An AI consultancy and product studio for founders and engineering teams shipping AI, web, and mobile software, without standing up a permanent in-house team. AI integration, product MVPs, modernization, and the technical surface behind AI-search visibility.
Most engagements fall into one of four shapes: AI integration that survives production, product MVPs that don't feel like ones, modernization of systems that already work, and the AI-search infrastructure behind generative discovery. The shape sets scope and timeline. The craft underneath stays the same.
We build AI features into real products (LLM integration, RAG pipelines, agent workflows) and own the parts most teams underestimate: evals, latency budgets, retries, and what happens when the model is wrong. Demos are easy. Production is the deliverable.
End-to-end MVP development for founders, from a first call to a product in real users' hands. We keep the scope tight and the build genuinely usable, so the result feels finished rather than like an apologetic prototype.
Application modernization for systems that already work, including products built fast with AI tools and now straining under real users. Performance investigations, framework migrations, platform consolidation, and careful AI infill, only where it earns its keep. We respect the system in place and the people who built it.
The technical surface that AI engines actually read: the engineering side of generative engine optimization. We build MCP servers and the apps that plug into ChatGPT and Claude Desktop, generate and maintain llms.txt, structure content for LLM retrieval, and run the behavior pipelines that show whether ChatGPT, Claude, and AI Overviews are citing you.
llms.txt, eval-driven content infrastructure) that generative engines actually read.
llms.txt generators & citation surfaceAny of these shapes sound like your project? Tell us what you're building. A couple of sentences is enough to start.
Start a projectCursor, Lovable, Bolt, Claude Code: they got you to a real product with real users. Then comes the part nobody warns you about. The bugs you can't trace, the security you can't vouch for, the codebase you're scared to touch. We take AI-built products and make them production-grade, without throwing away what already works.
llms.txtA studio without a point of view is a vendor. These are the calls we make when no one's looking, and the ones we'll defend in the kickoff.
Decks slip. Working demos compound. At a regular cadence you see real, clickable progress, not a status deck that says everything's on track. If something slips, you hear why on the call, not in a report.
The person you talk to in week one is writing the code in week six. No layered agencies, no offshoring, no handing your project down a chain.
We've shipped AI features that survived launch and ones that didn't. That experience is exactly what tells us where AI earns its place, and we bring that judgment to your first call.
We're opinionated, but not religious. If your team will own this after we're done, we use what your team uses. We pick the fights worth picking.
Or the next model. A clean handoff is the deliverable, not an afterthought. Architecture docs, runbooks, decisions logged, and an llms.txt so AI tools can read what we left. We also wire up the coding agents, so the codebase explains itself and keeps building itself after we're gone.
We scope the work up front and break the estimate down by stage, so you always know roughly where the number stands. If something would move it, you hear that before it does. We'd rather scope tight and ship than scope loose and slip.
No account managers, no offshoring, no handoffs. You work directly with the people building your product, and we take projects one at a time so the work stays that direct.
A small, remote studio, with overlap across European and US time zones, and years of production experience across consumer apps, B2B SaaS, developer tooling, and AI products.
We don't have project managers. We don't have a sales floor. We don't have a "delivery team." We have engineers who design the work, build the work, and own it through handoff. That's the studio.
We pick projects one at a time. If we don't have the capacity or the right fit, we say so on the first call, and usually know someone who does.
The most common questions we field on a first call. If yours isn't here, send it. We'd rather answer in writing than dance around it later.
llms.txt, eval-driven content infrastructure, and the behavior pipelines that track citations across ChatGPT, Claude, Perplexity, and AI Overviews. We're engineers, not content marketers. If you need the content work itself, we can point you to people in our network who do it well.A predictable rhythm from first call to handoff. Clear scope, honest estimates, and visible progress you can check at every step. If something is going to slip, you hear it on the call before it slips, not after.
We interview stakeholders, audit the existing system, and pressure-test the scope. You leave the phase with a real proposal: a number, a timeline, and a definition of "done" that we'll both sign.
We choose the stack, draft the system, and ship a working spike of the riskiest part first. The plan stops being a plan and starts being a project, with the unknowns retired before they become surprises.
We ship. Working demos on a regular cadence, a shared channel for the day-to-day, and a recorded walk-through for stakeholders who don't live in it. No status reports. The demo is the report.
We leave you with the repo, the infrastructure, runbooks, architecture docs, and a hands-on workshop with the team picking it up. The first on-call rotation should feel set up to win, not abandoned.
Ready to start? A few sentences is enough. We'll reply within one business day.
Start a projectA few sentences is enough to start. We reply in under 24 hours on weekdays, from a real person who'll be on the project. If we're not the right fit, we'll say so on the first call.
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