Why Real-time Data Synchronization Matters More Than Ever

Teams today run on multiple tools. Your development team works in Jira, your sales team lives in Salesforce, and your customer support agents rely on Zendesk or ServiceNow. 

The problem: Data gets stuck in each of these platforms like islands, disconnected from the rest of the business.

Without synchronization, teams pass data around manually through emails, phone calls, or worse, by sharing credentials or buying extra seats just so another team can see what’s going on. That approach doesn’t scale, and it introduces errors, delays, and security risks at every step.

Real-time data synchronization solves this by keeping information flowing between systems automatically, the moment something changes. No manual handoffs, no stale data, no guesswork.

This blog covers what real-time data synchronization actually is, the different types, practical use cases across industries, the challenges you’ll run into, and how tools like Exalate make the process reliable and secure.

Key Takeaways

• Real-time data synchronization keeps data consistent across systems the moment changes happen, eliminating manual handoffs and stale information.
• One-way syncs work best for migrations and reporting consolidation, while two-way syncs keep collaborative workflows aligned across teams.
• Event-driven architectures outperform polling-based approaches for true real-time responsiveness.
• Common use cases include ticket escalation, MSP environments with SLA obligations, cross-company collaboration, and data consolidation for reporting.
• Conflict resolution, latency management, and hybrid system connectivity are the biggest challenges to solve.
• Choosing the right tool means evaluating sync speed, scripting flexibility, security posture, and support for your specific platforms.

What Is Data Synchronization?

Data synchronization is the process of exchanging data between different systems while keeping that data accurate, consistent, and accessible to the people who need it. 

It’s a continuous process. When something gets created, updated, or deleted in one system, the synced system reflects that change.

Synchronization can happen between all kinds of data sources: mobile devices, data warehouses, data lakes, ERPs, legacy systems, or standalone applications. In this blog, we’re focused on synchronization between business applications like CRMs, ITSM platforms, and project management tools.

Here’s a quick example. 

A customer raises a high-priority ticket in Zendesk. With data synchronization in place, a Bug is automatically created in the development team’s Jira instance. Statuses, comments, priorities, and attachments are all exchanged based on predefined sync rules. Every change in Zendesk (the source) gets reflected in Jira (the destination), and vice versa.

That’s data synchronization in general. But there’s a critical distinction to make: real-time versus batch.

What Is Real-time Data Synchronization?

Real-time data synchronization, or real-time data sync, is the continuous exchange of data between systems where changes in one system are immediately reflected in others.

The keyword here is “immediately.” We’re talking about instantaneous or near-instantaneous data replication. Users in connected systems always see the most current information without refreshing their screens or waiting for a scheduled job to run.

For some scenarios, batch synchronization is perfectly fine. A retail company that syncs sales data from multiple stores to a central database every night doesn’t need real-time updates. Nightly batch processing keeps the central system current without straining networks during business hours.

But for time-sensitive workflows, real-time sync is non-negotiable. Incident management, financial transactions, order processing, and cross-team collaboration all break down when data arrives late. A 15-minute SLA on incident acknowledgment doesn’t wait for the next batch cycle.

Real-time vs. Near-real-time vs. Batch Synchronization

These three terms get used interchangeably, but they mean different things, and the distinction matters when choosing a synchronization approach.

Real-time synchronization propagates changes within milliseconds to a few seconds. The source system pushes data to the destination as soon as a change event occurs. This is event-driven: a trigger fires, data moves, and the connected system updates. Financial trading platforms and emergency response systems typically require this level of immediacy.

Near-real-time synchronization introduces a small, controlled delay, usually seconds to a few minutes. Changes are still detected as they happen, but they may be queued briefly for batching efficiency or conflict resolution before being applied. Most enterprise integration tools, including Exalate, operate in this near-real-time range. For practical purposes, this feels instantaneous to end users.

Batch synchronization collects changes over a defined period (hourly, nightly, weekly) and processes them all at once. It’s efficient for high-volume, low-urgency data transfers like nightly data warehouse updates, end-of-day financial reconciliation, or periodic CRM data aggregation.

The right choice depends on your workflow. If a support agent needs to see the development team’s status update within seconds, near-real-time or real-time sync is essential. If you’re aggregating monthly reports, batch sync is more practical and less taxing on system resources.

How Real-time Data Sync Works: Event-driven vs. Polling

Under the hood, real-time synchronization relies on one of two architectures: event-driven or polling-based. Understanding the difference helps you evaluate tools and set realistic expectations.

1. Event-driven synchronization relies on triggers. When a record is created, updated, or deleted, the source system fires an event (commonly a webhook or a change notification). The integration platform listens for these events and immediately processes the change. This is the faster, more efficient approach because data only moves when something actually changes. There’s no wasted processing on “nothing happened” checks.
2. Polling-based synchronization works on a schedule. The integration platform checks the source system at regular intervals (every 30 seconds, every minute, every 5 minutes) to see if anything has changed. If changes are found, they’re processed. If not, the cycle repeats. Polling is simpler to implement but inherently adds latency equal to the polling interval. It also puts more load on the source system’s API.
3. Most mature integration platforms use a hybrid approach. They rely on event-driven triggers for primary change detection and fall back to polling as a safety net to catch anything the event system might have missed. Exalate, for example, uses its own transport layer with an event-driven architecture that triggers immediate updates when changes occur in connected systems, combined with retry mechanisms that ensure nothing gets lost during network interruptions.

When evaluating tools, ask how they detect changes. Pure polling at 5-minute intervals isn’t real-time sync; it’s scheduled sync with a short interval. True real-time requires event-driven processing.

Types of Real-time Data Synchronization

One-way Real-time Data Sync

One-way data synchronization means data flows in a single direction: from the source system to the destination. The reverse path doesn’t exist.

You control exactly what gets transferred through your sync rules. For instance, you might sync work item summaries, keys, statuses, descriptions, comments, and attachments from Jira to ServiceNow, with no information flowing back from ServiceNow to Jira. All updates made in Jira are instantly reflected in ServiceNow.

One-way syncs are the best fit for scenarios like live migrations between systems, say from Jira to Azure DevOps, or for consolidating data from multiple sources into a single reporting platform.

Two-way Real-time Data Sync

Two-way real-time sync means data flows in both directions simultaneously. Changes, insertions, deletions, and field updates in either system are instantly reflected in the connected system.

For example, when a ServiceNow incident is created, a Bug is opened in Azure DevOps. Statuses, priorities, urgencies, comments, and other fields stay in sync. Any change triggered in either system immediately updates the other based on the rules you’ve defined.

Two-way sync is essential when both teams are actively working on the same data. Support agents update ticket statuses in Zendesk while developers update Bug resolutions in Jira. Both sides see the latest information without switching platforms.

There’s no more like, “Oh, you created a ticket at your end, but we didn’t see the ticket at our end.”.

– CARLOS ALMEIDA
VP OF ENGINEERING SPK AND ASSOCIATES

The important thing about real-time synchronization, whether one-way or two-way, is that it keeps your existing workflows intact. Each team continues working in their own platform and receives the data they need without switching tools or refreshing screens.

Why Is Real-time Data Synchronization Important?

There are concrete reasons why companies choose real-time data synchronization over batch-based approaches.

Faster Incident Resolution and Customer Responsiveness

In incident management, order tracking, or financial account updates, real-time sync ensures customers and internal teams always see the latest status. When a support ticket is escalated to development, the support agent sees the developer’s progress in real time. There’s no “let me check with the team and get back to you.”

Better Decision-Making from Current Data

Business decisions are only as good as the data behind them. Real-time updates give leaders access to the latest metrics, whether that’s customer feedback trends, pipeline changes, or operational bottlenecks. Waiting 24 hours for a batch sync to reveal a trending product defect means 24 hours of avoidable customer churn.

Operational Efficiency and Reduced Manual Errors

Manual data transfer is error-prone, time-consuming, and pulls your people away from strategic work. Automated real-time sync eliminates copy-paste errors, reduces the cost of rework, and frees up teams to focus on what they were actually hired to do. When you factor in the security risk of sharing credentials across teams just so they can access another system’s data, automated sync becomes even more critical.

SLA Compliance in Partner and Vendor Relationships 

For MSPs and outsourced service providers, SLAs often include response time commitments measured in minutes. A 15-minute SLA on incident acknowledgment requires real-time visibility into incoming tickets. Batch sync at hourly intervals would guarantee SLA breaches.

Cross-company Collaboration Without Shared Access 

Real-time sync lets organizations collaborate with external partners, vendors, and clients without granting direct access to internal systems. Each side controls what they share and what they receive independently, maintaining security boundaries while keeping data current.

Practical Real-time Data Synchronization Use Cases

Data Consolidation and Reporting

Case: Task-level data from multiple ITSM and project management systems needs to be consolidated in a single platform. This often happens after mergers and acquisitions, or when management needs a unified view across business units for reporting and strategic planning.

Solution: Set up one-way real-time syncs from multiple source systems (ServiceNow, Freshservice, Azure DevOps, Zendesk) into a single destination like Jira. Sync fields like work logs, statuses, priorities, and custom metrics. Management gets a unified view of operations without asking each team for manual reports.

Real-world application: You can sync data from different cross-functional Jira projects into a single Jira instance. Work logs, statuses, and key metrics flow in real time, giving stakeholders a live reporting view without disrupting any team’s workflow.

Customer Ticket Escalation

Case: Customer tickets arriving in a support platform need to be escalated to another system for resolution. The escalation might go to L2 support, development, IT operations, or external providers (MSPs) based on ticket type, priority, or customer tier.

Solution: Configure real-time sync rules that automatically route tickets based on custom logic. When a ticket meets specific criteria (e.g., high priority, specific category, VIP customer), it’s created in the destination system instantly. All subsequent updates, comments, status changes, and attachments flow back and forth.

Real-world application: Discover how Qualco used real-time data synchronization by syncing change requests, incidents, and other entities related to their support-to-development workflow. When a customer raises a change request in Jira Service Management, it’s escalated to the development team working in Azure DevOps.

Several specific scenarios fit this pattern:

• Multi-destination incident routing. Incidents arrive in a single ITSM system like ServiceNow. Real-time sync routes them automatically to Jira, Azure DevOps, Freshservice, or another ServiceNow instance based on conditions you define, such as category, geography, or affected service. You also control which fields are shared in each escalation scenario, so development teams see technical details while business teams see impact summaries.
• End-to-end service desk orchestration. Connect multiple service desks or portals like JSM, Zendesk, Freshdesk, and Freshservice to orchestrate a complete support workflow. Tickets are routed from L1 to L2 to L3 automatically and in real time. This avoids the cost of adding expensive agent seats in a single system just so different support tiers can see the same tickets.
• Support-to-development handoff. A classic workflow automation pattern. When a customer reports a bug in Jira Service Management (JSM), it gets passed to the development team in Jira Software. The dev team updates the status in Jira, and the JSM status updates automatically. Internal comments flow to the dev team so they have full context, and support agents maintain real-time visibility into resolution progress.
• Multiple tickets, single work item. Real-time data synchronization lets you connect multiple Zendesk instances to a single Jira instance. When multiple customers report the same problem, a single Jira work item handles the fix, with links to the respective Zendesk tickets for status updates. When the developer resolves the work item, all connected Zendesk tickets update simultaneously.

MSP Environments

Case: A managed service provider (MSP) provides helpdesk outsourcing for its clients. The contract includes SLAs with monetary obligations. A high-priority incident is raised in the customer’s ServiceNow instance that needs to be escalated to the MSP’s Jira instance, with a 15-minute response time for incident acknowledgment.

Solution: Real-time sync between the customer’s ServiceNow and the MSP’s Jira ensures the incident appears in the MSP’s queue within seconds. The MSP can immediately start working on it while providing status updates and visibility to the customer throughout the resolution process.

Real-world application: Learn how MSPs like SPK leverage real-time data sync to keep their customers’ instances connected and aligned, resulting in better onboarding and stronger client relationships. With real-time sync, the incident is caught promptly, SLAs are adhered to, and the MSP maintains trust with their clients.

Cross-company DevOps Collaboration

Case: Two organizations are collaborating on a joint product or integration. Company A’s development team works in Jira, while Company B’s team works in Azure DevOps. Both teams need visibility into each other’s progress, blockers, and deliverables without granting access to each other’s internal systems.

Solution: A two-way real-time sync between Jira and Azure DevOps shares work item summaries, statuses, comments, and priorities across the organizational boundary. Each side independently controls what data they send and receive through their own sync rules. Sensitive internal fields, such as cost estimates or internal notes, are excluded automatically.

Real-world application: This is common in technology partnerships, outsourced development arrangements, and post-acquisition integration scenarios where teams need to collaborate before systems are consolidated. Exalate’s architecture gives each organization independent control over their sync configuration, so neither side can access or override the other’s settings.

CRM-to-Development Feedback Loop

Case: Product feedback, feature requests, and escalated bugs flow from the sales and customer success team in Salesforce to the development team in Jira. The sales team needs real-time visibility into development progress to keep customers informed during renewal conversations.

Solution: Two-way real-time sync between Salesforce and Jira. When a Salesforce case is flagged for development, a Jira work item is created with the relevant context. Status updates, priority changes, and resolution notes sync back to Salesforce so the sales team always has the latest information.

Real-world application: Customer success managers can tell a client “your feature request is in sprint planning” or “the fix ships next Tuesday” without pinging the development team. This tightens the feedback loop between customer-facing and product teams while keeping both sides in their preferred tool.

Challenges of Syncing Data in Real-time

Real-time sync is powerful, but it’s not without obstacles. Here are the main ones to plan for.

• Scalability under growing data volumes. As businesses grow, so does their data. Your synchronization needs to handle increasing volume without degrading speed or reliability. A solution that works fine for 50 synced work items a day might buckle under 5,000. When evaluating tools, ask how they handle load scaling. Look for platforms with queue-based processing and asynchronous transport layers that absorb spikes without bottlenecking.
• Latency and its business impact. Even small delays can have outsized consequences in time-sensitive industries. In finance, where staying aligned with consumer finance trends is crucial, a few seconds of latency can mean monetary losses. In incident management, delayed sync can mean missed SLAs. The architecture behind the sync tool, event-driven vs. polling, directly determines how much latency you’ll experience.
• Conflict resolution when both sides change simultaneously. In two-way syncs, both systems can update the same field at the same time. Without proper conflict resolution rules, you end up with data overwrites or inconsistent states. The best tools let you define conflict resolution strategies: last-write-wins, source-system-priority, or custom logic based on field type or user role.
• Network outages and system downtime. Systems go down. Networks fail. These are not edge cases; they’re certainties. The question is how your sync tool handles them. Look for platforms with built-in retry mechanisms, transactional sync queues, and the ability to process backlogged changes in sequence once systems recover, so nothing gets lost or applied out of order.
• Security during real-time data transfer. Syncing sensitive data in real time requires robust security: encryption in transit (TLS 1.2/1.3 minimum), token-based authentication, role-based access controls, and compliance with regulations like GDPR. This becomes especially important in cross-company syncs where data crosses organizational boundaries.
• Hybrid and multi-cloud complexity. Syncing between cloud apps, legacy systems, and on-premise platforms introduces architectural challenges. Connecting internet-facing public systems with firewall-protected private systems requires careful security planning and often additional infrastructure. Tools that support multiple deployment options (cloud, on-premise, Docker) handle these scenarios more gracefully than cloud-only platforms.
• Data consistency across systems with different schemas. Different platforms model data differently. A “priority” in ServiceNow doesn’t map 1:1 to a “priority” in Jira. Custom fields, different field types, and varying data structures all need to be accounted for. Script-based mapping with support for conditional logic and data transformations is essential for maintaining consistency across heterogeneous systems.

Build vs. Buy: Getting Real-time Sync Right

When companies decide they need real-time data synchronization, the first question is usually: Do we build it ourselves or buy a tool?

Building Your Own Sync Solution

It’s tempting to think a custom integration will be straightforward. Two systems, one API each, some glue code in the middle. In practice, building your own real-time sync means maintaining the integration long after the initial excitement fades.

You’ll need to handle API versioning, rate limiting, error recovery, data transformation, conflict resolution, retry logic, and security, all while keeping up with platform API changes on both ends. The ongoing maintenance cost typically exceeds the initial build cost within the first year.

We didn’t go down the road of developing a solution ourselves purely due to resource limitation because if we developed it, we’d have to maintain it and make regular improvements.

– MICHAEL FOWLER, ENGINEERING LEAD
QUORUM CYBER

Custom builds also mean building your own security posture from scratch. Encryption, authentication, access controls, and compliance certifications all fall on your team. 

For cross-company integrations, you’d need to build the entire trust and access control framework that commercial platforms already provide.

Native Data Sync Options

Most modern SaaS platforms offer built-in integrations with popular tools. These native options are convenient for simple, out-of-the-box use cases. However, they’re typically limited in scope: fixed field mappings, no custom logic, limited platform support, and often one-way only.

Native options work for straightforward scenarios, like syncing Jira notifications to Slack. They fall short when you need advanced field mapping, conditional routing, two-way sync with conflict resolution, or cross-company data exchange with independent access controls.

Third-party Data Sync Tools

Third-party synchronization tools sit between the build and native options. They range from no-code, template-based platforms (great for simple use cases) to script-based solutions (flexible enough for complex enterprise workflows).

The right choice depends on what you need. If your use cases are simple and don’t require custom logic, a template-based tool might suffice. 

If you need advanced data mapping, conditional sync rules, cross-company security controls, and the flexibility to handle edge cases, a script-based platform like Exalate gives you full control without the maintenance burden of a custom build.

How to Choose the Right Real-time Data Synchronization Tool

With so many tools in the market, picking the right one can feel overwhelming. Here’s what to prioritize.

• Sync speed and architecture. Verify that the tool actually offers real-time or near-real-time sync, not just polling at short intervals. Ask about the underlying architecture. Event-driven systems with dedicated transport layers will consistently outperform polling-based tools. Not all tools available in the market offer genuine real-time capabilities, so verify before committing.
• Scripting flexibility and data mapping. Choose tools that give you a wide range of use cases to implement. Script-based solutions let you define exactly how data maps between systems, including conditional logic, field transformations, and dynamic routing. This flexibility matters more than it seems at first, because real-world sync requirements almost always involve edge cases that templates can’t handle.
• Security by design. Prioritize tools with encryption in transit, role-based access controls, and token-based authentication baked into the platform. Security is especially critical for cross-company real-time syncs where data crosses organizational boundaries. Check for relevant certifications and review the vendor’s security documentation before evaluating features.
• Independent sync control. In cross-company integrations, each organization should control what data they send and receive independently. One side shouldn’t be able to override or access the other’s configuration. This protects both parties and ensures compliance with internal data governance policies.
• Platform coverage and extensibility. The tool should support the platforms you use today and the ones you’re likely to adopt. Look for native connectors for your core systems and custom connector support (REST API-based) for niche or proprietary platforms.
• AI-assisted configuration. Modern integration platforms increasingly offer AI-powered tools that generate sync scripts from natural language descriptions. This can dramatically reduce implementation time, especially for teams without deep scripting experience. Exalate’s Aida, a scripting assistant, helps teams build and refine Groovy sync scripts faster by generating configurations based on what you describe.

Case Study:

Michael, from Quorum Cyber, points out that the team chose Exalate for the following reasons:

• It supports near real-time synchronization
• The synchronized data doesn’t exist outside the environment
• The solution supports platform flexibility and autonomy (segregation)

Exalate: A Real-time Data Synchronization Tool

Exalate is a script-based integration platform that offers real-time data synchronization between systems like Jira, Salesforce, ServiceNow, Zendesk, Azure DevOps (Cloud and Server), GitHub, Freshservice, Freshdesk, Asana, and more. For platforms not on that list, Exalate supports custom REST API connectors, extending its reach to proprietary and niche tools.

Exalate embeds the sync status directly within the platforms you use. Data synchronization doesn’t require a screen refresh every time a new update arrives. Changes propagate automatically based on the sync rules you’ve defined.

Each side of a sync operates autonomously. In a two-way connection, both organizations (or teams) define their own rules for what data they send and what they receive. The other party cannot override or access these settings, which is critical for cross-company integrations where data governance and trust boundaries matter.

Exalate also includes Aida, an AI-powered scripting assistant that helps teams generate and refine Groovy sync scripts from natural language descriptions. Instead of writing complex scripts from scratch, you describe what you want synced, and Aida produces the script for review and refinement.

The Exalate managed service offering just sat nicely with us because it takes the burden of setting up, managing, and owning the infrastructure away from us and effectively does it on our behalf.

– MICHAEL FOWLER, ENGINEERING LEAD
QUORUM CYBER

How Exalate’s Transport Layer Enables Real-time Sync

At the core of Exalate’s real-time capability is a custom-built transport layer. This isn’t a minor technical detail. It’s the reason Exalate can deliver reliable, low-latency synchronization across platforms with different architectures and availability characteristics.

• Low latency through optimized delivery. Exalate’s transport layer is purpose-built for fast data transfer. It prioritizes getting data from point A to point B as quickly as the connected systems allow, without the overhead of generic middleware.
• Asynchronous communication. Connected systems don’t wait for each other to process data. The transport layer handles asynchronous messaging so that a slow response on one side doesn’t block the other. Updates are propagated promptly regardless of how fast each system processes them.
• Built-in retry and fault tolerance. Network interruptions, API rate limits, and temporary system outages are inevitable. Exalate’s transport layer includes integrated retry mechanisms and sync queues that track changes in order. When connectivity is restored, pending changes are applied in sequence. The system remains real-time even under imperfect conditions.
• Event-driven change detection. The transport layer works with event-driven architectures where changes in one system trigger immediate updates in the other. This enables near-instant synchronization without relying on polling intervals.
• Efficient data serialization. The transport layer uses optimized serialization formats that reduce the size of transmitted data, speeding up transfer and reducing network overhead during high-volume sync periods.
• Integrated security. Encryption protocols, JWT-based authentication, and access controls are built directly into the transport layer. Security isn’t bolted on as an afterthought. It’s part of the communication process itself, which avoids the latency that external security layers can introduce.

Conclusion

Real-time data synchronization is no longer a nice-to-have. For teams collaborating across platforms, managing SLA-bound escalations, or making decisions based on live data, it’s a requirement.

The technology behind it matters. Event-driven architectures, robust conflict resolution, built-in retry mechanisms, and strong security controls are what separate reliable real-time sync from tools that just poll at short intervals and call it real-time.

By keeping your data accurate, consistent, and current across every system your teams use, you eliminate the guesswork, the manual effort, and the delays that hold businesses back.

Want to explore what’s possible with real-time data syncs? Discuss your use case with us and take the lead!

Frequently Asked Questions

What platforms does Exalate support for real-time data synchronization?

Exalate supports real-time sync between Jira Cloud, ServiceNow, Salesforce, Zendesk, Azure DevOps (Cloud and Server), GitHub, Freshservice, Freshdesk, Asana, and more. For platforms not on that list, Exalate offers custom REST API connectors that extend integration capabilities to proprietary or niche tools. Each connector applies the same sync engine, security controls, and scripting flexibility.

How does Exalate handle conflicts in two-way real-time sync?

In two-way sync, both systems can update the same field at the same time. Exalate handles this through its scripting engine, where you define conflict resolution rules based on your business logic. You can set up last-write-wins, source-priority, or custom rules per field. For example, you might let ServiceNow always own the “priority” field while Jira owns the “status” field. This granularity prevents overwrites and keeps data consistent.

What happens to synced data during network outages or system downtime?

Exalate uses transactional sync queues that track every change in order. When a connected system goes down or a network interruption occurs, pending changes are queued. Once connectivity is restored, the queued changes are applied in the correct sequence. Nothing is lost, and nothing is applied out of order. The built-in retry mechanisms handle temporary failures automatically.

How does Exalate ensure security during cross-company real-time sync?

Each organization in a cross-company integration controls their sync configuration independently. You define what data you send and receive through your own sync rules. The other party cannot override or access your settings. Data in transit is encrypted using TLS 1.2/1.3, authentication uses JWT tokens, and role-based access controls govern who can modify sync configurations. 

What is the difference between real-time and near-real-time data synchronization?

Real-time sync propagates changes within milliseconds, typically used in scenarios like financial trading or emergency response. Near-real-time sync introduces a small delay (seconds to a few minutes) for queuing and conflict resolution. Exalate operates in the near-real-time range, which for most enterprise workflows feels instantaneous. The distinction matters mainly for latency-critical use cases where even a few seconds of delay has a measurable business impact.

Can Exalate sync custom fields and complex data types?

Yes. Exalate’s Groovy-based scripting engine gives you full control over field mappings, including custom fields, multi-select fields, nested objects, attachments, and comments. You can apply conditional logic (e.g., only sync comments tagged as “external”) and data transformations (e.g., map ServiceNow priority values to Jira priority equivalents). This flexibility is why script-based platforms outperform template-based tools for complex enterprise use cases.

How does Aida help with setting up real-time synchronization in Exalate?

Aida is Exalate’s AI-powered scripting assistant. Instead of writing Groovy sync scripts from scratch, you describe what you want in plain language, such as “sync high-priority bugs with their attachments and comments from Jira to ServiceNow,” and Aida generates the script. You can then review and refine it manually for full control. This significantly reduces implementation time and lowers the technical barrier for teams without deep scripting experience.

Is Exalate suitable for MSPs managing multiple client integrations?

Absolutely. MSPs commonly use Exalate to connect their internal systems (like Jira) with multiple client instances across different platforms (ServiceNow, Zendesk, Freshservice, Freshdesk). Each client connection operates independently with its own sync rules, so one client’s configuration doesn’t affect another’s. Real-time sync ensures SLA compliance across all client relationships, and the managed service option offloads infrastructure management to Exalate.

How does real-time data sync with Exalate differ from using native integrations?

Native integrations offered by platforms like Jira, ServiceNow, or Zendesk are convenient for simple use cases but limited in scope. They typically offer fixed field mappings, no custom logic, one-way sync only, and limited platform coverage. Exalate goes further with two-way real-time sync, granular field-level control, script-based conditional logic, cross-company security controls, and support for 10+ platforms plus custom connectors. The difference becomes obvious when your use case involves anything beyond a basic, one-directional data push.

Recommended Reading:

• Point-to-Point Integration: Explore the Strengths and Pitfalls
• Exploring the Working Principles of Service Desk Integration
• Service Integration and Management (SIAM): The Complete Guide
• Jira ServiceNow Integration: How to Connect Jira and ServiceNow in 6 Steps
• ITSM Integration: Simplify Your IT Services Like Never Before
• Automated Integration: A Key to Scalable and Agile Business Operations