✦ Investor Overview • Pre‑Seed • Marketplace Infrastructure

A Managed Marketplace for
Trusted Local Services

ProLynk helps clients find verified pros, get matched faster, request custom quotes, and book securely in one app.

📈

Vertical Wedge

High-trust service categories overlooked by horizontal platforms

Rapid Match

Demand routing that helps clients reach relevant available pros faster

📜

Custom Quotes

Flexible quote workflow for complex, event-based, and scope-based jobs

💳

Transaction Layer

Stripe Connect, booking fees, and future pro growth products

Managed Marketplace Rapid Match Custom Quotes Stripe Connect Community First Verified Supply Trust Infrastructure Cultural Services Business Model Transaction Layer Managed Marketplace Rapid Match Custom Quotes Stripe Connect Community First Verified Supply Trust Infrastructure Cultural Services Business Model Transaction Layer

Why ProLynk exists

Horizontal platforms treat every service the same. ProLynk turns fragmented, relationship-based local services into a repeatable booking workflow.

🎯

Focused vertical wedge

Cultural services, lessons, home chefs, event help, and other high-trust categories require context, scheduling, and confidence that generic listing sites rarely provide.

🔒

High-intent workflows

Rapid Match captures urgent demand, while Custom Quote supports higher-value requests where scope, timing, and expectations need to be clarified before booking.

💰

Trust and transaction layer

Approved profiles, verification options, reviews, in-app chat, scheduling, and Stripe payments create a managed marketplace instead of a loose directory.


What makes it hard to copy

Three layers of moat that deepen with scale.

01

Marketplace network effects

Every new pro adds supply; every booking, quote, review, and match adds demand signal. The platform becomes more useful as it grows, making it harder for a new entrant to start from zero.

02

Workflow ownership

ProLynk owns the path from request to quote, chat, schedule, payment, and review. For pros, daily schedule views, open slots, auto-blocking, unavailable dates, and booking management make the platform part of daily operations.

03

Trust differentiation

Admin-approved profiles, verification, review history, in-app communication, and secure payments create a trust layer that cannot be replicated overnight. Clients stay because they trust the supply.


What’s already built

A production-ready workflow spanning client demand, pro supply, and marketplace operations.

Consumer Layer

Client experience

  • Rapid Match for faster connection to relevant pros
  • Custom Quote requests for detailed and flexible jobs
  • Browse & discover by service and availability
  • In‑app chat before confirming a booking
  • Secure card payments via Stripe
  • Booking statuses: Requested → Confirmed → In Service → Completed
  • Reviews & ratings after service
  • Push notifications for updates
  • In‑app support chat
Provider Layer

Pro tools

  • Receive better-fit leads from Rapid Match requests
  • Send custom quotes based on scope, time, and expectations
  • Stripe Connect onboarding for payouts
  • Calendar with 6 availability slot types
  • Daily schedule view with open slots and booking availability
  • Auto‑block on booking confirmation
  • Block unavailable dates, hide from search
  • Manage requests, negotiate via chat
  • Exclusive & Shared booking modes
  • Profile completion & verification steps
Platform Layer

Admin & operations

  • Admin portal: approve/reject providers
  • Service category & subcategory management
  • Marketplace controls for requests, quotes, bookings, and support
  • FAQ, Terms, and Configuration controls
  • Stripe webhook automation
  • Push notification infrastructure
  • Cross-platform mobile app, .NET API, SQL Server data layer

Why now & why this matters

Why now

Diaspora and culturally specific communities are growing across suburban and metro areas, yet many cultural and personal services are still coordinated through phone calls, referrals, and informal messaging. Consumers now expect discovery, chat, booking, and payment in a single mobile experience, while infrastructure such as Stripe Connect and cross-platform mobile frameworks makes it practical to build a production-quality marketplace earlier and more efficiently.

Why this matters

ProLynk doesn’t just aggregate listings—it owns the workflow. Rapid Match, Custom Quote, scheduling, communication, payment, and trust all happen inside one platform. That creates higher conversion potential, lower leakage, and a data advantage that compounds over time. By starting with a focused vertical wedge, ProLynk can build community loyalty before expanding into adjacent service categories.


Monetization built around completed work

The model is intentionally transaction-led first, with expansion paths that grow as supply, demand, and repeat usage deepen.

💳

Booking transactions

Core monetization comes from completed bookings, including card-based transactions through Stripe Connect and clear platform fees where cash payment is part of the workflow.

📜

Custom quote upside

Custom Quote supports larger and more complex jobs where pricing depends on scope, timing, and service expectations, creating room for higher-value marketplace activity.

🚀

Pro growth products

Future levers include featured placement, subscription tiers for power pros, service-area boosts, premium lead tools, and category-specific growth products.


Unit economics for a single pilot county

Base‑case projection for Collin County, Texas alone — modeled from ProLynk’s planned transaction fee structure. This is the pilot economic unit, with Conservative and Upside scenarios shown as sensitivity ranges below.

Base‑case model shown below. Actual results will depend on launch execution, pro supply density, repeat usage, and community distribution.

Pilot coverage area

Frisco · Plano · McKinney · Prosper · Celina (Collin County, TX). Combined population near 1.0M, median household income above $100K, and one of the densest South Asian / Indian‑American diaspora concentrations in the United States.

  • Estimated target serviceable households: ~35,000, based on the culturally aware household segment within the Collin County launch area. This number should be validated through community outreach, waitlist signups, pro onboarding, and early booking data.
  • High concentration of demand for priests, classical music & dance tutors, home chefs, language coaches, and event helpers
  • Strong word‑of‑mouth distribution through temples, schools, and community groups

How ProLynk monetizes

ProLynk monetizes through transaction‑based platform fees across online card bookings, direct/cash bookings, and limited cancellation revenue.

Current planned fee structure:

Fee Rate & Cap
Online card bookings 8% platform fee, capped at $300. Stripe processing (3% + $0.30) passes through to pro.
Direct/cash bookings 5% booking fee, with $3 minimum and $200 maximum limits.
Cancellation revenue Limited upside, not core revenue. Pro picks 0% / 5% / 8% / 10% of booking, capped at $300. ProLynk takes 30%, pro takes 70%.
No lead fees $0. Pros do not pay unless real booking activity happens.

Modeling assumptions (explicit)

Assumptions are intentionally explicit so investors can stress‑test each input.

Target serviceable households~35,000 in the Collin County pilot, subject to validation through outreach, onboarding, and early usage data
Active households~700 Y1 → ~2,500 Y2 → ~5,250 Y3 in the Base Case
Bookings per active household0.5–1.0 per month depending on category mix. Recurring categories such as tutoring, music, dance, language coaching, home services, and lessons can drive the higher end of the range.
Booking mix & average value55% small ($80) · 30% medium ($350) · 12% large ($1,200) · 3% premium ($4,500). Blended avg: $428.
Payment mode mix70% card · 30% cash
Cancellation behavior5% of bookings cancel; pros average a 5% selected cancellation fee
Blended take rate~6.8% of GMV after fee caps and card/cash mix

Modeled Base Case — Collin County Pilot

Year 1 launch target assumes successful community partnerships, pro‑led distribution, and early repeat usage from core categories.

Year 1 · Launch

Supply build & first cohort

Target launch case: onboard verified pros, seed demand through community partnerships, and build early repeat usage from core categories.

  • Active HH~700
  • Bookings / HH / month0.5
  • Annual bookings~4,200
  • GMV~$1.8M
  • Platform revenue~$122K
  • Annualized revenue run‑rate~$200K–$250K
Year 2 · Penetration

Community word‑of‑mouth compounds

Modeled growth assumes stronger pro density, deeper community distribution, and more repeat usage in recurring categories.

  • Active HH~2,500
  • Bookings / HH / month0.6
  • Annual bookings~18,000
  • GMV~$7.7M
  • Platform revenue~$524K
  • Annualized revenue run‑rate~$750K–$850K
Year 3 · Mature in geo

Reference market for next metro

Base case assumes gradual community adoption and stronger recurring categories after the pilot wedge is validated.

  • Active HH~5,250
  • Bookings / HH / month0.75
  • Annual bookings~47,250
  • GMV~$20.2M
  • Platform revenue~$1.38M
  • Annualized revenue run‑rate~$1.6M–$1.8M

Scenario Sensitivity — Year 3

Conservative case assumes lower early penetration and lower repeat usage. Base case assumes gradual community adoption and stronger recurring categories. Upside case assumes ProLynk becomes a trusted booking layer across multiple recurring and event‑based categories.

Conservative
  • Active HH~3,500
  • Bookings / HH / month0.5
  • Annual bookings~21,000
  • GMV~$9.0M
  • Platform revenue~$611K
  • Annualized revenue run‑rate~$800K–$900K
Base
  • Active HH~5,250
  • Bookings / HH / month0.75
  • Annual bookings~47,250
  • GMV~$20.2M
  • Platform revenue~$1.38M
  • Annualized revenue run‑rate~$1.6M–$1.8M
Upside
  • Active HH~7,000
  • Bookings / HH / month1.0
  • Annual bookings~84,000
  • GMV~$36.0M
  • Platform revenue~$2.44M
  • Annualized revenue run‑rate~$2.8M–$3.0M

Why this model is credible

  • Focused launch geography instead of nationwide assumptions
  • Clear transaction‑based monetization
  • Explicit household, usage, GMV, and take‑rate assumptions
  • Conservative/Base/Upside ranges instead of a single aggressive forecast
  • Upside driven by repeat categories and expansion into comparable metros

Collin County is the pilot wedge

If ProLynk validates supply density, trust, and repeat usage here, the model can be expanded into similar high‑density metros such as Houston, Austin, Bay Area, New Jersey, Chicago, Atlanta, DC, and Seattle. Comparable metros may support similar economics if ProLynk can replicate supply density, trust, and community distribution.

Comparable diaspora‑dense expansion metros

Greater Houston (Sugar Land, Katy, Pearland) · North Austin (Cedar Park, Round Rock) · SF Bay Area (Fremont, Cupertino, Sunnyvale) · New Jersey / NYC metro (Edison, Iselin, Jersey City) · Chicago (Naperville, Schaumburg) · Atlanta (Cumming, Alpharetta) · DC metro (Loudoun, Fairfax) · Seattle (Bellevue, Redmond)

1 metro mature
~$1.6M–$1.8M
Annualized revenue run‑rate · Collin County only
3 metros mature
~$4.8M–$5.4M
Annualized revenue run‑rate · if validated
5 metros mature
~$8.0M–$9.0M
Annualized revenue run‑rate · modeled scenario

Some target metros may have larger addressable household bases than Collin County, but each new metro requires its own supply build, local trust, community partnerships, and repeat category adoption. Expansion should be sequenced after the pilot economics are validated.

Terms used in this forecast (GMV, revenue run‑rate, take rate, etc.)

GMV — Gross Merchandise Value
Total dollar value of all bookings flowing through the marketplace. Not what ProLynk keeps — what changes hands between clients and pros. Year 3 Base Case example: ~$20.2M.
Platform Revenue
The portion of GMV that ProLynk actually keeps, after the pro receives their payout. This is transaction‑based marketplace revenue. Year 3 Base Case example: ~$1.38M.
Take Rate
Platform Revenue divided by GMV, expressed as a percent. ProLynk’s blended take rate is ~6.8% — intentionally lower than TaskRabbit (~22%), Fiverr (~26%), or Airbnb (~17%) because we don’t charge pros for leads.
Annualized platform revenue run‑rate
A point‑in‑time projection: “if we kept generating platform revenue at the current rate, what would we make in a year?” Calculated from the most recent monthly revenue pace × 12. This is transaction pace, not recurring subscription revenue.
Monthly platform revenue pace
The monthly version of the annualized platform revenue run‑rate. It reflects transaction revenue pace, not recurring subscription revenue.
Platform Revenue vs. Annualized Revenue Run‑Rate
Platform revenue is what was actually earned across all 12 months of the year. Annualized revenue run‑rate is the year‑end revenue pace annualized from the latest monthly cadence, which can be higher than full‑year revenue when the marketplace is growing.
EOY — End of Year
Point‑in‑time at year‑end. “~700 active households EOY” means 700 households were active as of December 31.
Active client households
Households that completed at least one booking in the recent steady‑state period. Used here as a measure of demand‑side scale, since a single household can generate many bookings over time.
Active pros
Verified pros who are listed, approved, and have completed at least one booking recently. Supply‑side scale.
Bookings
Count of completed transactions. A single household can generate many bookings per year. In the forecast, “annual bookings” is the number completed across the entire year; annualized revenue run‑rate is calculated from the year‑end monthly cadence annualized.
Penetration
The share of target households that have used ProLynk at least once. The Base Case models ~700 active households in Y1, ~2,500 in Y2, and ~5,250 in Y3 out of an estimated ~35K target serviceable households in Collin County.

⚠️ Honest caveats

  • ProLynk is pre‑launch. Every figure here is a model output, not history.
  • The forecast depends on successfully onboarding ~500 verified pros by Y3 and on the booking‑mix assumption holding in this geography.
  • Pre‑launch model should be updated as soon as waitlist signups, pro onboarding, quote activity, completed bookings, repeat usage, and category‑level conversion data are available.
  • Numbers are top‑line revenue only — they exclude marketing spend, COGS, and operational costs.
  • The 8% card‑fee cap at $300 means revenue compression on bookings above ~$3,750; we’ve flagged this as a future optimization.
  • Multi‑metro expansion math is illustrative. Comparable metros may support similar economics if ProLynk can replicate supply density, trust, and community distribution.

✦ Pre‑Seed · Raising Now

Let’s talk

We’re raising a pre-seed round to launch ProLynk and build out the early supply side. If you back early-stage marketplace and community infrastructure, we’d love to share more.

Reach out at for the deck, a product walkthrough, or a founder conversation.