I’m using AI agents to build a research pipeline that maps industries, the software they rely on, and the features, prices, and complaints associated with those tools. The aim is to make product exploration more systematic—not to have an agent declare a guaranteed winning idea. The project is still evolving, and its collected data has not been independently audited.
What I want the agents to uncover
The project starts with a practical founder’s question: before building software for a particular industry, what tools are businesses already using, what can those tools do, and where do users say they fall short?
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I want to map local industries and sub-industries, identify incumbent products, and gather public information about their features, pricing, and user complaints. That information could help me decide what to investigate further, which capabilities appear important, and how a potential product might differ. It is input for judgment, not proof of customer demand or product-market fit.
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I also prefer processing that I can run in-house and want to reduce dependence on subscription model providers. Those are project preferences, not evidence that the approach is inherently more private or that it produces better market research.
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The stack behind the pipeline
In my current setup, I use Ollama with Ministral-3:8B for reasoning and tool use, and Llama 3.2 for simpler structured reasoning and categorization. I use opencode to run agent tasks with web search and fetch tools, Python for project commands, and SQLite through SQLModel/SQLAlchemy as the database layer. The database could later move to Postgres.
My impressions of model behavior, context limits, and rate limits come from using the stack during this project. They are time-specific experience, not benchmark results or guarantees about how these models or tools will perform for other people.
How I changed the research workflow
1. Expand broad industries into starting areas
I began with broad industry labels and asked an agent to expand them into roughly 100 starting areas. This gives the later research a more specific set of industries and sub-industries to work through, rather than asking one broad question about “business software.”
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2. Find incumbent tools in small batches
My first attempt to discover tools across industries all at once timed out. I changed the workflow to handle batches of up to 10 tools and skip entries already in the database. I eventually had approximately 1,400 tools recorded. That is my project’s count, not a verified estimate of the software market or proof that the database covers every relevant product.
3. Separate features, pricing, and complaints
A single broad task asking an agent to explain a tool could take 10 minutes or more per tool in my experience. I split the work into separate tasks for features, pricing, and complaints instead of asking for everything in one response. The narrower assignments are easier to scope, though I have not run a controlled comparison to show how much this improves speed or accuracy.
4. Make long runs resumable
I added a queue that records tasks as completed or errored, then resumes with pending work. A limit option lets me process a bounded number of tasks at a time. This makes failures less disruptive: I can revisit unfinished items without treating a long research run as all-or-nothing.
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5. Store the gathered records for analysis
My database contains approximately 27,000 feature records, 7,500 complaint records, and 2,000 pricing entries. These are approximate, self-reported project totals. They do not tell me whether every record is current, correctly attributed, complete, or representative of an industry.
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My current feature prompt asks an agent to search a vendor’s site or other sources, list key features, and return only a JSON array. In practice, the task remains search-heavy. Runs can time out, and some sites are difficult to access or do not clearly disclose product details. Vendors also organize their pages differently and use different language for similar capabilities, so there is no universal feature page or vocabulary I can rely on.
A more bounded prompt is a next experiment, not a workflow I have already validated. For each product, I could ask the agent to:
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- Confirm the product and vendor before gathering details, to reduce confusion between similarly named tools.
- 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 and stop once those pages have been reviewed.
- Return each feature with a short evidence excerpt, page title, URL, and access date.
- Separate explicitly documented capabilities from inferences rather than turning marketing language into an unqualified claim.
- Return “not found” or an empty result when evidence is unavailable, and report access failures separately instead of guessing.
- Keep a concise canonical feature label while storing the vendor’s own wording in a separate field for later classification.
This design would make it easier to review where a record came from and distinguish what a vendor states from what an agent infers. It would not eliminate missing information: product sites vary, and limiting the number of pages could leave relevant details undiscovered.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Classification is not just a labeling problem
One of the harder analysis questions is how to recognize the same capability when vendors describe it differently. I have tried assigning categories and am considering repeated classification passes, deduplicating category labels, and deciding whether a category should apply across industries or only within one.
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Turning records into useful comparisons
I have generated feature-frequency charts and heatmaps, but very large charts become noisy. Counting mentions alone can hide how unevenly tools were researched, whether a feature label was classified consistently, and whether a product’s public documentation is unusually detailed.
More useful comparisons may come from narrowing the view to an industry and pairing comparable dimensions, such as feature category, complaint category, and pricing information. Any chart should make coverage visible: how many tools were checked, how many had usable data for each dimension, and what kinds of sources informed the records. Relationships between features, complaints, prices, and change over time are questions I would like to explore, not findings the current data has established.
The sample report currently lists available tools and counts of features and complaints. A fuller industry report would also need credible market-size and business-count information. Complaint records need source review and context before they can fairly be presented; a collected user complaint is not, on its own, a verified product defect.
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What this project can—and cannot—tell me yet
At this stage, the pipeline helps organize public information and exposes places where research is incomplete. It does not automatically recommend a product, validate that buyers will pay, or predict whether a product will succeed. I hope eventually to generate 10–20 possible concepts and perhaps add SWOT-like analysis, but that is a future goal rather than a demonstrated result.
I do not have a controlled speed or accuracy benchmark, an independently validated market-size estimate, or evidence that a recommendation from this system has been built and adopted. The most useful outcome so far is a more structured way to gather and revisit evidence, with the remaining judgment—and verification—still in the loop.
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