// HAWKEYE AI VENTURES · CURRENT INDEPENDENT WORK JANUARY 2026–PRESENT

Lead Product Designer & AI Strategist

I know how to decide whether an idea deserves to become software.

I use independent venture work to turn ambiguous problems into evidence-backed product opportunities. I frame the root problem, make value and risk explicit, reimagine the work around people and AI, and build enough of the strongest direction to support the next decision.

See the HuntMate case
Open live HuntMate demo ↗ Open opportunity brief

Synthetic portfolio data. The demo does not apply, send, contact an employer, negotiate, accept, or decline for the user.

// OPERATING MODEL

Move from a signal to a decision, with evidence at every step.

  1. 01

    Discover

    Find the signal, affected people, current work, and uncertainty.

    Is there a real problem?
  2. 02

    Frame

    Separate the root problem from the first proposed solution.

    What is worth pursuing?
  3. 03

    Reimagine

    Redesign the service around human judgment and AI assistance.

    What should change?
  4. 04

    Validate

    Build the smallest useful proof and test the riskiest assumptions.

    What did we learn?
  5. 05

    Decide

    Advance, revise, pause, or stop with the rationale recorded.

    What earns investment?

// CURRENT CASE · HUNTMATE

A connected, human-controlled workspace for the First-Five job-search journey.

Hypothesis

When job-search work spans separate tools and stages, people may spend too much effort recreating context and checking whether AI output is grounded in their real evidence.

Working evidence

A current end-to-end demo now connects simulated access, onboarding, opportunity work, truthful preparation, follow-up, interview, offer support, and lifecycle closure.

// OPPORTUNITY FRAMING

Compare the work people need to complete, then test where continuity could create value.

A category-level scan organized the opportunity around five connected jobs. It is directional framing, not proof of demand, willingness to pay, or market size.

Job-search workQuestion the scan exploredTestable HuntMate opening
Find and rankCan a person see fit, constraints, and next actions together?Prioritized opportunities with visible context.
Clarify and tailorDoes generated material stay tied to approved evidence?Clarification before drafting, then human approval.
Apply and follow upCan commitments remain attached to the opportunity?Internal state plus a two-day outreach task.
Prepare for interviewsCan preparation reuse evidence without inventing claims?Role-specific preparation grounded in known material.
Evaluate an offerCan AI reduce cognitive load without taking the decision?Comparison, questions, and negotiation preparation.

// HUMAN + AI REIMAGINATION

As consequence rises, increase evidence, review, and human control.

AI can prepare

  • Rank opportunities and explain the visible factors.
  • Surface missing evidence and ask focused questions.
  • Draft tailored material for review.
  • Compare an offer and prepare questions or negotiation points.

The person controls

  • What evidence is true and approved.
  • Whether a draft is downloaded, edited, or used.
  • Every application, email, and employer contact.
  • Negotiation, acceptance, decline, and final decisions.
HuntMate customer journey mapping actions, emotions, pain points, opportunities, evidence, and human control across the First-Five journey
Customer journey V2Where uncertainty, effort, and consequence change across the journey.Open full-size artifact ↗
HuntMate service blueprint showing user actions, visible product behavior, AI assistance, human controls, backstage support, evidence, risks, and measures
Service design blueprint V2How visible product behavior, AI support, safeguards, and evidence work together.Open full-size artifact ↗

// WORKING PRODUCT PROOF · CURRENT DEMO

The product proof covers the full First-Five service arc.

These captures come from the current synthetic demo. They demonstrate working product states, not validated user demand or customer outcomes.

  1. 01Login + simulated MFA
  2. 02Onboarding + readiness
  3. 03Jobs + clarification
  4. 04Applied + follow-up
  5. 05Interview
  6. 06Offer support
  7. 07Hired state
01 · Access and trust

Establish the control boundary before AI begins recommending.

Login and simulated MFA make the demo boundary explicit. This is product-flow evidence, not production authentication proof.

HuntMate simulated login screen with explicit synthetic-demo and human-control language
Login · simulated access
HuntMate simulated multi-factor verification screen
MFA · simulated second factor
02 · Activation and readiness

Start with the minimum foundation, then make deeper enrichment optional.

The flow captures the search goal and résumé metadata, then confirms when the person has enough to start.

HuntMate onboarding screen asking for target role, location, and work model
Search goal · required context
HuntMate readiness screen confirming target, location, and synthetic resume metadata
Readiness · enough to begin
03 · Opportunity and evidence

Keep fit, constraints, questions, and approved evidence attached to the job.

The workspace connects prioritized opportunities to clarification and truthful tailoring. Generated material remains a version to review, not a silent rewrite of the master résumé.

HuntMate jobs workspace with opportunity states, fit, and next actions
Jobs · opportunity work
HuntMate review and clarify workspace that surfaces evidence gaps before tailoring
Clarify · evidence before drafting
04–05 · Momentum and preparation

Carry the same opportunity context into follow-up and interview work.

Internal Applied state, a two-day outreach task, and role-specific preparation continue the service without sending anything to an employer.

HuntMate Lumen Ridge opportunity detail showing the applied date and a two-day outreach follow-up as the next action
Applied · opportunity + follow-up context
HuntMate Atlas Works interview workspace showing complete-loop preparation, generated-round progress, and explicit answer approval
Interview · complete-loop preparation
06–07 · Decision and closure

Prepare the high-consequence decision without taking it.

Offer support separates known terms from open questions and prepares negotiation points. Hired closes the synthetic lifecycle after the person records a choice; it is not a measured hiring outcome.

HuntMate offer workspace comparing synthetic compensation and preparing negotiation points without contacting an employer
Offer · analysis and preparation
HuntMate congratulations screen marking lifecycle closure in a synthetic demo
Hired · synthetic lifecycle state

// INNOVATION OPPORTUNITY & INVESTMENT BRIEF

Make what is known, working, hypothesized, and missing visible before asking for more investment.

HUNTMATE · DECISION BRIEF · 2026-08-25Recommendation: advance to controlled user validation
Opportunity signal
Hypothesis Job-search context may break as people move across discovery, application, follow-up, interview, and offer tools.
Target user
Hypothesis A white-collar job seeker managing several active opportunities and repeated preparation work.
Value hypothesis
Hypothesis Preserving approved evidence and decisions across the journey could reduce context reconstruction and improve decision confidence.
Human + AI model
Working evidence AI ranks, asks, drafts, compares, and prepares. The person approves truth and controls every employer-facing action.
Feasibility
Working evidence The current demo connects the complete First-Five workflow using synthetic data and browser-local state.
Desirability
Unknown No claim of validated demand, repeat use, trust, or behavior change.
Viability
Unknown Willingness to pay, acquisition, support cost, retention, and market size require research.
Primary risks
Overtrust, fabricated career claims, privacy loss, excessive automation, and a workflow that feels heavier than existing habits.
Next investment
Run controlled sessions that test first-use comprehension, correction, repeated utility, context persistence, and value. Use the evidence to advance, revise, pause, or stop.
Working software reduces feasibility uncertainty. It does not prove desirability, viability, or product-market fit.

// STAGE-GATE READINESS

Each stage earns the next one.

StageDecision questionEvidence nowStatus
ProblemIs the problem specific enough to investigate?Root problem and affected workflow framed.Framed
OpportunityCould a connected workflow create meaningful value?Category-level opportunity hypothesis; business value unproven.Hypothesis
ConceptIs the human + AI service coherent and bounded?Journey, blueprint, control model, and risks defined.Defined
PrototypeCan the model work as a product?Current First-Five demo with synthetic states.Working evidence
ValidationDo people understand, trust, return to, and value it?Controlled user evidence has not been collected.Next gate
Beta / investHas the opportunity earned broader investment?Desirability and viability remain unresolved.Not yet earned

// WORKING PRODUCT

Experience the First-Five journey.

Start with simulated access, move through the connected opportunity workflow, and finish with offer decision support. The demo uses synthetic data and keeps consequential actions with the person.

Open live HuntMate demo ↗

Synthetic data only. No application, email, employer contact, negotiation, acceptance, or decline is sent.

// WHAT REALITY MUST ANSWER NEXT

The prototype has earned a controlled test, not a business claim.

  1. 01Comprehension

    Can someone understand the connected model and reach first value without coaching?

  2. 02Trust and correction

    Do evidence visibility and approval points help people catch and correct weak output?

  3. 03Repeated utility

    Does preserved context reduce repeated setup across real opportunities?

  4. 04Viability

    Which users value the continuity enough to pay, and what does responsible support cost?

Current decisionAdvance to controlled user validation.

// Have an ambiguous product opportunity?

Get In Touch