From AI Prototype to Production
We take AI-built prototypes to production on EVM chains: review the vibe-coded demo, decide what belongs onchain, harden the AI-generated Solidity, and ship with real tests, infrastructure, and monitoring. Send a demo or repo link, take a discovery call, and receive a written proposal one business day later.
Reviewed by Luis Medeiros, Field CTO at ProtofireLast reviewed: August 2026
Taking an AI prototype to production is the work of turning an MVP built with AI tools (Claude, ChatGPT, Cursor, v0, Lovable, Replit) into software that can hold other people's money on a public blockchain: deciding what actually belongs onchain, reviewing and hardening any AI-generated Solidity, replacing the parts the demo faked, and standing up the tests, infrastructure, and monitoring a live system needs. You built it with AI. We make it safe to ship. Protofire maintains Solhint, the open-source Solidity linter that 1M+ developers run over their code, including code an AI wrote, and we have shipped 250+ projects since 2016 with zero vulnerabilities across delivered projects.
The engagement starts with your demo or repository link and a discovery call, and one business day after the call you have a written proposal: what we found, what production requires phase by phase, and the first thing we would build.
From demo link to mainnet
The same sequence whether your prototype already has AI-written contracts or has no contracts yet.
Share the demo
Discovery call
Written proposal
Production-readiness review
Build and harden
Launch and operate
What we do with an AI-built prototype
Many AI-built products arrive as a working web app with a wallet button and no smart contracts at all, because the AI kept the hard parts off-chain or faked them. That is a reasonable prototype and an unanswered architecture question. We map which parts of your product need a blockchain (custody of user funds, token issuance, settlement, verifiable state) and which are better served by a conventional backend, then design the contract architecture around that boundary.
The result is a system you can defend to investors and auditors: a chain where it earns its place, a database where it does not. Where the product needs a token or a full user-facing app, the token launch and dApp development practices carry that work. Benefits: an architecture you can defend to investors · contracts scoped before a line is written · no chain where a database does the job.
When the prototype already contains AI-generated Solidity, we review it line by line, run it through Solhint and the rest of a production linting and CI pipeline, and rework the patterns AI code reliably gets wrong: missing access control, unsafe external calls, prices read from manipulable sources, unbounded loops. Then we write the test suite the code never had, testing intended behavior and known attack patterns rather than restating the implementation.
We are not an audit firm: for contracts that will hold meaningful value we bring in external auditors, and our preparation is what keeps that audit short. That trail is public: Certora formally verified our ve8020 launchpad for the Balancer ecosystem, and Pessimistic audited the DEX core we engineered for Swarm Markets.
The smart contract audit practice covers the deeper assurance work. Benefits: the failure patterns AI repeats reworked at the root · a test suite written against intent · code that arrives at its audit prepared.
Every prototype fakes something, and a good demo hides it well: hardcoded balances, a backend simulating the chain, a mocked price feed, an RPC key sitting in the client bundle. We identify each faked seam in the readiness review and replace it with the production integration: price data through Chainlink oracles, reliable reads through The Graph subgraphs, treasury and admin custody through Safe multisigs, and node infrastructure that is not a free-tier endpoint.
This is where our track record does the work: core contributor to Chainlink, a top-3 indexer on The Graph since 2019, and an official Safe Guardian with $2B+ secured across 120+ networks. Benefits: every faked seam found and named · every mock replaced with its production integration · infrastructure that survives real traffic.
Production begins at launch. We set up the deployment pipeline, QA and testing gates, monitoring, and alerting, and define who is paged when a contract does something unexpected. Teams that want the operational layer handled long-term move to managed on-chain operations, where monitoring, incident response, and routine operations run as a service.
For teams that keep operations in-house, we leave runbooks and a working alerting stack rather than a handover document. Either way the goal is the same: anomalies surface in your monitoring first, with a rehearsed response behind them. Benefits: a repeatable deployment pipeline · monitoring live before the first user arrives · a defined response before the first incident.
What breaks in an AI-built dapp
A demo that works can still be unsafe to ship. These are the failure classes that show up most often in AI-built prototypes, and none of them announce themselves while the only user is the author.
Secrets shipped with the app
AI scaffolds put RPC keys, API keys, and sometimes private keys into .env files and client bundles. A key committed to a public repository is compromised from that moment, and a key in the frontend bundle is public by definition. We move every secret server-side, rotate what was exposed, and wire admin authority to a Safe multisig instead of a single hot key.
Admin functions anyone can call
Mint, pause, withdraw, and parameter-setting functions generated without access control. The model adds onlyOwner when the prompt mentions it and omits it when the prompt does not, and the demo works either way. On mainnet, an unprotected function that moves value is found by bots within hours.
Prices read from wherever was easiest
A spot price from a single DEX pool is a price an attacker can move within one transaction. Production systems read Chainlink feeds or time-weighted prices with staleness checks, and every value-bearing calculation gets asked: what happens if this number is manipulated for one block?
The parts the demo faked
Hardcoded balances, a backend simulating chain state, mocked KYC, a contract "deployed" only on a local fork. Faked seams are invisible in a demo that works, and each one is real engineering on the path to production. The readiness review names all of them, with the cost of making each real.
Tests that assert the code does what it does
AI typically writes tests by restating the implementation, so the suite passes by construction and catches nothing. Missing are the adversarial cases: reverts, reentrancy, value-path fuzzing, invariants that must hold across every state transition. We write the suite against intended behavior and known attack patterns.
Gas, loops, and scale
Iterating over an unbounded array works with three test users and reverts permanently at five thousand, sometimes on the exact function users need to withdraw. AI code optimizes for reading well in a chat window, so gas limits, storage growth, and per-transaction cost go unexamined until they are load-tested.
No pause, no path to fix
Immutable contracts with a bug and no pause switch, or an upgradeable proxy wired by a model that has seen a thousand half-correct examples. Both extremes turn the first serious bug into an incident. We design the pause and upgrade authority deliberately, held by a multisig, before deployment. If the incident has already happened, protocol rescue is the emergency line.
Nothing watching after launch
No monitoring, no alerting, no runbook, so the first notification of an exploit is a user's screenshot. Production means something watches every contract call and someone is defined as first responder, whether that is your team with our runbooks or our managed operations service.
How an AI prototype to production engagement starts
Send the link
The discovery call
The written proposal, one business day later
The first engagement
What founders and teams bring us
Who this is for
Two groups arrive with the same artifact. Founders, usually pre-seed to seed, who built a working demo with AI in weeks and now need it to survive contact with real users, real money, and an investor's technical diligence. And teams inside companies, innovation groups and product units, whose AI-built prototype proved the case internally and now has to meet the organization's security, compliance, and operational bar.
The conversation is the same for both: what the prototype proves, what it hides, and what production requires. Scope is EVM chains and Solidity; if your prototype targets a non-EVM stack, we will say so on the call. And if your product does not need a blockchain at all, the proposal will say that too, in writing.
The engineering team behind Solhint
Protofire is an engineering agency that has shipped 250+ projects since 2016 across 60+ networks and 95+ protocols, with zero vulnerabilities across delivered projects. We maintain Solhint, the open-source Solidity linter that 1M+ developers run over their contracts, including contracts an AI wrote.
We are a core contributor to Chainlink, a top-3 indexer on The Graph since 2019 (the ranking is on-chain), and an official Safe Guardian with $2B+ secured across 120+ EVM networks. We also use AI in our own delivery: AI-augmented squads where agents draft and a named senior engineer reviews and signs off every commit. That is the standard your prototype inherits: AI speed where it helps, senior judgment on everything that ships.
“What we found, what production requires phase by phase, and the first thing we would build: in writing, one business day after the call.”
Getting from prototype to production
| Iterating with AI alone | Protofire engagement | |
|---|---|---|
| Security review | The model reviews its own output | Senior engineers, Solhint and CI pipelines, external audit when value at risk demands it |
| Architecture | Whatever the prompts accumulated | The onchain/off-chain boundary decided deliberately and documented |
| Tests | Restate the implementation, pass by construction | Written against intended behavior and known attack patterns |
| The faked parts | Persist silently into launch | Named in the readiness review, replaced with production integrations |
| After launch | You watch it yourself, without tooling | Monitoring, alerting, runbooks, and an operations option |
| The artifact | A chat history | A written proposal you can act on |