AI Code & Architecture Review
Drop the code or design doc in. Four rival AIs pressure-test correctness, security and scale, then hand back a severity-ranked fix list.
A verdict on the design with the riskiest decisions and concrete alternatives.
Signed off by an independent Final QA audit — with a “verify before you rely on this” list, never a black box.
Why this team
Architecture reviews go soft when everyone shares the designer’s assumptions, so the seats are chosen to collide: the Software Architect judges boundaries and coupling, the Reliability Engineer asks what breaks at 3 a.m., the Security Engineer walks the trust boundaries, and the Performance Engineer finds the wall the design hits at ten times the load. The CTO weighs build cost against the team that must maintain it, and the Pragmatist defends the simplest design that meets the real requirement — usually against everyone else.
Each seat runs on a different frontier model — GPT, Claude, Gemini, Grok — so it’s a genuine cross-model review, not one AI in costumes. How a run works →
What the debate sounds like
An illustrative excerpt — A four-engineer team is proposing to split their monolithic booking platform into nine microservices with an event bus before onboarding their biggest customer yet.
Nine services for four engineers is more than two services per person — every one adds a deployment, a contract, and a failure mode. The stated goal is isolating the booking engine from reporting load. That is one seam, not eight. Extract the single hot boundary and leave the rest of the monolith alone until a second seam actually hurts.
The event bus is where this design fails operationally: at-least-once delivery means duplicate bookings unless every consumer is idempotent, and the design doc does not mention idempotency once. A monolith’s failure modes are boring and known; distributed failure modes have to be designed, staffed and paged for. Who is on call for nine services?
The numbers do not justify the surgery: current peak is modest, and the big customer multiplies load roughly fourfold. A read replica for reporting queries plus a cache on availability lookups absorbs that with two weeks of work. The proposed architecture solves a scale problem this system does not have yet — and creates a latency problem it does not have either.
Questions
What level of detail does the council need — diagrams, code, a doc?
A component list with data flow, the key technology choices, expected scale, and team size — a design doc or an architecture sketch is ideal, but a thorough prose description works. Team size matters as much as the diagram: the same design can be right for forty engineers and wrong for four.
Can it review an existing system rather than a proposed design?
Yes — describe the current architecture and where it hurts (deploy fear, incident frequency, a wall you can see coming). The debate then weighs live constraints, since the cost of change in a running system is a first-class input rather than a footnote.
What does the verdict commit to?
One of sound / sound-with-changes / rethink, plus the one or two decisions that are hardest to reverse and a severity-ranked change list. Reversibility drives the priority: a wrong library choice ages fine, a wrong data-ownership boundary compounds forever.
Your material is used only to run your review — never to train public models. Encrypted in transit and at rest. Security & privacy →
Want full control — pick your own minds, set the depth? Open the full council →