Find People that matter to you

Most people-search tools make you think like a database. You pick a job title from one list, a location from another and a company size from a third, and you get back everyone whose profile happens to contain those words. The engineer who spent years on an exchange’s trading systems, but whose title says “Systems Developer”, never shows up.

Find People works the other way round. You write down who you’re looking for, the way you’d say it to a colleague. Otto turns that sentence into filters, searches in several phrasings, checks each person against what you meant and ranks them. Here’s one real search, from the sentence we typed to the 10 people Otto delivered.

The ask

“Rust engineers in London who’ve built low-latency trading systems.”

That one sentence holds three kinds of requirement, and Otto separates them before it searches:

• Skills: Rust, low-latency systems, trading systems
• Location: London
• A check for the judge: experience building low-latency trading systems

Skills and location become filters. The check is a question Otto asks about every person it finds, once it has read their work history.

Six ways to ask the same question

People describe the same work in different words, so Otto writes several versions of your ask and searches with all of them, where a keyword tool runs only the words you typed. Here are the six it wrote for this one:

1. Rust engineers in London
2. Developers building low-latency trading systems in London
3. London-based Rust programmers specializing in high-frequency trading infrastructure
4. Software engineers in London with experience in low-latency systems and Rust
5. Rust developers in London focused on financial trading technology
6. London-based engineers experienced in building performant trading platforms using Rust

298 people screened

Those six searches brought back 298 people. In the film each person is a square, and a ring runs out across the crowd, lighting each one up as Otto screens them.

215 in London

Then the filters. 83 of the 298 weren’t in London, which left 215. The skills filter doesn’t cut anyone. Plenty of good engineers never list every skill on their profile, so when a profile doesn’t state the skills outright, Otto ranks that person lower instead of dropping them.

Next, the judge read each of the 215 against its check, “experience building low-latency trading systems”, and set 2 aside. That left 213 people Otto was willing to put in front of us.

This search ran at the Researched depth, the one that includes the judge. Otto delivered the top 10 in 38 seconds and kept the next 100 ready in case we wanted more.

The results

We ran the same ask again while writing this post. Here are the 10 Otto delivered that time, in the order it ranked them. We’ve left out names, photos and profile links. Each row shows the person’s current role, their work history and why they fit the ask, including anything their profile doesn’t show.

The 10 Otto delivered

All ten have worked on trading systems or at a trading firm, and five have Rust in their current job title.

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How it works

Every search runs the same five steps:

1. You describe who you want. You can also start from a saved persona or from a list of companies.
2. Otto turns the ask into filters and several phrasings, and searches with all of them.
3. It applies the filters it can check in code: location, current and past employer and when they left, title, seniority, and states like founder, in stealth or new in the role.
4. At the deeper settings, a judge reads each person against the meaning of your ask.
5. It ranks what is left and tells you why each person matched.

Depth decides how hard Otto works. Quick runs the filters. Researched adds the judge. Exhaustive adds a second, deeper pass on your original sentence and keeps digging when results run thin. Everyone you keep goes into a workbook, and the search is saved so you can run it again.

What people use it for

Any “people like this” question works the same way. These are the asks from the film:

• Recruiting: Rust engineers in London who’ve built low-latency trading systems (this post)
• Sales: Heads of RevOps at B2B SaaS in London, new in the role
• Fundraising: Seed investors in London who back developer tools
• Events: Founders in London building AI agents
• Investing: Ex-DeepMind founders in London
• Advisors: Fractional CFOs who’ve worked with UK fintech startups

We ran two of them again for this post, at the Researched depth. Here’s what Otto set from each ask and who came back, again without names.

Sales: “Heads of RevOps at B2B SaaS in London, new in the role”

Otto looked for Head, Director and VP of Revenue Operations titles in London, at a senior level, who started the role in the last 12 months, and gave the judge one check: new in the role. It screened 434 people: 258 had started a new role in the last year, 54 of those were in London and 49 were senior. The judge set 16 aside, and Otto delivered the top 10.

All ten lead Revenue Operations in London and started the role within the last year.

Events: “Founders in London building AI agents”

Otto looked for founders in London whose profile mentions AI agents, and had the judge check that they build them. It screened 347 people: 292 were founders and 242 of those were in London. Otto couldn’t confirm “AI agents” from their profiles, so it ranked them on their titles and companies instead of cutting them. Nine of the ten it delivered name their company:

After you find them

Finding them is step one. From the same workspace you can:

• Monitor them. Signal agents tell you when someone changes job, gets promoted or starts posting about a topic you care about.
• Get in touch. Campaigns run across email, LinkedIn, WhatsApp, Instagram and calls, with voice notes, video messages and branches for who is in your network, who accepted and who opened.
• Ask from your own AI. Otto works inside Claude, ChatGPT and Gemini. The film’s search was run that way, from inside Claude. More on Otto Pilot MCP.

Try Find People

Bring the person you’ve been trying to find, and we’ll run the search with you, live.

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