documentation.
master the art of lead generation with our advanced search architecture.
the three-box architecture
0dayleads uses a "net, sieve, and brain" pipeline. instead of one unreliable search query, your search is split into three stages: the target topic, the advanced filter, and the ai intent filter.
target topic — the net
this is the broad net we throw into the ocean. keep it simple and broad. it is hard-capped at 100 characters to prevent upstream api issues. you can scope it to a specific subreddit by prefixing with r/SubredditName.
- good: saas
- good: nextjs developer
- good: r/AskReddit what (subreddit-scoped)
- bad: looking for a saas developer who knows nextjs and react
advanced filter — the sieve
once we pull raw results using your target topic, we run them through your advanced filter locally. this lets you write complex boolean logic queries without api restrictions. posts that don't match are discarded before the ai even sees them.
requires one of looking/hiring/need AND one of developer/engineer, while excluding intern posts.
finds saas or software posts, excluding freelancers, agencies, and job seekers.
ai intent filter — the brain
the final stage. even after boolean filtering, you may get false positives. the ai analyzes remaining posts to determine if the author actually intends to buy or use your product. it classifies each post as high, medium, or low intent.
- posts that pass your advanced filter are batched (up to 50 at a time)
- your custom ai instructions are sent alongside the posts
- the ai scores each post as high, medium, or low
- only posts matching your delivery filter settings are published as leads
how ai classifies leads
every post is scored into one of three intent levels. configure which levels you receive in delivery filters. new accounts default to high only.
the author has a specific, current pain point that your product directly solves, is in a decision-making role (founder, vp, team lead), and shows buying signals — urgency, frustration, budget mentions, comparing tools, or dissatisfaction with their current solution.
the author describes a relevant pain point but isn't actively seeking solutions. they may be in early research, evaluating build-vs-buy, or their role is unclear but the pain matches your product.
the author is sharing knowledge, building their own solution, is a student or researcher, is job hunting, selling a competing product, or the post topic matches but their actual intent is informational or self-promotional.
writing good ai instructions
your ai instructions tell the model what you sell. be specific — vague instructions produce vague results. the ai needs to know your product to judge if someone is a buyer.