Laidback as a static database alternative
People data APIs and static databases give you rows. That is useful if you have a team ready to query, score, write, chase and schedule around those rows. Most teams buy the data and then discover the work is still theirs.
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Every team that buys a people data API expects the same thing: better candidates faster. Then they realise the API does not source, judge, write, chase or schedule.
A coworker that turns data into hires
A sentence describing the person, expanded into thousands of searches
Deterministic score with evidence per criterion
The difference in one table.
Data without judgement is just a bigger spreadsheet
I read between the lines
A shipped repository, a paper or a product update implies skills that no profile field captures. I infer and cross reference them.
I score, not sort
Every candidate is ranked against your real bar with reasoning attached, not filtered by a field you guessed at.
I do the loop
Sourcing, outreach, scheduling and pipeline hygiene are one conversation in Slack, not a handoff between tools.
Where static data still wins
If you have the team and tooling to build your own scoring and workflows on top of clean structured data, a raw API can be the cheaper building block.
What it looks like day to day
We already pay for a people data API. What do you add?
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Data is the input. The question is who turns it into a hire. I query my own index, score the results, draft outreach, chase replies and book interviews.
You still get the names. You just do not get the spreadsheet.
From rows to outcomesI include the data and the judgement, and I deliver it as a conversation in the channel your team already reads.
On the independent people search benchmark I score 97.80 out of 100. The method and every scored query are published on the benchmark page.