01Relationship first
Reactivate Marcus Ilori before applying to Northlake
A dormant second-degree path reaches the hiring organization directly. Applying cold first spends the opportunity at its weakest dimension.
- 12 minutes
- highest leverage
AI Opportunity Intelligence
An AI decision system for navigating the enterprise AI market.
Built to continuously discover, evaluate and prioritize companies, roles, signals and relationships — then determine what deserves attention next.
Illustrative demonstration data.
The system in two minutes
See how intelligence becomes action.
A short walkthrough of the system I built to identify opportunities, evaluate what matters, map the people who can create access, and decide what deserves attention next.
View transcript
- 0:00Title: The Opportunity Engine — Intelligence → Decision → Action.
- 0:14The AI market moves faster than anyone can follow.
- 0:19So we let something else watch it.
- 0:23This morning, something changed.
- 0:27A company raised money. Weeks earlier, it hired a new revenue leader.
- 0:33Two ordinary facts. One pattern.
- 0:35Hours later, a new role appears.
- 0:39But finding the role isn't the hard part.
- 0:42The harder question is… is it worth pursuing?
- 0:46So the engine looks at fit. Timing. Momentum. Wealth.
- 0:50Ninety-two.
- 0:53Which looks like an easy yes.
- 0:56And then… access.
- 0:59Sixty-four.
- 1:03Ah. There it is.
- 1:06The quiet thing between opportunity and conversation.
- 1:17So it maps the people.
- 1:21Not the most important person — the reachable one.
- 1:25One message from the decision.
- 1:27And the day rearranges itself.
- 1:31Twelve minutes now, instead of an application nobody asked for.
- 1:36So it doesn't say apply.
- 1:39It says… improve access first.
- 1:44Signal detected. Opportunity evaluated. Relationships mapped. Action prioritized.
- 1:48That's AI Opportunity Intelligence.
Built as a working system, not a concept.
Interested in the system, the thinking behind it, or what we could build together?
rhinehart.brooks@gmail.com
Interactive demonstration
Signal → Discovery → Score → War Room → Relationship Intelligence → Daily HQ → Action
A 60–90 second walk-through of how new information changes the allocation of opportunity, attention and relationship capital.
Step 1 of 7 — Signal
Step 1 — Signal
A material market signal is detected.
Material signal
Northlake Systems announces $140M growth round
Paired with a senior enterprise revenue hire 19 days ago, this indicates an enterprise GTM organization being stood up now — not next year.
What changed
$140M growth round announced (illustrative)
Supporting change
SVP of Enterprise Revenue hired 19 days ago
Implication
Enterprise GTM expansion may be opening
- Detected 6 hours ago
- Northlake Systems
- Applied AI infrastructure
Signal confidence87
Two independent, corroborating public events.
Corroborating change
New SVP of Enterprise Revenue joins from a public infrastructure vendor
Leadership pattern suggests a repeatable enterprise motion is being imported.
- Detected 19 days ago
What the system decided
Flagged Northlake Systems as materially changed.
Why
Capital plus a new enterprise revenue leader is the pattern that precedes an enterprise seller build-out.
Key takeaway
Capital + new enterprise leadership suggests a hiring window may be opening.
Illustrative demonstration data.
Command Center — Daily HQ
Something changed. The system interpreted it. Today's priorities changed.
Rendered live from a static, fully fictional demonstration dataset.
92/ 100
Today
Portfolio Opportunity Health
One material change reshaped today's allocation of attention.
- Northlake Systems
- Applied AI infrastructure
- Austin, TX
Fit95
Timing94
Wealth90
Location88
Access64
Weakest dimension — determines today's move
Action91
New intelligence
Northlake Systems announces $140M growth round
Paired with a senior enterprise revenue hire 19 days ago, this indicates an enterprise GTM organization being stood up now — not next year.
- Detected 6 hours ago
- Material signal
Score movement
Northlake Systems — Overall Opportunity
8692+6
Funding event plus enterprise revenue leadership raised Timing and Wealth. Access stayed low, so the recommended move shifted from applying to activating a relationship.
- Updated this morning
New role discovered
Enterprise Account Executive
Northlake Systems · Net-new enterprise logos, platform-led deals
Compensation
$180K base / $360K OTE (illustrative)
Work model
Austin, TX · Hybrid — 3 days onsite
- Discovered 6 hours ago
- Enterprise focus
Recommended actionHighest leverage
Reactivate Marcus Ilori before applying to Northlake
A dormant second-degree path reaches the hiring organization directly. Applying cold first spends the opportunity at its weakest dimension.
Access path
Marcus Ilori · Director, Enterprise Sales
Warm-but-dormant path. Previously worked alongside a shared former colleague; a single reactivation message reaches the hiring org directly.
Effort
12 minutes
Relationship before application · Last exchange 14 months ago
Today
Today's top 3
02Positioning
Draft the Northlake enterprise-expansion thesis
A one-page point of view on their enterprise motion converts the referral conversation into a candidate-of-record moment.
- 25 minutes
- high leverage
03Sequencing
Hold the direct application for 48 hours
Sequencing matters more than speed here. A referred application enters review with materially different weight.
- No work required
- high leverage
Opportunity thesis
Capital plus new senior revenue leadership almost always precedes an enterprise seller build-out. Entering before the org is fully staffed maximizes both leverage and equity timing.
- Enterprise GTM expansion following a major funding event
- 410 → 640 projected
- Series C (placeholder name)
No other portfolio changes require attention today.
Quiet dimensions stay gray on purpose. Color is reserved for decisions.
Illustrative demonstration data.
The system
One intelligence loop. Multiple decision systems.
Market information enters once. It is discovered, interpreted, scored, connected and resolved into a single prioritized action — then the outcome feeds back in.
Market + Company Intelligence
Public market activity, company trajectory, leadership movement and product momentum enter as raw, unstructured information.
Radars
Specialized discovery workflows run continuously rather than on demand.
- Company Discovery
- Job Discovery
- Executive Discovery
- Signal Intelligence
Trigger Intelligence
Determine what changed — and whether it matters enough to affect a decision.
Decision Engines
Separate models evaluate the same opportunity from different angles, so tradeoffs stay visible.
- Company Scoring
- Role Scoring
- Timing
- Wealth
- Momentum
- Access
- Career Fit
Opportunity Graph
Entities are connected, not filed. A single change propagates to everything related to it.
- Companies
- Roles
- Executives
- Relationships
- Signals
- Actions
Daily HQ
The graph is resolved into three executive questions every morning.
- What changed?
- Why does it matter?
- What should I do?
Action + Learning Loop
Decisions, applications, outreach, follow-ups and score history feed the operating system forward.
Output feeds the next morning's intelligence
What makes it different
Four principles the system is built on.
Each one changes how information is treated after it arrives.
Discovery is not a decision.
Finding a company or a role only starts evaluation. Nothing earns attention until it has been scored against the alternatives.
Scores explain tradeoffs.
Fit, Timing, Wealth, Momentum and Access frequently point in different directions. The score's job is to expose that conflict, not hide it.
Relationships are part of the decision model.
A strong opportunity with weak access requires relationship activation before application. Access is modeled as a dimension, not an afterthought.
Intelligence must change behavior.
A signal only counts when it changes priority, timing or the next action. Anything else is a notification.
Decision models
Eight dimensions instead of one opaque AI score.
Every opportunity is evaluated by separate models, then composed into a single index that can always be decomposed.
Opportunity Score
Overall attractiveness of the opportunity.
Career Score
Quality of the career opportunity itself.
Wealth Score
Compensation and equity asymmetry.
Stability Score
Probability of durable company success.
Momentum Score
Current trajectory and rate of change.
Networking / Access Score
Ability to create credible access.
Personal Fit Score
Match between the opportunity and my experience.
Timing Score
Whether now is the right moment to move.
Composite
Overall Opportunity Index
The point is not mathematical theater. Separate dimensions expose why an opportunity is attractive and what strategy it requires — a 92 driven by Timing demands a different move than a 92 driven by Fit.
Why I built it
It was an intelligence problem, not a job-search problem.
I built AI Opportunity Intelligence because navigating the AI market had become an intelligence problem, not a job-search problem.
Companies were emerging quickly. Funding, leadership changes, product momentum, new roles and relationships were constantly changing the opportunity landscape.
Traditional job boards answer one question: what jobs are open? I needed a system that answered a harder one — where should I place my time, attention and relationship capital, and why?
What began as a system for navigating my own move deeper into enterprise AI became an operating system that continuously discovers opportunities, evaluates them, maps access, monitors change and converts intelligence into action.
Job boards answer
What jobs are open?
The question I needed answered
Where should I place my time, attention and relationship capital — and why?
What building it demonstrates
Each capability is visible in the system itself.
The product is the evidence — every item below maps to something shown earlier on this page.
AI-native product thinking
Turning an ambiguous operating problem into a persistent intelligence system rather than a one-off tool.
Seen in: the intelligence loop, not a search box.
Agentic workflow design
Specialized discovery, trigger, scoring and follow-up workflows that operate as one system with a shared graph.
Seen in: Radars → Trigger Intelligence → Decision Engines.
Structured intelligence
Converting unstructured market information into entities, signals, relationships, scores and decisions.
Seen in: the Opportunity Graph.
Human-in-the-loop AI
AI discovers, synthesizes and recommends. Consequential career and relationship decisions stay human.
Seen in: recommended moves that require approval, not automation.
Decision-model design
Separating Opportunity, Career, Wealth, Stability, Momentum, Access, Fit and Timing instead of collapsing judgment into one unexplained score.
Seen in: Access as the limiting dimension in step 3.
Enterprise GTM judgment
Treating companies and career opportunities like strategic enterprise accounts: research, qualification, stakeholder mapping, timing and penetration strategy.
Seen in: the War Room and the Access Map.
Product + UX judgment
Designing Daily HQ around three executive questions instead of a dashboard of metrics.
Seen in: what changed, why it matters, what to do.
Human + AI operating model
A deliberate division of labor.
AI
- Discovers
- Monitors
- Structures
- Scores
- Synthesizes
- Detects changes
- Recommends
Human
- Sets objectives
- Defines judgment criteria
- Challenges assumptions
- Approves decisions
- Builds relationships
- Takes consequential action
The goal isn't to automate judgment. It's to give judgment better intelligence.
Evolution
Nine layers, each caused by the last.
Each layer exists because the previous workflow exposed a new decision problem — not because a feature list was planned up front.
- 01
Opportunity research
Manual research produced notes, not comparisons.
- 02
Company scoring
Comparisons needed a consistent basis across very different companies.
- 03
Job Radar
Company interest was useless without knowing when roles actually opened.
- 04
War Rooms
Roles and signals were fragmented; each opportunity needed one synthesized thesis.
- 05
Executive CRM
A thesis without access is unusable. Stakeholders had to become first-class entities.
- 06
Trigger Intelligence
Static evaluations aged badly. The system had to notice what changed.
- 07
Follow-Up + Score History
Change only mattered if I could see movement and close the loop on commitments.
- 08
Daily HQ
Too much intelligence, too little direction. Attention needed a daily allocation.
- 09
AI Opportunity Intelligence
The layers were one system: intelligence in, prioritized action out.
Built to navigate opportunity. Designed to demonstrate how I think.
I'm interested in the companies building the next generation of enterprise AI — particularly where complex technology, enterprise customers and evolving GTM models intersect.
The product experience shown here uses fictionalized demonstration data to protect private operating information. No production systems, records or private information are connected to this page.