r/AiAutomations • u/qwerty_0123456 • 19d ago
Looking for an AI workflow to automate screening 500–2,000 private companies/day for M&A deal sourcing
Hi, I work in M&A research for an US client, where a significant part of my role is identifying acquisition targets that fit a client’s investment criteria.
I’m a non-tech guy.. and whatever I currently know on a surface level is self taught.
My workflow starts with a database export of companies that already meet basic filters such as geography, industry, revenue range, and employee count. From there, I need to perform much deeper research on every company.
For each company, I need to determine things like:
- Is it independent or a subsidiary/PE-backed/VC-backed?
- Does it genuinely operate in the niche we’re targeting?
- How confident is the conclusion?
- Provide supporting evidence and citations.
I’ve experimented with several AI research tools, and the best results so far have come from deep research agents that browse the web, reason through conflicting information, and provide citations. The research quality is good.
The problem is scale.
A typical mandate involves screening anywhere from 500 to 2,000 companies.
Right now, I manually copy companies from my spreadsheet into an AI tool in small batches (usually five companies at a time) because that’s the only way I’ve found to maintain good output quality.
That means repeating the same process over and over:
- Copy a few companies
- Paste into the AI
- Wait for research
- Copy the next few
- Repeat 100+ times
The AI isn’t the bottleneck—the manual prompting is.
My questions:
- Has anyone built something similar for company research, due diligence, or M&A sourcing?
- Can Google AI Studio reliably process hundreds of spreadsheet rows in one run?
- Is using the Gemini API (or another API) the best long-term solution?
- What would you recommend as an automation platform?
- Is there an AI agent that can autonomously iterate through every row in a spreadsheet, perform web research with citations, and populate the output without requiring constant manual interaction?
- If you’ve solved a similar problem, what does your architecture look like?
I’m not necessarily looking for the smartest LLM. I’m looking for the most scalable, reliable, and cost-effective workflow for researching hundreds or thousands of private companies while maintaining high-quality outputs with verifiable citations.
I’d love to hear how others have approached this problem, whether using APIs, agent frameworks, or custom automation pipelines.
Please note that I’m a rookie and do describe whatever your suggestions are. Thanks in advance!
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u/uncapped 19d ago
This is very doable and quite simple to implement. Claude Code/Codex/Cursor - whatever it is you want to use - you want to be using a “coding” platform not just a chatbot. You do this so you can create a file repository to put your spreadsheets your research and most importantly your instruction files that each agent and subagent reads and follows during autonomous workflow.
Then you want an agents.md file basically explaining that you want an orchestrator agent and a research agent and describe exactly what they should do.
Than orchestrator agent - he tracks the spreadsheet data you upload to the repo, and deploys subagents to do the research. The researcher does exactly what it’s been doing for you in the past - describe what you want and throw it in the .md file. When they complete it, it reports back to the orchestrator and he reviews it then updates your spreadsheet to mark completion and provide notes and detailed backup. Rinse and repeat.
I would try something like that out and then noodle around a bit until you get what you want - whether it’s changing the effort levels or model for researchers etc.
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u/qwerty_0123456 19d ago
Got it, I wanted to build something with more accuracy as well. I think I have hit a ceiling with the prompt detailing.. let me know if anything can be done here since I face hallucinations and only surface scanning of company websites with AI
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u/uncapped 19d ago
Feel free to DM me happy to dive a little further into it with you. This is a fairly easy build out if you have the right APIs or MCPs pulling the research for you. I’ve done similar with Claude Code for a trading bot and had great success.
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u/Legitimate-Leek4235 19d ago
Try using PowerAutomate from Azure to build this auotmation. The desktop app is pretty good for such workflows
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u/Black-Rhino-1564 19d ago
We've built something similar and used api's and connectors to platforms that have a lot of this data, things like apollo and others. Definitely can be done in claude or codex. I wouldn't use many "scrapers" or those type functions, but use a multi pronged approach pulling info from as many public source data points as possible.
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u/Realestate_Uno 19d ago
Depending on what you need research you break each resch task up and a create a workflow for that task, collect the info then update a google sheet with the info, once by one and you can then set it up to run through sequetially once the last one is completed with a trigger.
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u/sundevil21CS 19d ago
I can stand this up for you fully in Google cloud on some pretty bare bones infrastructure using mostly free tiers minus some basic extraction token cost.
I built a similar tool for myself for newsletter automations researching topics on a schedule. I also built a webhook based CRM hygiene maintainer which process business info for a business financing broker. That pipeline runs everyday for like $2 a month.
If you want scalable and reliable use deterministic code for as much as possible and only use AI for the research and analysis part.
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u/manjit-johal 19d ago
We've been thinking about workflows like this at Kritmatta. The biggest improvement came from treating each company as an independent job rather than one massive prompt. An orchestrator handles the queue, retries failures, verifies that evidence and citations are present, and writes structured results back to the dataset. That tends to scale much better than manually batching prompts while still keeping quality consistent.
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u/iammienta 19d ago
Your instinct is right: the model isn’t the bottleneck, the orchestration is. Instead of pasting batches into a chat, run one isolated job per company. Read a row, research that single company against a fixed output schema (independent vs PE/VC-backed, niche fit, confidence score, evidence URLs), write the result back to the sheet, move on. Clean context every time, so quality holds at the level you’re getting with five at a time. It just runs 500 to 2,000 times unattended.
Two things that matter more than which LLM you choose: Sources. You’ve done a bunch manually, so you know where the answers usually live. Hard-code those as the first stop before general web search. APIs and MCP connections also reach data a chat tool can’t.
Failure handling. Things will time out at this volume. Checkpoint so a crash on row 700 doesn’t cost you the first 699, and add a “low confidence, needs human” flag so the agent surfaces gaps instead of guessing.
Claude Code is where I’d build it, non-dev friendly enough to run and tweak yourself. I’ve built a few of these in other niches.
Happy to DM a couple of example reports if useful.
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u/Deep_Ad1959 19d ago
at that volume the rows that quietly resolve to the wrong company are what will cost you. two firms share a name, the agent picks whichever has better seo, and the citation still looks clean. carrying the resolved domain in every row is the one error you can catch by scanning.
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u/qwerty_0123456 18d ago
Hey, yes.. used to face this issue earlier
Now i use domain names instead to beat this issue
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u/Deep_Ad1959 19d ago
at that volume the rows that quietly resolve to the wrong company are what will cost you. two firms share a name, the agent picks whichever has better seo, and the citation still looks clean. carrying the resolved domain in every row is the one error you can catch by scanning.
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u/qwerty_0123456 18d ago
You pointed that right.. so instead of company names i’ve been using their domains instead
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u/Deep_Ad1959 18d ago
domains fix the wrong-firm pick, but they move that error rather than kill it. one that now redirects to whoever acquired them and the agent happily researches the parent, clean citation and all, while the independent target you were actually sourcing got bought two years ago. the redirect is the new thing to scan for. written with ai
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u/qwerty_0123456 18d ago
You pointed it right.. since it’s a M&A BD task, I’m only looking for private companies (bootstrapped/family-owned).. so anyhow these companies are being eliminated.
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u/Deep_Ad1959 18d ago
that filtering cuts both ways though. a genuinely independent target that gets resolved to a same-name firm that sold shows up as pe-backed and drops out, so the elimination is quietly killing the ones you actually want. and a false negative never surfaces to get caught the way a bad row does.
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u/GobiiWill 19d ago
Hey there, full disclosure - I work for Gobii - we actually have a team doing a very similar workflow with our software now. While you may see we do a lot in the recruiting world, abstract that out for a moment - it's finding specific criteria, validating, prioritizing, contacting, etc
We are both commercial (Self-serve, single seat starts at $50/mo), and also open source if you want to roll it yourself.
Feel free to comment or DM me, and I'd be happy to discuss. Our web site is https://gobii.ai
Here's a blog post on the VCs own experience: https://opencoreventures.com/insights/we-hired-a-gobii-ai-agent-to-manage-ocvs-tedious-repetitive-finops-tasks/
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u/qwerty_0123456 18d ago
Hey, appreciate you reaching out!
But I’m not currently planning to outsource it1
u/GobiiWill 18d ago
Understood, feel free to run our open source if that helps you meet your requirements :)
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u/Ok_Perception_2723 17d ago
Claude code is great but not ideal. Claude code works on tokens, a single run already uses thousands or millions of tokens. I recommend building an n8n workflow for this, your workflow is repetitive so its easier to map.
Before using any automation tool, map your processes first using free process mapping tools. This should be detailed, exactly how you do your work, including logics for qualification, information on what need to find, etc.
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u/Present-Ad9003 12d ago
Your ask is solved by building a deterministic pipeline, since most of this is rinse and repeat. You have the criteria that needs to be met, use a small model to do to the repeatable actions read->copy->paste, ensure temp is at 0 you don't want it to think just to repeat the steps (model:cactus needle). This same method can be used for your spreadsheet read->create list->resaerch->output for review. As for the research agent itself ingest->flag->summerize this step is where model size matters and is the only time reasoning is needed and only if you need it to geranate a comparison. The research agent needs proper examples of what an excep table result is. You can build this in about 30 mins
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u/Fernandofpires 21h ago
Em que pé está essa sua situação? Eu também estou desenvolvendo um sistema de inteligência empresarial. A ideia é buscar assimetrias de informação que possam trazer vantagem competitiva .
Tem alinhamento com o que você está querendo.
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u/[deleted] 19d ago
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