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I’m using AI agents to map industries, the software they rely on, and the features, prices, and complaints associated with those tools before choosing a product idea. The system is helping me organize market research; it has not yet shown that it can identify product-market fit or predict whether an idea will succeed.
What the project is trying to uncover
The goal is to build a picture of local industries and the software businesses in them use: what each tool does, what it costs, and what users say they dislike or need. That information could help me judge what might be worth building, which features matter, and how a new product might differ from existing options.
I also want to work with relatively current public information and keep processing in-house, rather than depend heavily on subscription model providers. Those are project preferences, not proof that this approach produces more accurate market research or guarantees privacy.
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How the research pipeline works
The project combines AI agents with a database and a queue of research tasks. Its development has been iterative: when a broad task timed out or proved difficult to control, I narrowed its scope and made the work resumable.
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- Expand the industry map. I started with broad industry labels and asked an agent to break them into roughly 100 starting areas.
- Find incumbent software. An initial attempt to discover tools for every industry at once timed out. I switched to batches of up to 10 tools and skipped entries already in the database. I later had approximately 1,400 tools recorded.
- Research tools by data type. Instead of asking for everything about a tool in one run, I separated feature, pricing, and complaint research. The original broad task could take 10 minutes or more per tool in my experience.
- Track task state. A queue records tasks as completed or errored and resumes with pending work. A limit option lets me process a bounded number of tasks rather than launch an unbounded run.
- Store and inspect the results. The database layer uses SQLite with SQLModel/SQLAlchemy, with the possibility of moving to Postgres later.
The practical lesson for this project is to treat research as incremental data work, not one giant agent prompt. Small batches and explicit task states make interrupted runs recoverable. Separating features, prices, and complaints also makes each task easier to constrain. These are lessons from the workflow, not results from a controlled test proving that the changes improve speed or accuracy.
The tools I’m using
My reported stack includes Ollama running Ministral-3:8B for reasoning and tool use, Llama 3.2 for simpler structured reasoning and categorization, and opencode to run agent tasks with web search and fetch tools. Python handles project commands, while SQLite and SQLModel/SQLAlchemy provide the current storage layer.
My impressions of model behavior, context limits, and rate limits come from personal use during the project. They are not current benchmark results or a guarantee of how these tools will behave in another setup.
What the database contains—and what the counts mean
At the time I described the project, I had collected approximately 27,000 feature records, 7,500 complaint records, and 2,000 pricing entries. These are approximate, self-reported project counts. They show the scale of the collected material, not how completely each industry or vendor has been covered, or how accurate every record is.
That distinction matters most for complaints: collected reports need source review and context. A complaint in the database is not, by itself, a verified product defect.
Why feature research is still difficult
Vendors describe similar capabilities in different language, and product information is distributed across sites with different structures. My current feature prompt asks an agent to search the vendor’s site or other sources, list key features, and return a JSON array. That remains search-heavy; runs can time out or fail when pages are inaccessible or details are not disclosed.
A useful next experiment would make the evidence trail explicit and cap the search:
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- Prefer current official product, documentation, and pricing pages. Use secondary sources only to fill a clearly identified gap.
- Check a small, fixed number of pages, then stop rather than search indefinitely.
- For each feature, record a concise evidence excerpt, page title, URL, and access date.
- Separate capabilities the source explicitly documents from interpretations of its marketing language.
- Return “not found” or an empty result when evidence is unavailable, and record access failures separately instead of guessing.
- Keep a concise canonical feature label alongside the vendor’s own wording, so later taxonomy work does not erase the source terminology.
This is a proposed experiment, not a prompt I have reported testing. It also cannot assume every product has one definitive feature page; sites vary in structure and terminology.
How I’m approaching classification and charts
Even when products offer comparable capabilities, their feature labels may not match. I have tried assigning categories and am considering repeated classification passes, followed by deduplication of category labels. Another open question is whether a category should apply across industries or be specific to one.
Examples of cross-cutting needs I want to investigate include user management, role-based access control, and regulatory compliance. The project account uses “OHSA” as an example; that acronym should not be treated as a verified regulatory reference without checking the relevant regulator and terminology.
I have generated feature-frequency charts and heatmaps, but very large charts become noisy. A more useful comparison would focus on a defined slice of records and make its coverage visible. Potential axes include industry, feature category, complaint category, and pricing information. Looking for relationships among those dimensions or changes over time is still an analysis idea, not a finding from this project.
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The sample report currently lists available tools and counts of their recorded features and complaints. A fuller industry report would also need information such as market size and business counts. Without that context, a list of tools and record totals is an inventory, not a measure of opportunity.
I am also cautious about publishing reports that could be incorrect or unfair to a tool vendor. Complaint records need to be checked against their sources and presented with context; a count alone cannot establish how common an issue is or whether the product is defective.
What this has demonstrated so far
The system is incomplete, and it does not automatically recommend products to build. I hope eventually to use better data and reports to generate 10–20 possible product concepts, perhaps with SWOT-like analysis. That is a future goal, not evidence that the pipeline identifies real demand.
So far, the project demonstrates a way to collect and organize software-market information with agents, batches, and recoverable tasks. It does not provide a controlled speed or accuracy benchmark, an independently validated market-size estimate, or evidence that a recommendation has been built and adopted. As GroverTek put it: “Simple idea, complex project.”
Quick Recap
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