Why companies lose money on hiring — and how we fix it
Most companies hire blind: a polished resume, a "good overall impression", a referral from a friend. Three to six months later it turns out the person understands neither the niche nor the product economics. The budget is spent, the time is lost, and the search starts over.
It hits hardest in roles where the cost of a mistake isn't measured in salary, but in burned budgets and months without growth: marketers, product managers, GTM engineers, sales specialists, project managers. And now it's happening with engineering roles too — AI changed the requirements faster than the market could adapt.
Team
The team includes team leads, account managers, and data engineers. We continuously train the algorithm on the expertise of working marketers, product managers, and GTM specialists: every expert interview and every closed match makes the scoring more accurate.
What a perfect match actually is
A perfect match is not "a strong specialist in general". It's a measurable overlap of contexts: how closely the environment where a person already delivered results matches yours. We formalized that overlap into three conditions:
Industry and business-model overlap
A specialist from a similar niche with a similar monetization model already knows the channels, the audience, and the pitfalls — no training from scratch required.
A track record inside a growing company
Extra weight goes to candidates from companies that performed for several years in a row: revenue, traffic, and hiring growth. A growing company is the most honest filter there is.
Verified, measurable results
Not "worked on performance marketing", but concrete numbers: CAC, ROMI, retention, MRR growth.
To compute that overlap, every specialist and every company in the database is described with the same set of 9 parameters — the experience graph:
- 1Niche and industry
- 2Business model
- 3Product
- 4Price point
- 5Audience and JTBD
- 6Geo
- 7Channels and tools
- 8AI stack
- 9Measurable results
The algorithm ranks candidates by the combination of overlaps, not by any single parameter. For B2B SaaS with a long sales cycle the weights are one set; for mass-market B2C — another.
How the process works
Company Brief
Marketing and product are roles where specific background matters: niche, business model, product, check, tools.
We also learn goals for 90 days and a year, team size, business stage. Most importantly — a list of direct and indirect competitors.
We structure this data into a feature set:
Brief. A 30-minute call: we describe your company with the same 9 parameters — niche, model, price point, audience, goals for the next 3–6 months.
Sourcing. The algorithm matches candidates from the database and external sources. We don't look for "generally strong" — we look for people who already solved your exact task.
Validation. Three layers of vetting: an automated interview, an expert interview, and a portfolio review. Plus a test assignment, references, and work artifacts. The resume is often the weakest signal.
Shortlist. A ranked list with commentary: what price point, which audience, which channels — where the experience transfers to your task and where it doesn't.
Offer. We help with the test assignment, negotiations, and the offer — backed by market salary benchmarks.
Where we find the people who aren't on job boards
Job boards show you who's already looking. The best specialists rarely show up there — so job boards are just one source among many:
Job boards and networks
HeadHunter, LinkedIn: the base layer, roughly 20% of our finds.
Professional communities
Telegram chats and niche communities where practitioners share what actually worked.
Hackathons and competitions
People with a proven ability to ship to the finish line.
Conferences
Speakers with hands-on cases, not theory.
Tracking industry leaders
We deliberately follow strong operators at top-performing companies and stay in personal contact with them. When someone like that enters the market, we're the first to know.
References
Both sides of every reference; a bad review is a data point, not a verdict.