AI development that ships working software in 30 to 60 days, not slide decks
Custom AI development for technical founders and operators who need a working v1 in production, not a research phase. We build and run AI agents and automations end to end, and you own every line of code we write.
You have a real AI product to build and no time to hire an ML team
You know what you want. An agent that handles support, a pipeline that generates content, an automation that kills a manual workflow eating your team's week. The blocker is not the idea, it is capacity. Hiring a full ML team takes months you do not have, and most agencies hand you a strategy deck and an invoice instead of running software. You could build it yourself, and you probably can. The question is whether you want to spend the next quarter debugging retrieval and prompt regressions instead of selling and shipping the rest of your product.
How we actually build it
We start with the narrowest version that proves the system works, then harden it. First we map the workflow and pick the model per task (Claude for reasoning and tool use, cheaper models where they hold up under evals). We wire orchestration in n8n or code depending on how much control you need, add RAG when the agent has to ground answers in your data, and split work across multiple agents only when one agent stops being reliable. Before anything ships we write evals against real inputs so we can measure quality instead of guessing, and we add logging and monitoring so you see failures in production rather than hearing about them from a customer. We run this same stack on our own AI work, so you are not a test case for a process we have never used.
What you end up with
- —A production AI agent or automation running against your real data and tools, not a prototype that breaks outside the demo
- —The full codebase in your repository, documented and owned by you, with no lock-in to us
- —An eval suite that scores output quality on real inputs, so you can change a prompt or model and know if it got worse
- —Logging and monitoring wired in, so failures and cost surface in a dashboard instead of a support ticket
- —A human-in-the-loop review step wherever judgment or brand risk means a model should not have the final say
- —A short handoff doc covering architecture, model choices, and the tradeoffs we made, so your engineers can extend it without reverse-engineering our decisions
Proof of Work
Growing Beauty Brand70% Faster Image Production
A growing beauty brand needed premium product images faster and cheaper than photo shoots. We built an automated image generation pipeline in n8n that connects product data and brand guidelines to generative image models, with a human review step before any asset ships. It cut image production time by 70 percent, saved significantly against traditional shoots, and turned one-off content into a repeatable system with consistent on-brand output across channels.
Read the case studyCommon Questions
I could build this myself. Why hire an AI development agency?+
You probably could, and if you have the time and an engineer who has shipped agents before, do it. What we sell is timeline. We have already made the mistakes with evals, retrieval, model selection, and monitoring, so we compress the part that eats founders alive: getting from a working demo to something reliable in production. If your own build stalls at the reliability step, that is the exact gap we close.
Do I own the code, and is it maintainable?+
You own all of it. Everything we write lands in your repository under your control, documented, with no runtime dependency on us. We build it so your engineers can read it and extend it, and we hand over a doc explaining the architecture and the tradeoffs. If you fire us the day after delivery, the system keeps running and your team can take it from there.
Can I afford this without full-service agency rates?+
We scope to a v1 that proves the system in production, not a sprawling engagement billed by the month. You pay to ship a specific working thing in 30 to 60 days, then decide if you want us to extend it. That is deliberately cheaper than a retained team and far cheaper than the salaried ML hires most of this work would otherwise require.
Is 30 to 60 days realistic, or is that a sales number?+
It is realistic for a scoped v1: one agent or one automation, running against your data, with evals and monitoring. It is not realistic for a ten-workflow platform, and we will tell you that on the first call rather than after signing. We hit the timeline by shipping the narrowest useful version first and hardening it, not by cutting the parts that make it reliable.
How do you handle models being wrong or unreliable in production?+
Two ways. Before launch we run evals against real inputs so we can measure quality and catch regressions instead of trusting a vibe. In production we add logging and monitoring so you see failures and cost as they happen. And where a wrong answer carries real brand or financial risk, we keep a human in the loop for final sign-off rather than pretending the model is perfect. That review step is deliberate, not a gap we forgot to automate.
What do you deliberately not automate?+
Anything where a bad output costs more than the review saves. In the beauty brand image pipeline, a human approves assets before they ship, because on-brand judgment is cheaper to keep human than to fully automate and police. We will tell you which parts of your workflow are worth automating and which should stay manual, instead of selling you full autonomy you do not want.
Related Reading
Have a workflow worth automating?
Tell us the process. We will tell you what we would build.
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