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.
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.
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.
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:
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.
The worker parses the input to identify the subject of the report—using natural language understanding to recognize entities like company names or industries.
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.
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.
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.
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.
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.
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.
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:
Using natural language, you tell the platform what kind of worker you want. The wizard auto-generates a base structure and gives you suggestions.
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.
The investment report AI Worker offers meaningful advantages:
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.
Every report follows the same template, every time. No more variability between analysts or missed sections due to human error.
Since the worker pulls directly from approved sources, the risk of copy-paste errors, hallucinations, or outdated data is dramatically reduced.
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.
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.
Want to create your own AI Workers but not sure where to start?
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.
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.
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.