6–14wks
Scope to handoff
Service · AI & web apps
AI web apps that ship to production.
Most AI projects stall at the demo. Something works in a notebook, everyone is impressed, and then it never survives contact with real users, real data, and real load. We build the other kind.
6–14 weeks
Weekly working demo
Runbooks at handoff
What we actually build — the request path
Every arrow is something that can fail. We instrument all of them.
~5/yr
Client builds we take on
180+
Projects shipped since 1999
50k+
Monthly users on our own products
The gapWhat a demo hides What we hand over
What separates a production AI app from a demo?
Mostly error handling and cost tracking, plus retrieval that points at your data instead of a generic index. A demo runs on clean input with nobody watching the spend. Production is everything a demo hides, and closing that gap is the whole job.
- Rate limits nobody has hit yet
- A hallucination that reaches a real customer
- Prompts that drift the week the model updates
- Token spend with no per-feature attribution
- The plumbing between a model and your actual data
- Typed pipelines with schema-validated output
- Retrieval over your own content, not a generic index
- Guardrails and retries on every model call
- Usage and cost tracking per feature
- Runbooks your team can operate without us
How it runs
You see working software every week.
Week 0
Scope call
We start with whether the thing is worth building at all. If we are the wrong shop for it, we would rather say so in week one than in week nine.
Weeks 1–2
A thin slice, end to end
One real path all the way through the system. Ugly, but running and instrumented, so the unknowns surface while there is still time to do something about them.
Weeks 3–11
A demo you can poke at, not a status update
Something real every week. Nothing gets saved up for a reveal at the end, so a change of direction costs a conversation instead of a change order.
Final week
Handoff with runbooks
Code, deploy steps, and what to do when it breaks at 3am. We stay available for follow-on work, but we do not build in a dependency on us.
The gap between a working demo and a dependable feature is where most AI projects quietly die.Concept211 · How we work
Built with
What do you build AI web apps on?
Laravel for the app, Next.js for the front end, OpenAI and Anthropic for the models, Tailwind for the design system. It is the same stack our own products run on, which is the only reason we recommend it: we hit the failure modes ourselves first, on our own time.
LaravelApp framework
Next.jsFront end
OpenAIModels
Claude SDKModels
TailwindDesign system
Proof · our own build
Read the case study
What proof is there that this works?
ConceptSEO, which we built and still operate. It files and implements its own work every week across every domain we manage, and we are the ones on call for it.
ConceptSEO runs weekly analyses across every domain we manage
A Laravel and OpenAI/Anthropic platform that audits, scores, and files its own recommendations, then implements most of them without a human in the loop. It is the clearest example of what a production AI build looks like when we are the ones on call for it.
7Analysis types, weekly
1,831Recommendations filed
1,642Implemented end to end
What that looks like in numbers
Take the stack claim first, because it is the easiest one to check and the one most agencies cannot survive. Two of the five products this firm runs in production are built on the whole thing, Laravel with both the OpenAI and Anthropic APIs behind it: ConceptSEO and SocialBotify. Four of the five run at least one of the four technologies named above. The list is on the homepage with the stack printed under each one, so the count takes about a minute to verify.
Then the throughput, which is the number that says whether a build survives contact with production. Over the 20 weeks to 28 July 2026, ConceptSEO filed 1,831 recommendations across eight connected domains and closed 1,683 of them, and 1,642 of those closures shipped end to end by the agent rather than by a person editing code. Counts read from the platform's own recommendation ledger, one row per recommendation with a status and a timestamp, and published in full in the twenty-week benchmark.
The page you are reading is inside that number. This site is domain 10 on the platform, connected 5 July 2026 and audited on the same weekly cycle as every client domain. As of 25 August 2026 it has taken 114 recommendations, closed 97, and every one of those 97 was implemented by the agent. We do not point the platform at client work and quietly run our own site by hand.
The caveats travel with the figures. Eight domains is a small sample and it is not a random one, since four of the eight are our own properties. There is no control group behind any of it, and the ledger counts work implemented rather than work that provably moved a metric. Those limits are stated in the write-up rather than left for you to find.
Before you ask
Questions we get every time.
The things people ask on the first call, answered before you have to ask them.
Ask us something elseWhat stack do you build on?
Laravel and Next.js on the front, wired to the OpenAI, Anthropic, and Gemini APIs on the back. This is the same stack behind our own products, so you get patterns we already run in production rather than something we are trying for the first time on your budget. When a job needs on-chain data, real-time delivery, or a native desktop surface, we reach for the tool that fits instead of forcing everything through one framework.
How long does an AI build take?
Six to fourteen weeks from first scope to handoff. The short end is a focused feature with a model in the loop. The long end is a full application with retrieval, an admin surface, and cost controls behind it. Anything we think will run past fourteen weeks we break into phases and scope the first one, because a six-month estimate on a project nobody has started yet is a guess with a number on it.
What does the build actually include?
The full path from your data to a model and back into your product: typed pipelines, retrieval over your own content, guardrails on the output, usage and cost tracking, and a UI that makes the AI feel like a feature rather than a science experiment. Nothing lands as a proof of concept you still have to productionize.
What happens after handoff?
You get the code, the runbooks, and a system your team can run without us. The runbooks are the part people skip: how it works, how to deploy it, and what to do when it breaks at 3am. We stay available for follow-on work, but we do not build in a dependency on us to keep it running.
Work with us
Start a project Ready to leave the demo stage?
If you have an AI feature that has to survive real users, real data, and real load, that is exactly the kind of build we take on. The first conversation is about whether it is worth building at all.