HeyMilo AI

We just launched HeyMilo Red. heymilo.ai/red A standalone cheat detection layer that analyzes recordings for AI-assisted answers, coaching, second screens, proxy candidates and synthetic media, then returns timestamped evidence for human review. API-first and built to work with existing interview workflows.

One of our sr engineers is always the first person to drop a new model in Slack. He’s also often the first to defend older models for the sake of product quality. That’s the reality of applied AI. Model quality and product quality are not the same thing. A newer or stronger model can still make the experience worse if it’s slower, less predictable, harder to steer, or worse at recovering from messy inputs. The model is only one part of the system.

One of the harder parts of building an AI interviewer is deciding how much freedom it should have to follow up. Too little and the interview becomes a form. Too much and two candidates may no longer be getting comparable interviews. A lot of the product work is finding that boundary.

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We don't use an AI avatar interviewer, the data told us not to. Candidates felt more confident and performed better without one. It creates a better interview experience for them, and better signal for the hiring team. Sometimes the more "human-looking" AI experience is actually the less human one.

There are at least two versions of "the model got the answer right." It produced the expected output in a test, or it behaved correctly inside the actual workflow. We care much more about the second.

We've found that production voice systems expose the long tail very quickly. Background noise, accents, interruptions or latency aren't separate problems. The difficult cases are combinations of them happening at the same time.

We spend a surprising amount of time thinking about "reasonable human behavior." A candidate pausing for 15 seconds might be thinking, reading notes, having connection problems or getting help. Systems that interact with humans have to deal with ambiguity rather than pretend it doesn't exist.

Interview integrity should produce hard evidence, not just vibes. "Risk score: 82" is vague and hard to act on. "03:42-04:18: off-screen reading, cadence shift, corroborating transcript pattern" gives a reviewer something they can inspect and act on. That's why we built Red

Some exciting news! We've raised $6 million in funding, led by @Category_VC with participation from @canaanpartners, @alumniventures, and @remarkable_vc Also launching today: three new agent types. 1. Candidate Recommendations resurfaces talent already sitting in your ATS. 2. AI Scenario Assessment measures how candidates work with AI, built for data annotation, technical, and AI-adjacent roles. 3. Notetaker captures structured notes from live recruiter-led interviews so every candidate interaction produces consistent, reviewable signal. We've screened 1M+ candidates on the platform, 4x revenue in six months, and have brilliant customers like Randstad, Wilson, and Neo Financial in production today. Now back to building! Read the full announcement: heymilo.ai/blog/heymilo-rais…

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