A gig platform built around your block, not your city.
RVA Tech Help is the first provider on a platform I’m building from scratch — a small, AI-assisted booking app that connects neighbors with skilled providers who live close enough to actually be neighbors.
The big national platforms optimize for matching speed at any cost — even if it means dispatching someone from across town. This app optimizes for something different: the provider who lives ten blocks away, the gig that takes ten minutes of travel instead of an hour, and a transparent, paper-signed agreement at the door.
Three Ideas Underneath It
Most of the design choices below fall out of these three.
Hyper-local by default
Providers serve their own neighborhood first. The base fee is calculated from how easy it is to actually reach you — a bike ride down your street costs less than a ten-minute drive. Less travel, lower fees, smaller carbon footprint, more of the day available for actual work.
AI handles the awkward parts
An AI-led interview onboards each provider, helps them describe their services accurately, and turns each service into a tailored intake workflow for clients — collecting photos, measurements, and quirks of the job so we can give an honest price before anyone shows up. No more phone tag, no more “well, it turns out…” once the truck is in the driveway.
Connection only, no cut
The platform doesn’t take a percentage of the gig. We make the introduction, run the intake, and hand off a clean printable agreement. After that, the money goes directly from client to provider, the way it would if you’d hired your neighbor over the fence.
What we’re doing differently
None of these are dunks on the existing platforms. They’ve solved real problems. We’re just optimizing for different ones.
Typical gig app
- Match the closest available worker, even from across town.
- Take 20–30% of every transaction, forever.
- Star ratings from day one; one bad week can sink a provider.
- One-size-fits-all intake form for hundreds of service types.
- Generalists rewarded; specialists are buried.
- The platform owns the relationship.
This app
- Match the closest provider who actually fits the job, preferring on-foot/on-bike range.
- Free to use per gig; the platform sustains itself via fixed subscription fees (and possibly donations, from people who believe in the cause).
- Reviews only — and only later. No premature star ratings.
- AI-generated intake flow tailored to each service the provider offers.
- Hyper-specialization is the point: pick your niche, get found for it.
- The neighbor relationship is the product.
How it works for a homeowner
Four short steps, most of them happening before anyone shows up at your door.
- 1
Tell us what’s going on
Pick a service, answer a few service-specific questions, and upload a couple of photos. (“Here’s the wall I want the TV on, and here’s the closest outlet.”) Each service has its own short intake flow ("is it an outside wall", "is it dry wall/brick wall/cement/unknown" etc) — you’re not filling out a generic form.
- 2
See the honest price before you commit
A base fee based on how easily a nearby provider can actually get to you, plus an hourly rate matched to the kind of work. Both numbers show before you book. No surprise upcharge once they arrive.
- 3
Agree on the scope in writing
Once a provider accepts, the system generates a printable summary of what was agreed: the task, the price, the caveats, the photos. Both sides print and sign on arrival. Nothing fancy — just a clear record of what each side expects.
- 4
Pay your provider directly
No platform cut, no in-app payment dance. Cash, Venmo, Zelle, check — whatever you both prefer. The app helped you find each other; the rest is between you.
How it works for a provider
The AI does most of the setup with you. You answer questions in plain English; it produces something a client can actually book against.
Onboarding interview
The AI walks you through describing all the things you can do for interested clients... as many gig types as you like ("I install TVs on walls, do mobile car detailing, and offer dogwalking services"). Then, for each you describe in details what kind of gigs you can accept ("I hang TVs and install shelves, but only on drywall"; "I only walk dogs within this geographic area", "I can babysit, but only for kids between ages of four and nine"). The output is a clean profile that actually describes your work — not a checkbox list of generic service categories.
Guardrails on what you can offer
Some work needs a license, an insurance rider, or a certification. The system asks — and if the answer isn’t there, those services don’t go live. You list what you can actually back up. Clients see real skill, not aspirational claims.
Custom intake flows per service
For each service you offer, the AI generates a tailored intake interview the client fills out before booking. It confirms that the task at hand falls within the parameters you specified. You see all the details — photos, measurements, special conditions, a picture of the dog you're supposed to walk — via chat before you say yes or disclose your contact info. Hidden surprises are dramatically reduced, and there is no administrative effort playing phone tag with potential clients who are just shopping around or have unrealistic expectations.
Distance-aware base fees, your rules
Set your base fee for the neighborhood. Add upcharges for the routes that actually cost you time or stress — a block where parking a truck is really inconvenient due to the narrow streets, a toll road, a stretch of road with no bike lane, etc. The pricing reflects the real friction.
Printable agreement on arrival
When the gig is accepted, the app produces a one-page summary — service description, agreed price, caveats, photos, intake answers, base fee for coming out (which the client will have already paid before your arrival). Print it, hand a copy to the client, both sign. You have a clean paper record if anything goes sideways. If the client did not represent the gig accurately, keep the base fee and leave. If the job is more complex than anticipated due to factors that neither of you could've foreseen... re-negotiate, or have them contact a professional who has the right tool and know-how instead.
Hyper-specialization is the point
The AI-driven setup means a provider can carve out a very narrow niche and still get a properly built intake flow. Specialists who’d be invisible on a generalist platform are the easiest to find here, and once the platform has a large number of providers (hopefully in the future!), homeowners can submit highly specific searches and get matched with providers who are a great match for what they are looking for.
The kinds of providers we want
Real people doing real specialized work in their own neighborhood. A few hypothetical examples to make it concrete:
Pet-sitter, reptiles & cats only
Knows how to handle a bearded dragon’s heat lamp and a finicky senior cat. Doesn’t do dogs. Lists a three-block radius.
Carpenter, built-in shelves
Doesn’t build decks, doesn’t hang doors. Builds beautiful built-ins in alcoves and closets. Intake asks for wall photos and tape measurements.
Cleaner, hoarding & move-outs
The specialist for the jobs other cleaners turn down. Insurance and protocols documented. Walk-through photos required during intake so there are no surprises.
Old-house plaster repair
Works only on pre-1940 plaster walls. Refuses drywall jobs. Has the right tools, the right patience, and a tight service radius around the historic districts.
Bike-mounted handyperson
Small toolkit on a cargo bike. Anything that fits in the panniers, anywhere a bike can reach. Cheaper, faster, lower carbon for small jobs.
AI workflow setup for small business
Sets up the boring automations: receipt OCR, inbox triage, recurring report generation. Onsite or remote. Limits scope to what an owner can actually maintain.
These are illustrative examples, not real listings yet. They show the kind of specificity the platform is designed to support. Providers can list as many gig types as they want... so they can list plaster repair *and* drywall if they do both.
Quality & trust
Without star ratings, how do we keep the bad actors out?
Skill claims are gated up front
The onboarding AI asks pointed questions. License needed? Insurance for that kind of work? Years of hands-on experience? If the answers aren’t there, the relevant services don’t go live. We’d rather a provider list fewer things than over-promise.
Reviews, not ratings
Stars are too easy to game and too cruel to a provider having a rough month. Once the platform has enough volume for it to mean something, we’ll add proper written reviews. Until then, the absence of a star next to someone’s name isn’t a verdict.
Complaints get acted on
A no-show who pocketed the base fee. A provider who showed up unprepared for the job they accepted. A safety issue. Valid complaints lead to the provider being removed — not warned, scored, or buried under a 4.2 average. The platform stays small enough that humans can actually look at every report.
The printable agreement is the receipt
Both sides sign a one-page summary on arrival. If something goes sideways later, there’s a clear, dated record of what was agreed. We recommend documenting if there are any circumstances where the job can't be completed - or the successfully finished work - just in case there are complaints later.
What this app deliberately does not do
Knowing what you’re not building is part of building something good.
- No live booking calendar. Finding a slot is a conversation between two humans, not a Tetris game with someone’s week. Time gets nailed down in chat after the intake.
- No automatic dispatch. Clients pick a provider; providers pick gigs. No algorithm quietly shipping the cheapest match.
- No in-app payments. The money path is between the two of you. The platform doesn’t hold escrow, doesn’t collect a fee. Use the provider of your choice (PayPal, Zelle, Stripe...) even for the travel/base fee.
- No premature star ratings. Reviews when there’s enough signal for them to be fair. Until then, we’ll keep the platform small and moderate complaints by hand.
Where this stands today
As of May 2026, the app is under active development. The intake-workflow engine (the part that turns an AI-led onboarding into a printable agreement) is real and running in our own dev environment. The first beta release — with one provider (me) and a small group of clients — is planned for June 2026.
Patreon members get access first. Once the model has been road-tested with neighbors in Forest Hill, we’ll open it up to more providers, then to a wider client audience.
In the meantime, the work happens in public. The coding livestream is where you can watch this thing get built, ask questions, or call out something that doesn’t make sense.