AI made it. A human expert finishes it.
The work that happens after AI.
Bring what AI generated. A vetted human expert takes it from "almost right" to finished: refined, fixed, tested, approved and shipped. You pay when you approve. Every change and decision stays on one record.
Get it finished by someone who does this for a living.
Post the output and what "done" means. Get matched with an expert in that craft. Approve the result, then pay.
How requesting works I finish work for a livingEarn by doing the part AI cannot.
Designers, developers, editors, UX specialists, producers. Take on scoped work, get paid on approval, build a record that proves your judgement.
How experts joinThe gap
AI gets you 70% of the way. The last 30% needs a person who knows the craft.
A first output is not a finished outcome. Every kind of AI-generated work has the same shape: a fast start, then a list of skilled human steps that most people cannot, or should not have to, do themselves.
How it works
Post. Match. Work. Approve. Ship.
One flow for every kind of after-AI work. The requester owns the outcome; the expert owns the craft; the platform holds the payment and the record.
Post
Upload or link what AI produced, write what finished means, set a budget. Payment is held, not paid.
Match
Vetted experts in that craft see the job. You pick one from a short list, or accept our suggestion.
Work
The expert refines, develops, edits, tests. Every change sits next to the AI source it acts on.
Approve
You review against the definition of done. Request changes, or approve. Approval releases payment.
Ship
Final files delivered with the full record: AI source, human changes, decisions, disclosure.
What gets done
Wherever AI gives you a start, there is an expert for the finish.
Not limited to text. Not limited to code. Each kind of work has its own experts and its own definition of done.
AI-generated design
Brand designers turn a generated logo or concept into a usable identity system.
AI-generated apps
Developers take AI-assisted code from prototype to tested, integrated, deployed product.
UI and UX
Product designers make generated screens work for real users, devices and edge cases.
AI-generated content
Editors verify, restructure and finish AI drafts so they can carry your name.
Humanize AI text
Writers shape tone, voice and context so the words fit the person publishing them.
AI music and songs
Producers and songwriters arrange, rewrite, record, mix and prepare the release.
The experts
Vetted people, not a crowd.
Every expert is reviewed by a human in their own craft before they take a job: portfolio, a paid trial piece of real after-AI work, and identity. Their record on the platform is public: jobs finished, approval rate, revision rate, and the actual before-and-after on work they choose to show.
- Craft-specific. A brand designer finishes logos; a backend developer finishes APIs. No generalists assigned to everything.
- Comfortable with AI output. Experts here start from generated work by choice, and know its failure modes.
- Accountable by name. Approval, delivery and disclosure carry the expert's name, not "the AI".
Rhea K.
Marcus O.
Anita N.
Illustrative profiles. Real expert records are visible inside the app.
Why it works
AI makes the first version. People make it finished.
Generation is cheap now. Judgement, craft and accountability are not. After AI Work puts a fair price on exactly that part, protects both sides while it happens, and keeps a record of how AI and a person produced the result together.
Why this exists
"Looks correct but isn't" is the most common result of AI. Someone has to close the gap.
The people accountable for an outcome still have to finish it. Most do not have the time, or the craft, to finish every kind of work AI now starts for them. The 2026 numbers say the gap is not closing on its own.
of professional developers do not fully trust that AI-generated code is functionally correct. Only 48% always verify it before committing.
Sonar, 2026 State of Code Developer Survey, 1,149 developers. Sourceagree AI often produces code that "looks correct but isn't reliable". 38% say reviewing AI code takes more effort than reviewing a colleague's.
Sonar, 2026 State of Code Developer Survey. Sourceof AI code-generation tasks produce a known security vulnerability. The pass rate has sat at 56% for two years while AI's share of committed code doubled.
Veracode, 2026 GenAI Code Security Report, 100+ models. SourceProtected on both sides
Fair to the person paying. Fair to the person working.
Payment held, released on approval
Requesters fund the job when they post. Experts see it is funded before they start. Money moves when the work is approved against the definition of done.
Definition of done, agreed first
Every job has a short, observable definition of finished before work begins. It is the contract, and it is what approval is measured against.
Everything on the record
AI source, expert changes, comments, revision requests and approvals are all timestamped in one place. Disputes are settled on the record, not on memory.
Fair revisions and disputes
One revision round is included. Scope changes are new work, priced as such. If the two sides disagree, a human reviewer in that craft decides using the definition of done.
Has AI done the first part?
Post it. Get it finished by someone who knows the craft. Pay when it is right.