research

We don't ask. We read.

Most AI website builders make you fill out a brief. We read your public footprint and write the brief ourselves.

Stage 01

Input.

Maya Cohenlinkedin.com/in/maya-cohen

One textarea. One paste. We take it from here.

Stage 02

Research.

Public profile

  • Name
  • Headline
  • Bio
  • Photo

Public posts

  • Recent activity
  • Voice and tone signals

Social signals

  • LinkedIn
  • Twitter / X
  • Instagram
  • Personal site
  • GitHub

Contact info

  • Verified emails
  • Phone
  • Booking links (when public)

GenLP's research engine scans public sources for you. We never touch login-gated content.

Stage 03

Profile reduction.

{
  "personName": "Maya Cohen",
  "headline": "Strategy consultant for early-stage SaaS",
  "bio": "Helps founders sharpen positioning and pricing…",
  "specialties": ["B2B SaaS", "GTM", "pricing"],
  "services": [
    { "name": "1:1 strategy sprint", "summary": "3-week engagement…" },
    { "name": "Workshop", "summary": "Half-day intensive…" }
  ],
  "links": ["linkedin.com/in/maya-cohen", "mayacohen.com"],
  "gallery": ["…3 public images…"],
  "brand": { "keywords": ["clear", "rigorous", "founder-aligned"], "tone": "direct, warm" }
}

Raw research becomes a structured profile — the brief you'd otherwise have to write yourself.

Stage 04

Section generation.

Hero

← headline + specialties[0]

Sharpen your positioning before your next raise.

About

← bio

Maya Cohen helps early-stage founders find the language and price points that close enterprise deals.

Services

← services[]

  • 1:1 strategy sprint — 3-week engagement.
  • Workshop — half-day intensive.

Each section is composed from the structured profile. Editable post-generation.

Stage 05

Render.

maya-cohen.genlp.ai
Strategy consultingSharpen your positioning before your next raise.Maya Cohen — strategy consultant for early-stage SaaS.

Rendered in one of 14 crafted families. The engine picks fonts, palette, motion, and section composition — each axis multiplies into tens of thousands of unique outputs. Publish to a free subdomain or your own.

What we extract.

  • Name and headline
  • Bio and writing tone
  • Services and pricing (when public)
  • Testimonials and proof
  • Social handles (LinkedIn, X, IG, GitHub, personal site)
  • Contact info (verified emails, phones, booking links)
  • Gallery imagery
  • Language detection
  • Brand keywords
  • Industry signals

What we don't access.

  • Private LinkedIn profiles behind authentication.
  • Paywalled content.
  • Login-gated sites.
  • Anything not openly crawlable.
  • Private documents you don't share.

If a link is private, we work from your name and any other public footprint we can find. Add more public links for a richer result.

Inside the engine.

GenLP is a research engine, a profile reducer, a section writer, an imagery model, and a design assembler — orchestrated together. The list of models behind each layer evolves; the discipline doesn't.

Deep researchMulti-source public-web discovery. Reads the link you gave us, follows its public outbound profiles, and harvests structured signal.
Profile reductionRaw research becomes a typed identity brief — the structured object the rest of the engine writes against.
Section generationLong-form, voice-matched section copy. Hero, about, services, projects, FAQ — composed from the profile, editable post-generation.
Imagery synthesisHero imagery generated on demand and tuned to the chosen palette. No stock photography, no clip art.
Design assemblyFourteen crafted families × twenty-two palettes × dozens of compositions × light & dark — orchestrated so the chosen output is publish-ready.

Each layer is routed through GenLP's own gateway so the engine can pick the right model for the job, swap it when a better one lands, and keep the implementation an evolving choice rather than a published list.

10 seconds of your time. 10–20 minutes of ours.

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Free to try. No credit card.

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