Why Your Support Team Deserves Lumea

Introducing the smarter way to handle tickets, cut down on context switching, and keep users happy.

Why Support Still Feels Manual

Solving a support ticket isn’t just a matter of checking a few logs.

Application support engineers often have to dig through fragments of unstructured information just to figure out what’s really going on. Until recently, there wasn’t much in the way of technology that could make sense of all those old tickets, code and technical documentation.

The arrival of LLMs changed everything. These models are ideally suited to the kind of unstructured, fragmented data support teams deal with every day—such as tickets, code, and documentation. Engineers can ask questions using plain language, and the LLM then applies reason to uncover root causes, retrieve relevant history, and suggest next steps. Combined with human judgment, they make support engineering faster and more effective.

What It Really Takes to Keep Systems Running

An application support engineer does far more than simply resolving tickets. They also proactively tackle bug fixes, roll out upgrades, and validate systems. But the challenge is the same: making sense of scattered, unstructured information to understand what’s going on and what might break next.

Clues are everywhere – and often nowhere

Each task involves a fair bit of detective work/sleuthing. The engineer has to piece together a clear picture using fragments from all over:

• Past tickets that might hold a clue
• User guides that show whether the application is being used as intended
• Configuration, integration, or customization guides (it might just be a setup issue)
• The original application requirements – is it a bug requiring a fix or merely expected behavior?
• And of course, the source code

It’s not just about fixing things. It’s about understanding systems well enough to apply the right patch — and anticipate what downstream components could be impacted. Most organizations rely on a ticketing system and a store of support history – whether that’s Azure DevOps, Jira, Trello, or another platform – but the depth and quality of documentation can vary wildly.

The job never seems done

Even when you’ve applied a fix or a workaround, you’re not done. You still need to explain what happened, and update the documentation and support playbooks stored in platforms like Confluence or SharePoint, for example. Then, tweak the onboarding manual so others don’t hit the same wall.

Why the pressure never lets up

Then comes the Root Cause Analysis: a deep dive into why it happened and how to prevent it. That means pulling evidence from those same fragmented sources – again.

It’s no wonder support is seen as one of the most stressful jobs in tech. For many, it’s but the first step towards a career in engineering. The churn rate is high, documentation frequently out-of-date, and systems more and more complex.

In this environment, knowledge quickly slips through the cracks unless it’s captured and made accessible. Structured knowledge bases and disciplined documentation practices are essential—but they’re not enough. The real breakthrough is how GenAI tools like Lumea help teams navigate complex systems and enable even inexperienced juniors to find the answers they need—fast.

Tools Often Don’t Play Well Together

A big part of support work involves sifting through unstructured data such as logs, tickets, wikis, code, and more. And LLMs are ideally suited to parsing scattered, messy information and drawing useful connections.

The real issue isn’t whether GenAI can help. We’ve already proven that it can with Lumea.

The problem is that most GenAI tools don’t connect to the systems engineers rely on and they don’t work well together. The result is more context switching, more manual effort, and still no clear answers. These platforms weren’t designed with support workflows in mind — and that’s why they fall short. We explore that below.

Knowledge base platforms 

The AI-powered functionality of these platforms allows you to search stored knowledge using natural language. That’s useful – if you’re only looking for documents. But support engineers often need more than that. These tools typically can’t search outside the knowledge base – like past tickets, for example.

Ticketing systems

Examples: Jira Helpdesk, Azure DevOps, ServiceNow, Linear, Trello
Most ticketing platforms now support natural language searches – usually to retrieve past tickets. That’s helpful – but still limited. These tools can’t search outside the ticketing system, so they miss relevant documentation and code that could explain the issue.

General-purpose AI assistants

Examples: ChatGPT, Claue.ai, Gemini, etc.

These can help – but only if you feed them the right context.
But therein lies the rub. You still have to manually upload the ticket, attach relevant history, and prompt them for a response. Then you need to manually prompt it again to provide you with a concise resolution comment. And because they can’t access your systems, they don’t pull from your support history or documentation. You end up doing all the heavy lifting yourself.

Enterprise Search tools and Copilots

Examples: ChatGPT Enterprise, MS Copilot, Glean

These powerful tools are usually connected to a wide range of knowledge sources and search them well. But being general-purpose tools, they’re not built for the realities of application support. You can’t fine-tune your query to isolate which knowledge sources to search – or differentiate between similar components used across multiple applications.

And GenAI tools struggle with legacy code if prompts lack the deep context an experienced engineer would bring. Without that, it’s easy to misread what the code is doing or why it exists in the first place.

AI-powered Intelligent Development Environments (IDEs)

Examples: Cursor, Github Copilot

IDE copilots are great at navigating and analyzing code — and they’ve made life easier for many developers. But they weren’t built for application support. Even when connected to Jira or Azure DevOps, they fall short when support depends on pulling context from multiple projects and documentation sets spread across tools that don’t talk to each other.

Why Support Feels Like a Daily Fire Drill

Support engineers constantly switch between platforms, passing bits of context from one to the next to run a search or piece together an analysis. Documentation here, some tickets there, and code from somewhere else.

With today’s tools you wouldn’t expect this to be hard. But in reality, we couldn’t find a product that made it easy to search across all the knowledge sources support teams use.

So we built our own: Exadel Lumea.

Finally, Everything in Sync

Lumea doesn’t try to pull everything together in one place – it doesn’t have to. That’s not how it works. Instead, it makes sense of the question and the context behind it before connecting across your systems to make scattered knowledge instantly usable.

Here’s how it works:

• Fully integrated search. Lumea connects to all your knowledge sources – and lets you configure the search based on your specific needs. You can limit it to a few code repositories or expand it to include a specific Jira project, Azure DevOps board, or documentation space in Confluence or SharePoint.
• Built with SDLC in mind. The secret sauce is how Lumea pre-processes your data prior to search. Now searches return more accurate results, even across messy codebases and scattered documentation.
• Profiles tailored to your needs. You can configure different output modes depending on the audience:
  – Technical outputs like architecture diagrams or code snippets
  – Simple, clear responses for non-technical users
  – Documenting updates in a consistent, ready-to-publish format
  – Multilingual responses for teams and clients across different regions

Lumea harnesses LLMs to turn disconnected data into fast, usable answers – giving human professionals the leverage they need to stay focused and deliver faster fixes.

Proof in Action: Support That Speaks Every Language

Support teams using Lumea are already seeing the difference.

One example: Managed services teams supporting proprietary trading desk platforms for banks across the Americas. They rely on Lumea to bring together documentation, tickets, and onboarding materials into one unified support environment.

Crucially, Lumea integrates directly with:

• The trading desk platform and its documentation
• Jira or Azure DevOps for platform support tickets
• The onboarding wiki or Sharepoint workspace used across teams

In Latin America, customer teams handle incoming requests in Spanish or Portuguese – while most technical documentation is in English. With Lumea, they can search English documentation and code in their own language, and generate responses in the language of the user. It’s faster, smoother and much better for everyone involved.

This example shows how Lumea connects multiple systems, translates knowledge across languages, and tailors responses to the user.

The Future of Support Isn’t More Tools

Ask any support engineer – application support is rarely straightforward.
With tight timelines and scattered information, engineers always scramble to respond before they have the full picture.

That pressure isn’t going away. What’s changed is how teams handle it. Lumea gives support teams what they’ve been missing: one interface to access and make sense of documentation, tickets, code, and onboarding materials. That means fewer fire drills, faster fixes – and happier customers.

Exadel Lumea. Built by engineers – for engineers.