Technology & Innovation

AI-Native
Recruiting.

I've been building AI-powered recruiting tools since 2022 — before it became a talking point in the TA industry. The distinction I draw is important: I don't use AI as a shortcut. I use it to automate the parts of the job that don't require a human, so I can spend more time where it actually matters.

What does that mean in practice? It means hiring managers can draft JDs and generate Alignment Guides without me in the room. It means I have real-time visibility into every search without building manual tracking spreadsheets. It means candidates sit in offer conversations where I can visualize their total wealth creation scenario live, not two days later after someone ran numbers in a spreadsheet.

The Philosophy Behind the Tools.

Executive recruiting is fundamentally a relationship business. The only thing that can close a VP Engineering candidate against a Google competing offer is a recruiter who has spent months building genuine trust, understands what the candidate actually cares about, and can construct a narrative about why this is the right moment to make the move.

AI can't do that. But AI can absolutely handle the process overhead that used to eat hours of my week — the first-draft JDs, the intake document population, the pipeline status rollups, the compensation scenario modeling. Every hour I recover from those tasks is an hour I can spend on the relationship work that actually drives 93% offer acceptance.

That's the principle I've applied since 2022: use AI to protect time for the work that requires a human.

01
Automate Process, Not Judgment
AI handles first drafts, formatting, tracking, and modeling. Candidate assessment, stakeholder calibration, and offer strategy stay human.
02
Tools Should Solve Real Problems
Every tool I've built started as a genuine friction point. The Co-Pilot exists because intake alignment was slow. The Tracker exists because pipeline visibility was manual. The Wealth Model exists because offer conversations needed real-time data.
03
Stay at the Frontier
Started with GPT-3 in 2022. Rebuilt in Claude as the model improved. Always evaluating what's next — Gemini, Glean, JuiceBox AI, Covey. The specific tool matters less than the habit of staying current.
Built by Kyle

Tools I've Built.

Built · Slack + Claude
Hiring Manager Co-Pilot
Started in 2022 as a custom GPT that could answer hiring manager questions about the executive recruiting process. Over time, it evolved into a full Slack-integrated workflow powered by Claude — a single destination where hiring managers can learn the process, draft job descriptions with appropriate scope and leveling language, and generate fully structured Alignment Guides without requiring my involvement at the intake stage.
Impact: Reduced time-to-alignment on new executive searches. Hiring managers arrive at intake meetings with a working draft, not a blank page. The first alignment conversation is about calibration and refinement, not starting from zero.
Built · Claude
Executive Search Tracker
A real-time pipeline visibility tool built in Claude that centralizes search status, inbound vs. outbound sourcing mix, stage-level analytics, and time-in-stage data across all concurrent executive searches. The output powers the CEO-facing search dashboards I present at regular leadership reviews — giving the C-suite visibility into search health without requiring them to dig into Greenhouse.
Impact: Eliminated the manual pipeline reporting that used to require hours of weekly Greenhouse data export and formatting. Status is always current. CEO conversations about search health happen faster and with better data.
Built · Claude
Candidate Wealth Creation Model
A compensation visualization tool that models total wealth creation scenarios for executive candidates — comparing the candidate's current unvested equity schedule against proposed RSU packages, sign-on structures, and equity acceleration terms over a 3-5 year horizon. Built to be used live in offer conversations, not as a post-hoc document.
The problem this solves: executives evaluating a new role are making a complex financial decision under time pressure. They're comparing unvested equity at their current company, tax implications of acceleration or cliff events, and the upside potential of the new package. Most offer conversations happen without anyone having done this math clearly.
By building this model, I can run those scenarios live — showing a candidate exactly what the Klaviyo package means for their total wealth over the next 3-5 years, relative to staying. That transparency, used at the right moment in a negotiation, is a meaningful closing tool.
Full Stack

The Complete Toolkit.

ATS / HRIS
Greenhouse
Workday
iCIMS
JazzHR
Sourcing & Intelligence
LinkedIn Recruiter
JuiceBox
TalentWall
Crosschq
Covey
Interview & Assessment
BrightHire
Structured Interview Design
Scorecard Architecture
AI — LLMs
Claude (Anthropic)
ChatGPT (OpenAI)
Gemini (Google)
AI — Workflow & Apps
Glean
Lovable
LinkedIn AI Sourcing
JuiceBox AI
Reporting & Analytics
Custom CEO-facing dashboards
Board talent deck reporting
Sourcing mix trackers

AI Adoption Timeline.

2022
First AI recruiting tools — GPT-3 era
Built initial JD generation and Alignment Guide drafting workflows using early GPT models. First version of the Hiring Manager Co-Pilot launched as a custom GPT.
2023
Executive Search Tracker + Wealth Creation Model
Built the first version of the Executive Search Tracker to power CEO-facing pipeline dashboards. Developed the Candidate Wealth Creation Model as an offer conversation tool in the lead-up to Klaviyo's IPO.
2024
Slack integration + Claude rebuild
Rebuilt the Hiring Manager Co-Pilot in Claude and integrated it into Slack — creating a persistent, searchable knowledge base that hiring managers can access in their workflow. Expanded the tool stack to include Glean, Lovable, and Covey.
2025–
Continuous iteration
Ongoing refinement of all three core tools. Evaluating new AI sourcing capabilities in JuiceBox and LinkedIn. Focused on the next frontier: using AI to accelerate alignment speed and candidate pipeline quality, not just process efficiency.