A recruiter-owned talent network, discovered one workflow problem at a time.
Ryger didn't start as a recruiting product. It started as a question I kept running into during my own job search — and every answer changed what it should be. What follows is the sequence of discoveries that turned a matching tool into a network.
The short version
Each discovery moved Ryger further from “build a better match” and closer to “build the asset recruiters actually keep.” The matching engine was never the point. The network was.
Seven discoveries, one direction.
- 01
Initial Observation
What I saw
I was getting recruiter outreach for roles nowhere near my experience — and at the same time watching qualified people struggle to get seen despite searching hard. Mass layoffs, hundreds of applications each, signal getting buried. It wasn't just bad outreach. Both sides were working hard and still missing each other.
What it led to
That mismatch is what pulled me in. If effort this high on both sides still ended in misses, the problem probably wasn't effort. It was something in how recruiting itself works.
- 02
Research
What I saw
Digging into ATS platforms, sourcing workflows, and recruiter behavior, one pattern kept surfacing: recruiters repeatedly rebuild value they already created. Candidates, relationships, and recruiting effort get trapped across ATS systems, LinkedIn, spreadsheets, inboxes, and personal files.
What it led to
Recruiting effort almost never compounds. Each role tends to start over, and the work from the last search rarely carries into the next. The bottleneck wasn't technology — it was value leaking out of the system between roles.
- 03
Workflow Discovery
What I saw
Evaluating candidates myself, I was constantly moving back and forth between the job description and the resume, holding both in my head. The workflow just felt inefficient. The friction wasn't the decision — it was all the context-switching around it.
What it led to
That realization came before any feature. Once the problem was friction, the design followed: structured review that keeps the requirements and the evidence in one place, so the recruiter isn't reassembling context on every candidate.
- 04
AI Trust Discovery
What I saw
As I evaluated candidates, I kept moving between the job description and the resume to validate why someone was considered a fit. I also kept hitting the limits of keywords: a resume might list Agile, Scrum, or product management, but the words being present never proved the experience behind them. The real question was always “what evidence supports this?” — and no recruiter should have to reconstruct that by hand, candidate after candidate.
What it led to
So evidence had to travel with the recommendation. Matching became requirement-level reasoning — the support for a fit attached directly to each requirement, judged on demonstrated experience rather than keyword presence — so recruiters can make fast, confident decisions without constant re-checking.
- 05
Recruiting Nuance Discovery
What I saw
Most recruiting products seem to assume fit can be fully determined through matching, search, or a score. But the hard hiring decisions rarely work that way — they turn on trajectory, adjacent experience, transferable skills, hiring-manager context, and recruiter judgment.
What it led to
So the goal was never to automate judgment. It was to clear the obvious matches and obvious misses automatically, and focus a recruiter's expertise on the nuanced calls in between — where it actually adds value.
- 06
Marketplace Discovery
What I saw
Almost all the recruiting technology I looked at was built to help recruiters search ever-larger pools of passive candidates. Meanwhile I was watching the opposite problem play out in real time: large numbers of active job seekers, searching hard, and still invisible.
What it led to
It left me with a question, not a conclusion: are we solving the wrong problem? If qualified people who are actively searching still can't get seen, maybe the gap isn't sourcing at all. Maybe it's visibility and signal.
- 07
Network Discovery
The turning pointWhat I saw
Research consistently showed the same thing: recruiters rebuilding searches, rediscovering candidates they'd already found, and losing relationships scattered across ATS systems, LinkedIn, spreadsheets, inboxes, and hard drives. Matching was useful — but it was never the durable value. The durable value was the network itself.
What it led to
This was the moment the product thesis changed. Ryger stopped being a better way to match and became a way to own and grow a talent network — recruiter landing pages, owned candidate ecosystems, reusable talent pools, and continuous accumulation — with matching repositioned as activation rather than the product. The pattern underneath every discovery was the same: technology was rarely the bottleneck. Workflow, trust, signal, and compounding value were.
“The matching engine wasn't the asset. The network was.”
That reframe moved Ryger's center of gravity from a single transaction to a compounding asset. Instead of helping a recruiter fill one role, it helps them build a talent network that gets more valuable with every search.
A recruiter-owned talent network.
Instead of helping a recruiter fill one role, Ryger helps them build an asset that compounds with every search.
Growth — how the network compounds
A recruiter-owned network is only as valuable as its ability to grow. If recruiters have to manually source every candidate forever, the asset stays small. So recruiter landing pages and public candidate intake became core — a way to continuously grow the talent pool while the recruiter keeps ownership of every relationship. Not a marketing feature; a direct consequence of a network thesis that needed a mechanism to compound over time.
Integrity — growth without losing signal
Public intake raised an obvious risk. Recruiting is increasingly flooded with automated applications, mass-apply workflows, and bot-assisted submissions — and a recruiter-owned network only works if recruiters trust the quality of the pool. That led to deliberate controls around candidate ingestion, designed to discourage automated submissions and preserve signal. Growth matters; trust and integrity matter just as much.
A real system, built deliberately.
Every architecture decision served the product strategy — the network thesis, evidence-backed matching, and signal integrity. The stack supports the story; it isn't the story.
- Frontend
- Next.jsTypeScriptTailwindVercel
- Platform
- AWS LambdaAPI GatewayDynamoDBS3ECSCloudFrontSES
- AI & Intelligence
- OpenAIRequirement-level evaluationEvidence-backed reasoningMatch reasoning workflows
- Identity & Billing
- ClerkStripe
- Integrations
- Google Maps APIGeocodingLocation proximity
- Key Systems
- Recruiter-owned talent networksCandidate ingestion pipelinesRecruiter landing pagesResume intelligenceMatch reasoning engineSlate management workflowsSignal integrity controls
The biggest lesson from Ryger is that technology alone rarely solves workflow problems. The most valuable opportunities show up somewhere quieter — in how people actually work, where trust breaks down, where information gets lost, and where systems fail to compound value over time. Find those, and the product almost designs itself.