How to Generate Investment Reports with AI: A Faster, Smarter Approach

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Investment teams face an increasingly complex landscape: more data, higher expectations, and less time. Yet, much of the research and reporting process remains painfully manual. Analysts chase down data across systems, copy-paste into Word docs, and scramble to meet deadlines with little room left for actual decision-making.

There’s a better way.

In this week’s edition of EverWorker Live, EverWorker’s solution architect Ionut Mercas showcased a real-world example of what’s possible when you create an AI Worker designed specifically to automate the investment report process. This blog breaks down the key takeaways, demonstrates how it works, and explores how finance teams can rethink research and reporting with purpose-built AI Workers.

 

The Problem with Manual Investment Reporting

Before making any investment decision—whether in public markets, M&A, venture capital, or corporate strategy—teams must produce comprehensive reports. These reports often include:

  • Executive summaries

  • Company history and founding information

  • Current financial metrics

  • Market forecasts

  • Source citations and links

  • Internal commentary

Traditionally, gathering this information requires pulling from a variety of sources like market databases (e.g., Octagon), research tools (e.g., Perplexity), and internal files (e.g., SharePoint folders). It’s time-consuming and error-prone. Worse, the analyst often becomes the bottleneck—not because of lack of skill, but because of how fragmented the process is.

Even generative AI tools like ChatGPT or Gemini aren’t enough. They might summarize text or reword a paragraph, but they aren’t able to autonomously gather, analyze, and structure multi-source financial information into a usable report. That’s the gap EverWorker set out to close.

From Agents to AI Workers: A Step Forward in Automation

AI assistants helped us write faster. AI agents began to do things for us. But AI Workers go a step further—they do real work.

EverWorker’s platform enables business users—not just engineers—to create what we call AI Workers: goal-oriented, always-on digital teammates that handle repeatable workflows from start to finish. These workers are designed to:

  • Autonomously complete multi-step processes

  • Integrate across internal and external data sources

  • Output structured deliverables like reports, emails, and files

  • Operate in real time, triggered by events or schedules

The investment report demo exemplifies this evolution. It’s not just a tool—it’s a digital worker that delivers the exact output a finance team needs with a fraction of the effort.

Inside the Investment Report AI Worker

In the livestream, Ionut walks through an AI Worker he created for a customer in the finance sector. The goal: automate the generation of investment reports by extracting data, synthesizing insights, and formatting the result into a polished document.

Here’s how it works:

1. Start with a Simple Prompt

The worker begins with a basic input, such as:

“Generate an investment report on [Company Name]”

That’s it. No coding. No complex setup. The worker takes over from there.

2. Extract the Company Name

The worker parses the input to identify the subject of the report—using natural language understanding to recognize entities like company names or industries.

3. Fetch Data from Multiple Sources

The real magic happens here. The worker pulls relevant information from:

  • Octagon for financial and market data

  • Perplexity for AI-driven web research

  • SharePoint for internal documents like prior reports, financial statements, or analyst notes

Any data source with an open API can be integrated, including Google, Crunchbase, Bloomberg (if licensed), internal systems, and more.

4. Process and Synthesize the Data

The next step involves a "brain" node that acts as the system prompt. It instructs the AI on:

  • What sections the report must contain

  • How the formatting should look (including tables, citations, summaries)

  • How to handle missing data (“not specified” vs hallucinated placeholders)

  • How to cite and link sources cleanly at the end

This is a major leap from general LLMs. Unlike ChatGPT sessions where output can vary wildly, the AI Worker always delivers the same structured format—every time.

5. Format the Document

Once the raw content is generated, another node formats it into a Word-compatible layout. It ensures:

  • Consistent headers and styles

  • Clean source citations

  • Proper file structure for SharePoint compatibility

The result is a decision-ready report that matches the company’s internal template and compliance standards.

6. Store and Link

The final report is automatically saved to a SharePoint folder. A link is also generated at the bottom of the document for traceability and future access.

Customizable, Repeatable, and Enterprise-Ready

What makes this AI Worker powerful isn’t just the speed—it’s the repeatability and flexibility.

Once the worker is created, it can be reused again and again:

  • By analysts running weekly research

  • By strategy teams evaluating targets

  • By investor relations preparing briefing documents

  • By operations compiling executive updates

The data sources can change. The format can change. The delivery method can change (email, SharePoint, Slack). But the structure and intelligence remain consistent.

Need to integrate another API? The EverWorker Universal Connector makes it drag-and-drop simple. Want to add a layer of JavaScript logic for edge cases? Developers can plug in code executor nodes without rebuilding the workflow.

Why Not Just Use ChatGPT or Gemini?

It’s a fair question—and one the EverWorker team addressed directly during the session.

While general-purpose AI models are useful for answering questions or generating content on the fly, they have limitations when it comes to enterprise-grade execution:

  • They require constant prompting

  • They lack access to secure, internal data sources

  • They don’t produce consistently formatted outputs

  • They’re not autonomous—you must supervise every step

In contrast, the AI Worker is:

  • Connected to both public and private data sources

  • Configurable to your preferred format and content structure

  • Autonomous once triggered

  • Reliable in generating the same type of report every time

It’s the difference between having a helpful intern and a trained analyst that follows your playbook every time.

Building Your Own Investment Report AI Worker

You don’t need to be a developer to create an AI Worker like this. EverWorker’s platform is designed with business users in mind. Using the EverWorker Canvas, users can visually build workflows using drag-and-drop logic, connectors, and prompts.

There are two core approaches:

1. Wizard-Driven Creation

Using natural language, you tell the platform what kind of worker you want. The wizard auto-generates a base structure and gives you suggestions.

2. Custom Build with Canvas

Power users and AI engineers can go deeper, integrating complex logic, data transformations, and front-end extensions.

In the case of the investment report worker, the Canvas includes nodes for:

  • Prompt parsing

  • API connectors (Octagon, Perplexity, SharePoint)

  • Language model generation

  • Output formatting

  • File creation

  • Optional email distribution

And once created, this AI Worker lives on. It can be embedded in internal tools, triggered via API, or accessed through EverWorker’s Chrome extension or Slack integration.

Real-World Benefits for Finance and Investment Teams

The investment report AI Worker offers meaningful advantages:

Time Savings

What used to take hours—data gathering, formatting, cross-referencing—now takes minutes. Teams can redirect that time to strategic analysis instead of tactical work.

Consistency

Every report follows the same template, every time. No more variability between analysts or missed sections due to human error.

Data Accuracy

Since the worker pulls directly from approved sources, the risk of copy-paste errors, hallucinations, or outdated data is dramatically reduced.

Scalability

Need 10 reports this week? 100 next month? AI Workers don’t burn out, need PTO, or lose focus. They scale with your business needs.

Beyond Investment Reports: A Foundation for Finance Automation

This isn’t just about one report. It’s about rethinking how work gets done in finance.

Once you see the ROI of a single AI Worker, the possibilities open up:

  • Due diligence summary workers

  • Competitor benchmarking workers

  • Invoice reconciliation workers

  • Budget variance analysis workers

  • Forecasting and sensitivity modeling workers

And because EverWorker handles the complexity under the hood, your team can focus on what matters: making smarter decisions, faster.

Learn to Build Workers Yourself

Want to create your own AI Workers but not sure where to start?

Enter EverWorker Academy

EverWorker Academy is a free training program designed to help business professionals:

  • Understand AI fundamentals

  • Learn how to scope and build AI Workers

  • Earn certification to lead AI initiatives within their teams

You can complete the course in under two hours and walk away with a practical understanding of how to bring agentic AI into your daily workflows. No code. No fluff. Just real-world value.

Conclusion: The Future of Financial Research Is Already Here

AI isn’t the future of investment reporting—it’s the present.

By combining structured automation, multi-source data integration, and intelligent prompting, the Investment Report AI Worker is a glimpse into what’s possible when we move beyond chatbots and embrace autonomous AI.

Whether you're a private equity analyst, corporate strategist, or FP&A lead, automating research and reporting is now within reach. And it doesn’t require hiring engineers or replacing systems. You just need the right worker.

Ready to Automate Your Financial Reporting?

If your team is still spending hours compiling reports, it’s time to ask a better question:

What would change if that work was done for you—automatically, reliably, and at scale?

Learn how EverWorker can help you create AI Workers tailored to your workflows.

Let’s build your first AI Worker—together.

Joshua Silvia

Joshua Silvia

Joshua is Director of Growth Marketing at EverWorker, specializing in AI, SEO, and digital strategy. He partners with enterprises to drive growth, streamline operations, and deliver measurable results through intelligent automation.

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