM that helps sales reps take the guesswork out of sales. Flatly integrates with Freshsales's official APIs.

Connect Freshsales Which Freshsales API does Flatly use?

Available Datasets

Flatly can sync the following datasets from Freshsales.

  • Accounts by View
  • Account Fields
  • Accounts Views
  • Appointments - Upcoming
  • Appointments - Past
  • Contacts by View
  • Contacts+Details by View Combo
  • Contacts+Activities by View Combo
  • Contact Fields
  • Contacts Views
  • Deals by View
  • Deals+Details by View Combo
  • Deal Fields
  • Deals Views
  • Leads by View
  • Leads+Details by View Combo
  • Leads+Activities by View Combo
  • Lead Fields
  • Leads Views
  • Sales Activities
  • Tasks - Open
  • Tasks - Overdue

Send Freshsales data to

Flatly can sync Freshsales data to any of these destinations.

Note: Some destinations are aliased, for all intents and purposes they are functionally equivalent.

Querying Freshsales - AI or ETL?

Many SaaS applications and databases can be directly queried within AI chat apps using connectors. For ad-hoc, single-user requests, this direct architecture is superior—it offers real-time visibility, high customizability, and bypasses the need for complex data pipelines.

However, ETL (Extract, Transform, Load) can be more favorable when certain requirements exist. You might consider transitioning to an ETL system and a centralized data store when:

  • Collaboration is required: Multiple stakeholders (colleagues, partners) need consistent access to the same shared single source of truth.
  • Downstream audiences: The queried data must be routed into other internal IT systems, published to team dashboards, or preserved in official records rather than remaining isolated in a chat window.
  • Data is complex: The raw data needs heavy cleaning or joining before an AI or non-technical user can accurately understand it.
  • Internal capabilities are limited: Your organization lacks the in-house programming resources or infrastructure necessary to reliably build, deploy, and maintain custom agentic workflows or complex data integrations from scratch.
  • Numerous accounts: When a high volume of identities or accounts within a data source need to be analyzed in bulk, a sophisticated connection manager is needed.
  • Concurrency & infrastructure: If many data sets need to be analyzed in parallel, concurrency can become an issue. The device hosting an AI agent can become saturated without concurrency controls in place, whereas an ETL pipeline is built to handle parallel workloads reliably.