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cv2md converted traditional resumes into structured, agent-readable configuration files. We are no longer accepting uploads. This page explains what we built, what we learned, and why structured data matters.
A resume is a document written for humans. We converted it into a format built for agents.
Traditional resumes are unstructured text. A PDF of bullet points, a LinkedIn export, or a Word document. A hiring manager can read it. An ATS system can keyword-scan it. An AI agent can do neither well.
cv2md took that same career history and transformed it into a structured configuration file: a machine-readable format with explicit capability scores, inputs-to-outputs work history, function-call formatted skills, and a system prompt that tells any AI model exactly what this person does and what they optimize for.
The question was simple: does structure improve how agents evaluate a candidate?
It does. An ATS parsing a structured file with defined skill proficiency scores, quantified outputs, and a clear system directive evaluates the same candidate materially differently than a PDF of bullet points. The data is the same. The format changes what the machine can do with it.
The same career. Two different formats.
Jordan Reyes is a fictional composite representing a real career arc: a B2B marketing director with 14 years across content, demand generation, and revenue operations. The data below is consistent across both formats. What changes is how a machine can read it.
Summary
Results-driven marketing leader with 14 years of experience driving growth for B2B services companies. Proven track record of building demand generation engines, leading cross-functional teams, and translating business objectives into measurable revenue outcomes. Skilled at aligning marketing strategy with sales to accelerate pipeline and reduce cost per acquisition.
Experience
- Led a team of 7 across content, digital, and demand gen functions
- Rebuilt lead generation engine, increasing qualified pipeline by 340% in 18 months
- Launched ABM program targeting 200 named accounts, contributing $4.2M influenced revenue in year one
- Reduced cost per qualified lead from $820 to $310 through channel optimization
- Managed $2.1M annual marketing budget with full P&L accountability
- Compressed sales cycle from 87 to 54 days through weekly RevOps cadence with VP of Sales
- Grew organic search traffic 280% through SEO and content strategy, generating 1,200+ MQLs per quarter
- Managed $400K/year paid media budget across LinkedIn, Google, and programmatic channels
- Built first marketing attribution model: 62% of closed revenue influenced by organic content
- Launched product marketing function and trained 15-person sales team on new positioning
- Produced 3-4 long-form pieces per week across 8 B2B verticals
- Built editorial calendar system adopted across 12-person freelance team
- 190% average organic traffic increase across managed accounts
Skills
Education
| Metric | Value | Context |
|---|---|---|
| Pipeline Growth | 340% | 18-month demand gen rebuild |
| ABM Revenue Influenced | $4.2M | Year one, 200-account program |
| CPQL Reduction | $820 → $310 | 62% drop via channel optimization |
| Sales Cycle | 87 → 54 days | 38% compression via RevOps alignment |
| Organic MQLs | 1,200+/qtr | Post-SEO program, Vantage Point |
| Closed Revenue (Organic) | 62% | Attribution model finding |
| Annual Budget Managed | $2.1M | Full P&L accountability |
Agents do not read. They parse. Structure is the difference between visible and invisible.
When an ATS evaluates a resume, it is not understanding your career. It is scanning for pattern matches against a job description. A traditional resume hides its best data inside sentences and bullet points. A structured file surfaces the same data in a form the machine can extract directly.
The same dynamic applies everywhere an agent reads your information. A customer asking ChatGPT for a vendor recommendation. A recruiter's AI pre-screening a stack of candidates. A procurement tool evaluating supplier capabilities. In every case, the agent is doing the same thing: trying to extract structured signal from unstructured text.
Same candidate, PDF format
Same candidate, structured format
The candidate did not change. The work history is identical. The skills are the same. What changed is that the machine can now read what the human has been trying to say.
This is not a resume problem. This is an information architecture problem. Any business, professional, or organization that wants AI systems to accurately represent them, surface them in recommendations, or evaluate them fairly faces the same challenge. The format of your information determines whether agents can use it.
Google confirmed this in 2026 when it reversed an earlier position and stated that structured data, content authority signals, and schema markup do influence which businesses appear in AI-generated search results. What cv2md demonstrated for resumes applies directly to how businesses get found, evaluated, and recommended by AI systems.
Format your information for the machines that will read it, not just the humans.