What does each expert know, and how well does it match what the world needs? This system reads verified work, builds expertise maps, and connects people to opportunities they're actually qualified for — then does it again, automatically, forever.
Built automatically from every verified proof, Virgil classification, peer endorsement, and lens output. Each domain shows not just a score but the evidence behind it — so you can see exactly why we believe this person is qualified.
Grants open. RFPs publish. Collaborators seek partners. Most experts miss these — buried in agency newsletters or TTO bulletins they don't subscribe to. This system watches all of it and filters for what matches verified expertise.
Opportunities arrive from federal grant databases, organizational RFP feeds, partner collaboration requests, and manual TTO entries. Each one is tagged with domain requirements that map directly to the expertise profiles from Step 1. The system doesn't keyword-match — it understands depth. An RFP requiring "power electronics" favors an expert with 8 verified proofs over someone who mentioned it in a bio.
The system has read every opportunity and every expertise profile. Now it computes best fits.
The matching engine scores every expert against every opportunity — weighing domain overlap, proof depth, verification tier, peer endorsements, and recency. It doesn't just say who fits — it tells you why, backed by specific evidence.
Every invitation explains exactly why this opportunity matches this expert — citing specific proofs, Virgil verification levels, and peer endorsements. No generic "Dear Researcher" emails. Every word is backed by evidence.
When an expert accepts, the system generates a provenance-hashed receipt — a tamper-proof record of the match, the evidence that drove it, and the response. This receipt becomes part of the expert's reputation trail.
Everything above happens once. Auto-Pilot makes it happen continuously — an agent that watches for opportunities, evaluates fit, and takes action on your behalf, all within boundaries you define.
When the agent finds a match, it attaches a conviction level — how confident the system is that this opportunity is worth your time, based on expertise depth, alignment with your proven work, and how closely it fits your stated goals.
You set your Growth Profile once on your Campo identity — like choosing conservative vs. aggressive in a retirement account. Then every service in the platform reads it: Virgil adjusts exposure tier recommendations (reputation seekers get more visibility; return seekers get tighter IP protection). Field.Fun calibrates conviction suggestions based on strategic alignment. Scout prioritizes markets where participation compounds your chosen outcome. And this matching engine weights opportunities accordingly. One declaration, continuous compounding.
A verified proof enters the system. The agent reads your Growth Profile and routes it toward the outcome you chose. Same evidence, four materially different results.
One verified proof. Four experts with different Growth Profiles. The same evidence produced a tenure credential, a $45K grant match, two new domain connections, and three high-reputation collaborators. The agent made different decisions because each expert told it what to optimize for. This runs continuously, on every new signal, without human intervention.
Every match the agent finds. Every invitation you accept. Every proof that gets verified. Every peer endorsement received. Every conviction signal placed. These build a quantitative reputation substrate that belongs to you, not to any platform.
Credibility is everything in academia — but slow. Peer review takes months. Citation counts take years. Auto-Pilot accelerates this. Every verified proof, every match, every endorsement compounds. When you move institutions, your reputation graph moves with you — verified and portable. Tenure committees see a quantitative map of expertise with cryptographic evidence behind every claim.
LinkedIn shows job titles. Endorsements are social currency — your cousin endorsed you for “machine learning.” This is different. Built from verified work product: proofs you created, expertise the system mapped, matches you were selected for based on demonstrated capability. The quantitative layer of professional contribution that LinkedIn and SuperMe lack. When you change companies, your contribution history doesn’t reset to zero.
On Upwork, Fiverr, and expert networks, your credibility is rented. Leave the platform, and your reviews stay behind — they own the signal, not you. Auto-Pilot builds a credibility wallet. Every verified engagement, every expertise match, every conviction signal becomes a portable credential. No platform can cut you off from reputation you built.
The agent builds a provenance-backed record of your expertise that compounds with every interaction. This is reputation infrastructure — verified, portable, and permanently yours.