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Talent Systems

Talent systems built and shipped

These three projects give a useful view of my work on finding talent, developing interviewer judgment, and keeping searches moving. I am happy to walk through the problems, the choices behind the tools, and what I built. You will find more projects grouped below.

Talent Systems

Keep searches moving

TA CentCom brings the wider search into view: constraints, ownership, and the next decision. The sourcing and interview tools below go deeper into two parts of that work.

Hiring Command Center (TA CentCom)

Critical searches lose momentum when teams can see status but cannot see the constraint, the owner, or the decision required.

The command center for the TA engine: where time is being lost, who owns the next move, and what needs action.

Helpful for

TA leaders, People leaders, and executives accountable for critical hiring and the health of the TA engine.

What it enables

Replaces status-driven hiring reviews with a management view of TA engine health, bottlenecks, ownership, and action.

Talent Operations Hiring Intelligence Executive Leadership

Find relevant talent and develop interviewer judgment

Interview Evaluator

Interviewers rarely receive specific feedback on how their questions, evidence, and judgment affect a hiring decision.

Interviewer coaching and framework work connecting role standards, interview evidence, and practical feedback.

Helpful for

Interviewers, hiring managers, and Talent teams building stronger interviewing capability.

What it enables

Turns completed interviews into practical feedback that interviewers can use in the next conversation.

Claude Code Interviewer Training Structured Hiring Bias Safeguards

Sourcing Compass

Teams entering unfamiliar talent markets often begin searching before they have mapped where relevant capability may sit.

An AI talent mapper that turns a job title into a sourcing landscape to examine and refine.

Helpful for

Recruiters and hiring leaders entering specialist or unfamiliar talent markets.

What it enables

Moves sourcing strategy upstream, from a Boolean search to a deliberate map of the market.

React Groq Llama 3.3 Talent Intelligence Open Source

Also built for talent

  • RishiOSRole- and level-specific evaluation, scorecard validation, and checks against recorded hiring evidence.
  • AOP & Hiring SystemWorkforce planning across headcount, cost, attrition, scenarios, hiring progress, and two-way ATS sync.
  • Resume ScreenerRubric-based, evidence-backed review of resume batches with targeted screening questions.
  • Interview AssistTranscript analysis for structured interviewer feedback, candidate evaluation, and decision-ready reports.
  • Candidate Intel PlatformCandidate research across resumes, GitHub, publications, and public signals for leveling and compensation context.
  • Offer WizardPersonalised offer pages grounded in the candidate conversations that came before the offer.

Broader builder work

Prompt Control Plane, BlindBench, an AI Chief of Staff, and a video-generation pipeline.

Start a conversation

The work is where the conversation begins.

Building something for Talent, or thinking through where AI belongs in the workflow? I enjoy comparing notes on the problem, the design choices, and what we are learning.

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